U.S. patent application number 13/921075 was filed with the patent office on 2013-12-26 for universal customer based information and ontology platform for business information and innovation management.
The applicant listed for this patent is Strategyn Holdings, LLC. Invention is credited to Mark Jaster, Anthony Ulwick.
Application Number | 20130346146 13/921075 |
Document ID | / |
Family ID | 42729077 |
Filed Date | 2013-12-26 |
United States Patent
Application |
20130346146 |
Kind Code |
A1 |
Jaster; Mark ; et
al. |
December 26, 2013 |
Universal Customer Based Information and Ontology Platform for
Business Information and Innovation Management
Abstract
A system may include a search engine coupled to an Outcome
Driven Innovation (ODI) repository. The system may include a
segmentation engine having segmentation tools that segments a
market using segmentation tools. A metadata engine may tag and
bundle data records algorithmically, append meta-data associated
with business information (BI) to data records, facilitate
portfolio optimization decisions, facilitate capturing
relationships between different records and record types, and
facilitate calculation, ranking, analysis, and reporting of
opportunity scores. A strategy engine may include strategic tools
for pulling in data from various sources, visualizing, interacting
with, capturing and synthesizing insights so as to facilitate
strategic planning. An extract, transform, load (ETL) engine may
extract data from outside sources, transform the data to fit
operational requirements, and load the transformed data into to ODI
data repository.
Inventors: |
Jaster; Mark; (Rosemont,
PA) ; Ulwick; Anthony; (Mill Valley, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Strategyn Holdings, LLC |
San Francisco |
CA |
US |
|
|
Family ID: |
42729077 |
Appl. No.: |
13/921075 |
Filed: |
June 18, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12563969 |
Sep 21, 2009 |
8494894 |
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13921075 |
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61209764 |
Mar 10, 2009 |
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61098690 |
Sep 19, 2008 |
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Current U.S.
Class: |
705/7.29 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 30/0201 20130101; G06F 16/367 20190101; G06Q 10/06
20130101 |
Class at
Publication: |
705/7.29 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A system comprising: a search engine, coupled to an
outcome-driven innovation (ODI) data repository in operation,
including one or more communications protocols, wherein, in
operation, the search engine searches through data records and
other text based information in a format associated with the one or
more communications protocols for data for tagging, finding new
technology solutions or intellectual property, finding or
broadening markets for products, concepts or opportunities,
identifying subject matter experts, identifying competitors, and
populating the outcome-driven innovation (ODI) data repository; a
segmentation engine, coupled to the ODI data repository in
operation, including segmentation tools to interact with
statistical analysis and modeling packages and import additional
metadata tags into a job/outcome data schema in operation, wherein,
in operation, the segmentation engine segments a market using the
segmentation tools; a metadata engine, coupled to the ODI data
repository in operation, wherein, in operation, the metadata engine
tags and bundles data records algorithmically, appends meta-data
associated with business information (BI) to data records,
facilitates portfolio optimization decisions, facilitates capturing
relationships between different records and record types, and
facilitates calculation, ranking, analysis, and reporting of
opportunity scores; a strategy engine, coupled to the ODI data
repository in operation, including strategic tools for pulling in
data from various sources, visualizing, interacting with, capturing
and synthesizing insights so as to facilitate strategic planning;
an extract, transform, load (ETL) engine, coupled to the ODI data
repository in operation, wherein, in operation, the ETL engine
extracts data from outside sources, transforms the data to fit
operational requirements, and loads the transformed data into to
ODI data repository.
2. The system of claim 1, wherein the one or more communications
protocols include a protocol selected from the group consisting of
the financial information exchange (FIX) protocol, FIX extended
markup language (FIXML), FIX adapted for streaming (FAST) protocol,
a protocol for use in market communications.
3. The system of claim 1, further comprising a process engine,
coupled to the ODI data repository, for business process management
(BPM).
4. The system of claim 1, wherein the segmentation engine further
provides data manipulation tools to facilitate compiling and
loading data sets into external statistical analysis packages, and
utilities to enhance visual representation, analysis and tabular
reporting of statistical data properties.
5. The system of claim 1, wherein the metadata engine further
facilitates interaction with data and performs other functionality
that makes data more useful in a BI context.
6. The system of claim 1, wherein the strategic tools facilitate
implementation of a strategy from the group consisting of needs
delivery enhancement strategy, needs-based intellectual property
(IP) strategy, innovation strategy, market growth strategy,
consumption chain improvement strategy.
7. The system of claim 1, further comprising an ODI transformation
rules engine for transforming input data into an ODI format and
transforming ODI data into another format for output.
8. The system of claim 1, wherein, in operation, the ETL engine
transforms the extracted data by applying rules or functions to the
extracted data to derive data for loading into a target
repository.
9. The system of claim 1, further comprising a reporting engine,
coupled to the ODI data repository, wherein in operation, the
reporting engine prepares and delivers interactive and printed
reports.
10. The system of claim 1, further comprising an ODI transaction
engine, coupled to the ODI data repository, wherein in operation,
the ODI transaction engine provides interaction between engines
capable of writing to or reading from the ODI data repository.
11. The system of claim 1, further comprising a collaboration and
business process workflow engine, coupled to the ODI data
repository and the metadata engine, wherein in operation, the
collaboration and business process engine provides a collaborative
workspace and a platform for workers to execute workflows for
taking action on insights and data bundles.
12. A method comprising: accessing market segments in an outcome
driven innovation (ODI) repository, the market segments based on an
outcome driven innovation (ODI) model of a market; identifying,
based on a search of the data records, one or more outcomes
associated with the market segments; associating, based on the
search of the data records, one or more jobs with the one or more
outcomes, the associating based on the ODI model of the market;
identifying, based on the search of the data records, one or more
solutions for the problems in the market, the one or more solutions
based on the one or more outcomes and the one or more jobs; storing
the one or more solution in the ODI repository.
13. The method of claim 12, wherein accessing the market segments
in the ODI repository comprises storing the market segments in the
ODI repository or reading the market segments from the ODI
repository.
14. The method of claim 12, wherein the data records comprise: data
associated with customers, customer profile information, customer
jobs regions, or customer outcomes region.
15. The method of claim 12, wherein the data records comprise:
price sensitivity tables.
16. The method of claim 12, wherein the data records comprise: an
ODI translation table data region, customer file translation
tables, product/service offerings translation tables, sales and
marketing campaigns translation tables, innovation concepts
translation tables, business development translation tables, or
external data translation tables.
17. The method of claim 12, wherein the one or more jobs comprise:
one or more core functional jobs, or one or more emotional
jobs.
18. The method of claim 12, wherein the one or more solutions
comprise: portfolio optimization decisions; relationships between
different records and record types; calculation, ranking, analysis,
or reporting of opportunity scores; or visualizing, interacting
with, capturing or synthesizing insights so as to facilitate
strategic planning.
19. The method of claim 12, wherein the one or more solutions
comprise: technology solutions, intellectual property solutions,
new markets for products or concepts, new opportunities,
identification of subject matter experts, or identification of
competitors.
20. A system comprising: means for identifying a problem in the
market; means for accessing market segments in an outcome driven
innovation (ODI) repository, the market segments based on an
outcome driven innovation (ODI) model of the market, and being
based on the problem; means for identifying, based on a search of
the data records, one or more outcomes associated with the market
segments; means for associating, based on the search of the data
records, one or more jobs with the one or more outcomes, the
associating based on the ODI model of the market; means for
identifying, based on the search of the data records, one or more
solutions for the problems in the market, the one or more solutions
based on the one or more outcomes and the one or more jobs; means
for providing the one or more solutions.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional patent application of U.S.
patent application Ser. No. 12/563,969, filed on Sep. 21, 2009,
which in turn claims priority to U.S. Provisional Patent
Applications 61/098,690 filed Sep. 19, 2008, and 61/209,764 filed
Mar. 10, 2009. All of U.S. patent application Ser. No. 12/563,969,
U.S. Provisional Patent Applications 61/098,690, and U.S.
Provisional Patent Applications 61/209,764 are incorporated herein
by reference.
BACKGROUND
[0002] Today's modern business enterprises require and make use of
sophisticated information systems to acquire vital insights into
the performance or prospective future performance of their business
relative to goals, market metrics, competitive information, and the
like. This class of information products is known in the field
today as Management Information Systems (MIS) or Business
Intelligence (BI) tools. In addition businesses seek better ways to
identify the right strategies and new ideas that can help them
grow, and information solutions supporting these objectives are
often referred to as Collaboration Technologies, and Innovation
Management Systems. Collectively these information systems fall
under the general category of Enterprise and Marketing Intelligence
Systems and represent a critical part of today's business software
and information systems marketplace.
[0003] While data management and reporting technologies have
advanced to become adept at efficiently retrieving information
stored in these systems and generating reports, the problem that
plagues all these systems is the lack of a unifying information
framework, or ontology, that provides a stable and fundamental
frame of reference that is absolute and consistently meaningful
across all domains for gleaning business insights or for
facilitating value creation. The lack of an ontology means that
evaluations on the information gathered are highly subjective and
dependent on interpretation, and that each information domain tends
to exist as an island where local rules prevail, rather than as a
part of an integrated whole. The problems this creates for business
are innumerable; consequently MIS and BI systems today, while
enabling better informed decisions, have failed to deliver on their
promise of transforming management decision making. For example,
these systems can easily track the sales results and underlying
demographics for a particular market, but utterly fail at providing
any empirically defensible prediction, save extrapolation of past
results, around whether these results are sustainable or what
impact a new idea will have. More generally, the lack of a valid
unifying and quantifiable frame of reference for business insight
and intelligence means that compromises are made in making
decisions and projections into future business impacts are largely
guesswork. This problem has always existed in business information
analysis and decision making, and it is a root cause of many
mistaken beliefs and failures in business information technology
initiatives.
[0004] An example of market modeling created by Anthony Ulwick and
called Outcome Driven Innovation (ODI) creates an empirically valid
estimator of market demand by holistically identifying the Jobs
that customers and key participants in the consumption chain are
trying to get done in a particular market and then collecting
quantitative data on Importance and satisfaction levels associated
with all of these jobs and with the desired outcomes associated
with a specific core job of interest. This data is then analyzed to
identify needs that are underserved (representing opportunities for
new products and services) and those that are over-served
(representing areas that are ready for being disrupted). A
proprietary index called the opportunity score is used to determine
the strength of the underlying market conditions driving these
findings, and this score has been shown to be a valid empirical
estimator of customer demand/sentiment and hence the consequential
business value of fulfilling the market needs appropriately. The
practice of researching and analyzing markets in this fashion is
what is referred to as the ODI methodology.
[0005] The ODI methodology possesses four critical attributes that
collectively make it uniquely valuable for business analysis.
First, the use of the Jobs framework facilitates the description of
an interaction a customer or key influencer may have with current
or yet-to-be designed products and services and the measurement of
these in a meaningful unit of analysis. This is important to
obtaining insights and making informed decisions on questions where
the objects of interest are parts of interconnected systems like in
virtually all business matters. Today's MIS, BI, and Innovation
Management systems lack this unifying framework and so do little to
facilitate meaningful comparisons and analyses within and across
the inherently disparate information domains of the system (e.g.
competitive information, customer market information, product
management information, R&D, etc.). Second, the measurement
system used by the methodology provides direct quantitative
measurement of the fundamental driver of business
outcomes--customer demand, and this measurement system is both
reproducible and repeatable. Third, the actual measurements taken
are internally consistent; that is they report on the same
dimensions of importance and satisfaction irrespective of whether
jobs or outcomes are being studied and whether the job of interest
is a functional job, an emotional job, or a related job. This
therefore means that the methodology enables disparate variables of
successful business endeavors, such as emotional factors,
functional factors, and performance factors, to be compared
directly to one another for prioritization without transformation.
And fourth, the numerical data collected are normalized by an
indexing method to have the same market meaning regardless of the
factor being studied and are scaled in a manner that directly
reflects the significance of the metric in market terms. This
ensures that comparisons across factors are not just qualitatively
valid but also quantitatively correct and easily extrapolated to
real business impacts. For these reasons the foundation of ODI
presents an information platform for business analysis that is
fundamentally superior to all constructs that have preceded it.
SUMMARY
[0006] Presented herein are techniques for providing a valid
unifying ontology to organize business intelligence and innovation
assessments. Using one of these techniques, an entity can, for
example, evaluate its position in absolute terms relative to
markets, and competitors. It can confidently identify new product
opportunities and assess the threat from changes affecting its
markets. It can quantify future economic value and uncertainty of
development investments and provide important information to
capital markets related to its asset value compared to others in
its sectors. If the ontology requires information to be gathered
that is different from other forms commonly collected, the business
could develop a new strategic competitive advantage through the act
of gathering the information itself that will be difficult and time
consuming for competitors to copy. Taken collectively, creating and
commercializing a unifying ontology for business as taught herein
is truly transformational to business systems.
[0007] A system constructed using one or more of the techniques
includes a collective set of data structures, uniquely designed
entities, information tools, and computational and machine methods
useful to store, append, interact with, retrieve, process, and
present data and information in a fashion that enables associations
to be made between the entities and the particular Jobs and
Outcomes that pertain to the underlying markets, or possible
markets, of an enterprise which have been identified by separate
analysis following an Outcome Driven Innovation (ODI) methodology.
Through the associations, users can attain insights and explore
innovations and new business strategies that are virtually
unworkable without the system. The use of the system comprises both
pre-formed "canned" reports, and interactive "ad-hoc" queries for
original analysis and to facilitate guided collaboration in a
computer assisted fashion.
[0008] Processes/decisions that can potentially be improved using a
technique described in the detailed description can include, for
example, Primary Market Research, Use of Secondary Market Research,
Product Management and Marketing Strategy, Marketing
Communications, R&D, New Product Development, General Business
Strategy, Innovation Strategy, Innovation Collaboration, Ideation,
Business Case Analysis, IP Strategy, and Mergers & Acquisition
Strategy and Due Diligence. Business insights that can potentially
be improved using a technique described in the detailed description
can include, for example, Competitive Intelligence and Industry
Benchmarking, Unmet Market Demand, Modeling of underlying market
trends, Cause and Effect of Marketing Communications Results, New
Technology Assessments and Scouting, and New Product/Platform or
other Growth Investment Risk/Return. These improvements are
intended to be examples, not limitations, and some of them may not
be achieved in certain implementations of the techniques.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 depicts an example of a system including a universal
strategy and innovation management system (USIMS) server.
[0010] FIG. 2 depicts an example of a USIMS system.
[0011] FIG. 3 depicts a flowchart of an example of a method for
external data integration.
[0012] FIG. 4 depicts a flowchart of an example of a competitive
assessment method.
[0013] FIG. 5 depicts a flowchart of an example of a needs delivery
of current products method.
[0014] FIG. 6 depicts a flowchart of an example of a needs delivery
enhancement strategy method.
[0015] FIG. 7 depicts a flowchart of an example of a needs based IP
strategy method.
[0016] FIG. 8 depicts a flowchart of an example of a consumption
chain needs delivery method.
[0017] FIG. 9 depicts a flowchart of an example of a method for
computationally enabling and enhancing an ODI process.
[0018] FIG. 10 depicts a flowchart of an example of a method for
creating an innovation strategy.
[0019] FIGS. 11-15 depict flowcharts of examples of market growth
strategy methods.
[0020] FIG. 16 depicts a flowchart of an example of a method for
facilitating the creation of an overall growth blueprint.
[0021] FIG. 17 depicts a flowchart of an example of a method for
facilitating the development of a consumption chain improvement
strategy.
[0022] FIG. 18 depicts a flowchart of an example of a method for
facilitating qualitative research.
[0023] FIG. 19 depicts a flowchart of an example of a method for
facilitating quantitative research.
[0024] FIG. 20 depicts a flowchart of an example of a method for
identifying opportunities.
[0025] FIG. 21 depicts a flowchart of an example of a method for
segmenting the market.
[0026] FIG. 22 depicts a flowchart of an example of a method for
defining the targeting strategy.
[0027] FIG. 23 depicts a flowchart of an example of a method for
conceptualizing breakthroughs.
[0028] FIG. 24 depicts a conceptual diagram of an example of a data
structure having data entities.
[0029] FIG. 25 depicts an example of a system.
DETAILED DESCRIPTION
[0030] FIG. 1 depicts an example of a system 100 including a
universal strategy and innovation management system (USIMS) server.
In the example of FIG. 1, the system 100 includes a network 102, a
USIMS server 104, clients 106-1 to 106-N (referred to collectively
as the clients 106), an Outcome Driven Innovation (ODI) data
repository 108, and optional components including: a mail server
110, a mail data repository 112, a document management applications
(DMA) server 114, and a document data repository 116.
[0031] In the example of FIG. 1, the network 102 can include a
networked system that includes several computer systems coupled
together, such as the Internet. The term "Internet" as used herein
refers to a network of networks that uses certain protocols, such
as the TCP/IP protocol, and possibly other protocols such as the
hypertext transfer protocol (HTTP) for hypertext markup language
(HTML) documents that make up the World Wide Web (the web). Content
is often provided by content servers, which are referred to as
being "on" the Internet. A web server, which is one type of content
server, is typically at least one computer system which operates as
a server computer system and is configured to operate with the
protocols of the World Wide Web and is coupled to the Internet. The
physical connections of the Internet and the protocols and
communication procedures of the Internet and the web are well known
to those of skill in the relevant art. For illustrative purposes,
it is assumed the network 102 broadly includes, as understood from
relevant context, anything from a minimalist coupling of the
components, or a subset of the components, illustrated in the
example of FIG. 1, to every component of the Internet and networks
coupled to the Internet.
[0032] A computer system, as used in this paper, is intended to be
construed broadly. In general, a computer system will include a
processor, memory, non-volatile storage, and an interface. A
typical computer system will usually include at least a processor,
memory, and a device (e.g., a bus) coupling the memory to the
processor.
[0033] The processor can be, for example, a general-purpose central
processing unit (CPU), such as a microprocessor, or a
special-purpose processor, such as a microcontroller.
[0034] The memory can include, by way of example but not
limitation, random access memory (RAM), such as dynamic RAM (DRAM)
and static RAM (SRAM). The memory can be local, remote, or
distributed. As used in this paper, the term "computer-readable
storage medium" is intended to include only physical media, such as
memory. As used in this paper, a computer-readable medium is
intended to include all mediums that are statutory (e.g., in the
United States, under 35 U.S.C. 101), and to specifically exclude
all mediums that are non-statutory in nature to the extent that the
exclusion is necessary for a claim that includes the
computer-readable medium to be valid. Known statutory
computer-readable mediums include hardware (e.g., registers, random
access memory (RAM), non-volatile (NV) storage, to name a few), but
may or may not be limited to hardware.
[0035] The bus can also couple the processor to the non-volatile
storage. The nonvolatile storage is often a magnetic floppy or hard
disk, a magnetic-optical disk, an optical disk, a read-only memory
(ROM), such as a CD-ROM, EPROM, or EEPROM, a magnetic or optical
card, or another form of storage for large amounts of data. Some of
this data is often written, by a direct memory access process, into
memory during execution of software on the computer system. The
non-volatile storage can be local, remote, or distributed. The
non-volatile storage is optional because systems can be created
with all applicable data available in memory.
[0036] Software is typically stored in the non-volatile storage.
Indeed, for large programs, it may not even be possible to store
the entire program in the memory. Nevertheless, it should be
understood that for software to run, if necessary, it is moved to a
computer-readable location appropriate for processing, and for
illustrative purposes, that location is referred to as the memory
in this paper. Even when software is moved to the memory for
execution, the processor will typically make use of hardware
registers to store values associated with the software, and local
cache that, ideally, serves to speed up execution. As used herein,
a software program is assumed to be stored at any known or
convenient location (from non-volatile storage to hardware
registers) when the software program is referred to as "implemented
in a computer-readable storage medium." A processor is considered
to be "configured to execute a program" when at least one value
associated with the program is stored in a register readable by the
processor.
[0037] The bus can also couple the processor to the interface. The
interface can include one or more of a modem or network interface.
It will be appreciated that a modem or network interface can be
considered to be part of the computer system. The interface can
include an analog modem, isdn modem, cable modem, token ring
interface, satellite transmission interface (e.g. "direct PC"), or
other interfaces for coupling a computer system to other computer
systems. The interface can include one or more input and/or output
(I/O) devices. The I/O devices can include, by way of example but
not limitation, a keyboard, a mouse or other pointing device, disk
drives, printers, a scanner, and other I/O devices, including a
display device. The display device can include, by way of example
but not limitation, a cathode ray tube (CRT), liquid crystal
display (LCD), or some other applicable known or convenient display
device.
[0038] In one example of operation, the computer system can be
controlled by operating system software that includes a file
management system, such as a disk operating system. One example of
operating system software with associated file management system
software is the family of operating systems known as Windows.RTM.
from Microsoft Corporation of Redmond, Wash., and their associated
file management systems. Another example of operating system
software with its associated file management system software is the
Linux operating system and its associated file management system.
The file management system is typically stored in the non-volatile
storage and causes the processor to execute the various acts
required by the operating system to input and output data and to
store data in the memory, including storing files on the
non-volatile storage.
[0039] Some portions of the detailed description may be presented
in terms of algorithms and symbolic representations of operations
on data bits within a computer memory. These algorithmic
descriptions and representations are the means used by those
skilled in the data processing arts to most effectively convey the
substance of their work to others skilled in the art. An algorithm
is here, and generally, conceived to be a self-consistent sequence
of operations leading to a desired result. The operations are those
requiring physical manipulations of physical quantities. Usually,
though not necessarily, these quantities take the form of
electrical or magnetic signals capable of being stored,
transferred, combined, compared, and otherwise manipulated. The
signals take on physical form when stored in a computer readable
storage medium, such as memory or non-volatile storage, and can
therefore, in operation, be referred to as physical quantities. It
has proven convenient at times, principally for reasons of common
usage, to refer to these signals as bits, values, elements,
symbols, characters, terms, numbers, or the like.
[0040] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise as apparent from
the following discussion, it should be appreciated that throughout
the description, discussions utilizing terms such as "processing"
or "computing" or "calculating" or "determining" or "displaying" or
the like, refer to the action and processes of a computer system,
or similar electronic computing device, that manipulates and
transforms data represented as physical quantities within the
computer system's registers and memories into other data similarly
represented as physical quantities within the computer system
memories or registers or other such information storage,
transmission or display devices.
[0041] The algorithms and displays presented herein are not
necessarily inherently related to any particular computer or other
apparatus. Various general purpose systems may be used with
programs to configure the general purpose systems in a specific
manner in accordance with the teachings herein, or it may prove
convenient to construct specialized apparatus to perform the
methods of some embodiments. The required structure for a variety
of these systems will appear from the description below. Thus, a
general purpose system can be specifically purposed by implementing
appropriate programs. In addition, the techniques are not described
with reference to any particular programming language, and various
embodiments may thus be implemented using a variety of programming
languages.
[0042] Referring once again to the example of FIG. 1, in the
example of FIG. 1, the USIMS server 104 is coupled to the network
102. The USIMS server 104 can be implemented on a known or
convenient computer system, specially purposed to provide USIMS
functionality. The USIMS server 104 is intended to illustrate one
server that has the novel functionality, but there could be
practically any number of USIMS servers coupled to the network 102
that meet this criteria. Moreover, partial functionality might be
provided by a first server and partial functionality might be
provided by a second server, where together the first and second
server provide the full functionality.
[0043] Functionality of the USIMS server 104 can be carried out by
one or more engines. As used in this paper, an engine includes a
dedicated or shared processor and, hardware, firmware, or software
modules that are executed by the processor. Depending upon
implementation-specific or other considerations, an engine can be
centralized or its functionality distributed. An engine can include
special purpose hardware, firmware, or software embodied in a
computer-readable medium for execution by the processor. Examples
of USIMS functionality are described with reference to FIGS.
4-24.
[0044] In the example of FIG. 1, the clients 106 are coupled to the
network 102. The clients 106 can be implemented on one or more
known or convenient computer systems. The clients 106 use the USIMS
functionality provided by the USIMS server 104. Depending upon the
implementation and/or preferences, the clients 106 can also carry
out USIMS functionality. Depending upon the implementation and/or
preferences, in addition to or instead of using the USIMS
functionality provided by the USIMS server 104, the clients 106 can
provide ODI or other useful data to the USIMS server 104. The
clients 106 can also be USIMS-agnostic, and take advantage of USIMS
functionality without implementing any novel functionality on their
own.
[0045] In the example of FIG. 1, the ODI data repository 108 is
coupled to the USIMS server 104. The ODI data repository 108 has
data that is useful to the USIMS server 104 for providing the USIMS
functionality. The ODI data repository 108 can store data entities,
such as those described later with reference to FIG. 24. The ODI
data repository 108, and other repositories described in this
paper, can be implemented, for example, as software embodied in a
physical computer-readable medium on a general- or specific-purpose
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. This and other
repositories described in this paper are intended, if applicable,
to include any organization of data, including tables,
comma-separated values (CSV) files, traditional databases (e.g.,
SQL), or other known or convenient organizational formats.
[0046] In an example of a system where the ODI data repository 108
is implemented as a database, a database management system (DBMS)
can be used to manage the ODI data repository 108. In such a case,
the DBMS may be thought of as part of the ODI data repository 108
or as part of the USIMS server 104, or as a separate functional
unit (not shown). A DBMS is typically implemented as an engine that
controls organization, storage, management, and retrieval of data
in a database. DBMSs frequently provide the ability to query,
backup and replicate, enforce rules, provide security, do
computation, perform change and access logging, and automate
optimization. Examples of DBMSs include Alpha Five, DataEase,
Oracle database, IBM DB2, Adaptive Server Enterprise, FileMaker,
Firebird, Ingres, Informix, Mark Logic, Microsoft Access,
InterSystems Cache, Microsoft SQL Server, Microsoft Visual FoxPro,
MonetDB, MySQL, PostgreSQL, Progress, SQLite, Teradata, CSQL,
OpenLink Virtuoso, Daffodil DB, and OpenOffice.org Base, to name
several.
[0047] Database servers can store databases, as well as the DBMS
and related engines. Any of the repositories described in this
paper could presumably be implemented as database servers. It
should be noted that there are two logical views of data in a
database, the logical (external) view and the physical (internal)
view. In this paper, the logical view is generally assumed to be
data found in a report, while the physical view is the data stored
in a physical storage medium and available to, typically, a
specifically programmed processor. With most DBMS implementations,
there is one physical view and a huge number of logical views for
the same data.
[0048] A DBMS typically includes a modeling language, data
structure, database query language, and transaction mechanism. The
modeling language is used to define the schema of each database in
the DBMS, according to the database model, which may include a
hierarchical model, network model, relational model, object model,
or some other applicable known or convenient organization. An
optimal structure may vary depending upon application requirements
(e.g., speed, reliability, maintainability, scalability, and cost).
One of the more common models in use today is the ad hoc model
embedded in SQL. Data structures can include fields, records,
files, objects, and any other applicable known or convenient
structures for storing data. A database query language can enable
users to query databases, and can include report writers and
security mechanisms to prevent unauthorized access. A database
transaction mechanism ideally ensures data integrity, even during
concurrent user accesses, with fault tolerance. DBMSs can also
include a metadata repository; metadata is data that describes
other data.
[0049] In the example of FIG. 1, the optional mail server 110 is
coupled to the network 102, to the USIMS server 104, and to the
mail data repository 112. The mail data repository 112 stores data
in a format that is useful to the mail server 110. In this example,
the mail server 110 is considered an "external application" in the
sense that the format of data in the mail data repository 112 is
not necessarily in the same format as in the ODI data repository
108. To the extent mail data is used by the USIMS server 104 in
this example, it is assumed that the mail data has been translated
into a format that is useful to the USIMS server 104, which may or
may not be necessary depending upon the implementation. For
example, in another implementation, the mail server 110 could be
implemented as an integrated application in the sense that the
format of data in the mail data repository 112 is in the same
format as in the ODI data repository 108. In this implementation,
it is possible that no translation of the data stored in the mail
data repository 112 into another format would be necessary.
[0050] Particularly where the USIMS server 104 functions as a
business process management (BPM) server, it may be desirable to
enable the USIMS server 104 to have access to mail data. BPM, as
used in this paper, is a technique intended to align organizations
with the needs of clients by continuously improving processes. BPM
is an advantageous implementation because it tends to promote
business efficacy while striving for innovation and integration
with technology.
[0051] It should be noted that business process modeling and
business process management are not the same, and, confusingly,
share the same acronym. In this paper, business process management
is given the acronym BPM, but business process modeling is not
given an acronym. Business process modeling is often, though not
necessarily, used in BPM. Business process modeling is a way of
representing processes in systems or software. The models are
typically used as tools to improve process efficiency and quality,
and can use Business Process Modeling Notation (BPMN) or some other
notation to model business processes.
[0052] A business process, as used in this paper, is a collection
of related, structured activities or tasks that produce a service
or product for a particular client. Business processes can be
categorized as management processes, operational processes, and
supporting processes. Management processes govern the operation of
a system, and include by way of example but not limitation
corporate governance, strategic management, etc. Operational
processes comprise the core business processes for a company, and
include by way of example but not limitation, purchasing,
manufacturing, marketing, and sales. Supporting processes support
the core processes and include, by way of example but not
limitation, accounting, recruiting, technical support, etc.
[0053] A business process can include multiple sub-processes, which
have their own attributes, but also contribute to achieving the
goal of the super-process. The analysis of business processes
typically includes the mapping of processes and sub-processes down
to activity level. A business process is sometimes intended to mean
integrating application software tasks, but this is narrower than
the broader meaning that is frequently ascribed to the term in the
relevant art, and as intended in this paper. Although the initial
focus of BPM may have been on the automation of mechanistic
business processes, it has since been extended to integrate
human-driven processes in which human interaction takes place in
series or parallel with the mechanistic processes.
[0054] Referring once again to the example of FIG. 1, the optional
DMA server 114 is coupled to the network 102, to the USIMS server
104, and to the document data repository 116. The document data
repository 116 stores data in a format that is useful to the DMA
server 114. In this example, the DMA server 114 is considered an
"external application" in the sense that the format of data in the
document data repository 116 is not necessarily in the same format
as in the ODI data repository 108. To the extent document data is
used by the USIMS server 104 in this example, it is assumed that
the document data has been translated into a format that is useful
to the USIMS server 104, which may or may not be necessary
depending upon the implementation. For example, in another
implementation, the DMA server 114 could be implemented as an
integrated application in the sense that the format of data in the
document data repository 116 is in the same format as in the ODI
data repository 108. In this implementation, it is possible that no
translation of the data in the document data repository 116 into
another format would be necessary.
[0055] The USIMS server 104 can, of course, be coupled to other
external applications (not shown) either locally or through the
network 102 in a known or convenient manner. The USIMS server 104
can also be coupled to other external data repositories.
[0056] FIG. 2 depicts an example of a USIMS system 200. In the
example of FIG. 2, the system 200 includes an application layer
202; a data management layer 204; an ODI repository 206; and a
network 208. The system 200 can provide a metadata environment to
capture tags and links to other data stored in the system 200, and
an ETL designed to extract data from ERP or other enterprise data
sources, transform the data, and load the data into a structured
repository. The structured repository uses ODI data (Jobs and
Outcomes) as a reference model that organizes the disparate
data.
[0057] The Application Layer is the top protocol layer in both
major models of computer networking, the Transmission Control
Protocol (TCP)/Internet Protocol (IP) model and the Open Systems
Interconnection (OSI) model. In TCP/IP, the Application Layer
contains protocols and methods that fall into the realm of
process-to-process communications via an IP network using Transport
Layer protocols to establish underlying host-to-host connections.
In the OSI model, the definition of the Application Layer is
narrower, distinguishing explicit functionality on top of the
Transport Layer at two additional levels, the Presentation Layer
and the Session Layer. Common application layer services, such as
by way of example but not limitation, virtual file, virtual
terminal, and job transfer and manipulation protocols, can provide
semantic conversion between application processes. As used in this
paper, the application layer 202 can be associated with the TCP/IP
model, the OSI model, some other applicable known or convenient
model, or no model at all. In the example of FIG. 2, the
application layer 202 includes a search engine 220, a process
engine 222, a segmentation engine 224, a metadata engine 226, a
strategy engine 228, a reporting engine 230, and a collaboration
engine 232.
[0058] In the example of FIG. 2, the search engine 220 can include
one or more communications protocols. An example of one such
protocol is the financial information exchange (FIX) protocol for
electronic communication of trade-related messages. It is a
self-describing protocol in many ways similar to other
self-describing protocols such as XML. (XML representation of
business content of FIX messages is known as FIXML.) FIX Protocol,
Ltd. was established for the purpose of ownership and maintenance
of the specification and owns the specification, while keeping it
in the public domain.
[0059] FIX is provided as an example in this paper because FIX is a
standard electronic protocol for pre-trade communications and trade
execution. Another example of a protocol is Society for Worldwide
Interbank Financial Telecommunication (SWIFT).
[0060] Yet another example is FIX adapted for streaming (FAST)
protocol, which is used for sending multicast market data. FAST was
developed by FIX Protocol, Ltd. to optimize data representation on
a network, and supports high-throughput, low latency data
communications. In particular, it is a technology standard that
offers significant compression capabilities for the transport of
high-volume market data feeds and ultra low latency applications.
Exchanges and market centers offering data feeds using the FAST
protocol include: New York Stock Exchange (NYSE) Archipelago,
Chicago Mercantile Exchange (CME), International Securities
Exchange (ISE), to name a few.
[0061] In operation, the search engine 220 can search data streams
for relevant data for tagging; identifying competitors; and
populating product, market communications, service programs, NPD
tables, etc. FAST is a good example protocol because of its use in
market-related communications that are reasonably likely to be
relevant to those who use one or more of the techniques described
in this paper.
[0062] In the example of FIG. 2, the process engine 222 can be
implemented as a BPM engine or a BPM suite (BPMS). An example of a
BPMS is Bluespring's BPM Suite 4.5. However, any applicable known
or convenient BPM engine could be used. Of course, the BPM engine
must meet the needs of the system for which it is used, and may or
may not work "off the shelf" with techniques described in this
paper.
[0063] In the example of FIG. 2, the segmentation engine 224
facilitates segmenting a market. This can involve providing data
manipulation tools to facilitate compiling and loading data sets
into external statistical analysis packages, providing tools to
interact with statistical analysis and modeling packages and import
additional metadata tags into a job/outcome data schema, and/or
providing utilities to enhance the visual representation and
tabular reporting of the statistical data properties.
[0064] In the example of FIG. 2, the metadata engine 226 can be
implemented as a data analysis engine that tags data records
algorithmically, appends meta-data associated with business
information to data records, facilitates pipeline prioritization,
facilitates calculation, ranking and reporting of opportunity
scores, facilitates interaction with data, and performs other
functionality that makes data more useful in a BI context.
[0065] In the example of FIG. 2, the strategy engine 228 can be
implemented as a business intelligence (BI) tool. An example of a
BI tool is Microsoft Office PERFORMANCEPOINT.RTM. Server. An
advantage Of PERFORMANCEPOINT.RTM. is that it is integrated with
other Microsoft Office products, such as Excel, Visio, SQL Server,
SHAREPOINT.RTM. Server, and the like, and has monitoring and
analytic capabilities (e.g., Dashboards, Scorecards, Key
Performance Indicators (KPI), Reports, Filters, and Strategy Maps),
and planning and budgeting capabilities. Using the toolset, one can
create data source connections, create views that use the data
source connections, assemble the views in a dashboard, and deploy
the dashboard to Microsoft Office SHAREPOINT.RTM. Server 2007
(MOSS) or Windows SHAREPOINT.RTM. Services, and can save content
and security information to a SQL sever database. Using the
toolset, one can also define, modify, and maintain logical business
models integrated with business rules, workflows, and enterprise
data. It should be noted that the scope of the
PERFORMANCEPOINT.RTM. product has grown with time, and some of the
capabilities may seem to overlap with some of the other engines in
the Application Layer 202.
[0066] In general, the strategy engine 228 can include tools that
are useful for pulling in data from various sources so as to
facilitate strategic planning, such as needs delivery enhancement
strategy, needs-based IP strategy, innovation strategy, market
growth strategy, consumption chain improvement strategy, etc. It is
probably desirable to ensure that the tools in the strategy engine
228 are user-friendly, since human input is often desirable for
certain strategic planning.
[0067] In the example of FIG. 2, the reporting engine 230 can be
implemented as Microsoft SQL Server Reporting Services (SSRS) to
prepare and deliver interactive and printed reports. Crystal
Reports is another implementation, and any applicable known or
convenient BI tool could be used. It is frequently seen as an
advantage to have reports that can be generated in a variety of
formats including Excel, PDF, CSV, XML, TIFF (and other image
formats), and HTML Web Archive, which SSRS can do. Other report
generators can offer additional output formats, and may include
useful features such as geographical maps in reports.
[0068] In the example of FIG. 2, the collaboration engine 232 can
be implemented as a MOSS. It should be noted that Windows
SHAREPOINT.RTM. Services (WSS) might actually provide adequate
functionality to serve as a collaboration engine, but the MOSS can
bolted on top to provide additional services and functionality.
MOSS and similar technologies can include browser-based
collaboration and document management, plus the ability to host web
sites that access shared workspaces and documents, as well as
specialized applications like wikis and blogs; and tools can enable
the MOSS to serve as a social networking platform. There are many
conventional collaboration tools that could be used as or as part
of the collaboration engine 230 implementation, including, by way
of example but not limitation, adenine IntelliEnterprise, Alfresco,
Nuxeo, Cisco WebEx Connect, Liferay portal, Drupal, eXo Platform,
IBM Lotus Notes, 03spaces, OnBase, Novell--Teaming and Conferencing
link, Open Text Corporation's Livelink ECM--Extended Collaboration,
Oracle Collaboration Suite, MediaWiki, and Atlassian
Confluence.
[0069] In general, any applicable known or convenient tool that
acts as a collaborative workspace and/or tool for the management or
automation of business processes could be implemented.
Collaborative technologies are tools that enable people to interact
with other people within a group more efficiently and effectively.
So even email discussion lists and teleconferencing tools could
function as a collaboration engine 230; though sophisticated tools
are likely to encompass much more. For example, it is probably
desirable to enable users to have greater control in finding,
creating, organizing, and collaborating in a browser-based
environment. It may also be desirable to allow organization of
users in accordance with their access, capabilities, role, and/or
interests.
[0070] In the example of FIG. 2, the data management layer 204 is
coupled to the application layer 202. Strictly speaking, the data
management layer 204 would probably be considered part of the
application layer in a computer networking model. In this paper, a
distinction is drawn between "core applications" that perform core
customer-based ontology and delivery functions, and interfaces to
repositories and applications that support the management of data
in the repositories to make the data useful to the core
applications. In the example of FIG. 2, the data management layer
204 includes an ODI transaction engine 234, an ODI transformation
rules engine 236, and an extract, transform, load (ETL) engine 238.
The ODI transaction engine 234 is coupled to the ODI repository 206
and the ETL engine 238 is coupled to the network 208, which is, at
least in some implementations, coupled to applicable known or
convenient ETL repositories (not shown).
[0071] The ODI transaction engine 234 provides interaction between
engines capable of writing to or reading from the ODI repository
206. If a data stream is being provided, the ODI transformation
rules engine 236 may or may not transform the data into an
appropriate ODI format. Similarly, if data is being provided from
the ODI repository 206 to an engine that can make no, or limited,
use of the ODI data, the ODI transformation rules engine 236 can
transform the data from the ODI format to some other format. In a
specific implementation, the ODI transformation rules engine 236 is
only needed when interfacing with external devices because all
internal devices can use data in the ODI format.
[0072] The ETL engine 238 extracts data from outside sources,
transforms the data to fit operational requirements, and loads the
transformed data into an internal repository (e.g., the ODI
repository 206). The ETL engine 238 can store an audit trail, which
may or may not have a level of granularity that would allow
reproduction of the ETL's result in the absence of the ETL raw
data. A typical ETL cycle can include the following steps:
initialize, build reference data, extract, validate, transform,
stage, audit report, publish, archive, clean up.
[0073] In operation, the ETL engine 238 extracts data from one or
more source systems, which may have different data organizations or
formats. Common data source formats are relational databases and
flat files, but can include any applicable known or convenient
structure, such as, by way of example but not limitation,
Information Management System (MIS), Virtual Storage Access Method
(VSAM), Indexed Sequential Access Method (ISAM), web spidering,
screen scraping, etc. Extraction can include parsing the extracted
data, resulting in a check if the data meets an expected pattern or
structure.
[0074] In operation, the ETL engine 238 transforms the extracted
data by applying rules or functions to the extracted data to derive
data for loading into a target repository. Different data sources
may require different amounts of manipulation of the data.
Transformation types can include, by way of example but not
limitation, selecting only certain columns to load, translating
coded values, encoding free-form values, deriving a new calculated
value, filtering, sorting, joining data from multiple sources,
aggregation, generating surrogate-key values, transposing,
splitting a column into multiple columns, applying any form of
simple or complex data validation, etc.
[0075] In operation, the ETL engine 238 loads the data into the
target repository. In a particular implementation, the data must be
loaded in a format that is usable to an ODI system, perhaps using
the ODI transformation rules engine 236. Loading data can include
overwriting existing information or adding new data in historized
form. The timing and scope to replace or append are implementation-
or configuration-specific.
[0076] The ETL engine 238 can make use of an established ETL
framework. Some open-source ETL frameworks include Clover ETL,
Enhydra Octopus, Mortgage Connectivity Hub, Pentaho Data
Integration, Talend Open Studio, Scriptella, Apatar, Jitterbit 2.0.
A freeware ETL framework is Benetl. Some commercial ETL frameworks
include Djuggler Enterprise, Embarcadero Technologies DT/Studio,
ETL Solutions Transformation Manager, Group 1 Software DataFlow,
IBM Information Server, IBM DB2 Warehouse Edition, IBM Cognos Data
Manager, IKAN-ETL4ALL, Informatica PowerCenter, Information
Builders-Data Migrator, Microsoft SQL Server Integration Services
(SSIS), Oracle Data Integrator, Oracle Warehouse Builder, Pervasive
Business Integrator, SAP Business Objects-Data Integrator, SAS Data
Integration Studio, to name several.
[0077] A business process management (BPM) server, such as
Microsoft BizTalk Server, can also be used to exchange documents
between disparate applications, within or across organizational
boundaries. BizTalk provides business process automation, business
process modeling, business-to-business communication, enterprise
application integration, and message broker.
[0078] An enterprise resource planning (ERP) system used to
coordinate resources, information, and activities needed to
complete business processes, can also be accessed. Data derived
from an ERP system is typically that which supports manufacturing,
supply chain management, financials, projects, human resources, and
customer relationship management from a shared data repository.
[0079] Derived data can also be Open Innovation (OI) data, which is
an outside source of innovation concepts. This can include
transactional data (send a network of outside problem solvers
Opportunities for new ideas and receive the ideas back) and
unstructured data (repository of ideas) for searching.
[0080] In the example of FIG. 2, the ODI repository 206 includes
data associated with customers, including customer profile,
customer jobs region, and customer outcomes region. In a specific
implementation, a customer profile region can include customer
profile records that include customer identifier (ID) and profile
attributes. The customer ID can be in accordance with a public key
infrastructure (PKI). The profile attributes can include fields
associated with, for example, demographics, customer of . . . ,
products used, job role, customer chain role, consumption chain
role, outcome-driven segments, and attitudinal segments. The
customer jobs region can include a customer type code (note that
customer ID and customer type code can be dual PKIs), job map
models, scoring tables, and raw data tables. The customer outcomes
region can include a customer type code, job/outcome model tables,
scoring tables, and raw data tables.
[0081] The ODI repository 206 can also include price sensitivity
data tables, which can include jobs and outcomes (note that jobs
and outcomes can be implemented as dual PKIs) and fields that
include customer IDs.
[0082] The ODI repository 206 can also include an ODI translation
data region including customer file translation tables,
product/service offerings translation tables, sales and marketing
campaigns translation tables, innovation concepts translation
tables, business development translation tables, and external data
translation tables. In a specific implementation, the customer file
translation tables include a customer ID to customer type code
translation table. In a specific implementation, the
product/service offerings translation tables can include
job/outcomes as PKIs and cross-references indicating relevance for
product/service offerings, company products (subsystems and parts,
service programs), competitor products (subsystems, service
programs), and pipeline products. In a specific implementation, the
sales and marketing campaigns translation tables can include
job/outcome as PKIs and cross-references indicative of relevance
for sales campaigns and marketing campaigns (company and
competitor). In a specific implementation, the innovation concepts
translation tables can include job/outcomes as PKIs and
cross-references indicative of new product development, R&D
roadmap, sales and service concepts, and new marketing positioning
and branding concepts. In a specific implementation, the business
development translation tables can include job/outcome PKIs and
cross references indicative of new M&A targets and new
strategic partners. In a specific implementation, the external data
translation tables can include job/outcome PKIs and
cross-references indicative of patent records, open innovation
database records, and trade publication records.
[0083] Advantageously, customer needs can be captured as the needs
related to a market, goods, and services. A core functional job can
have emotional jobs (e.g., personal jobs and social jobs) and other
functional jobs (e.g., jobs indirectly related to core job and jobs
directly related to core job), each of which can be analyzed using
a uniform metric. During a concept innovation phase, a job can be
broken down into multiple steps, each step potentially having
multiple outcomes associated with it. Desired outcomes are the
metrics customers use to measure the successful execution of a job.
When the outcomes are known or predicted, the concept innovation
stage passes into the devise solution stage, and then a design
innovation stage where consumption chain jobs are identified, such
as purchase, receive, install, set-up, learn to use, interface,
transport, store, maintain, obtain support, upgrade, replace,
dispose, to name several. Then it is time to design/support a
solution.
[0084] FIG. 3 depicts a flowchart 300 of an example of a method for
external data integration. The benefit of external integration is
to normalize disparate enterprise market data from exogenous
sources into a job/outcome reference model of ODI. Advantageously,
enterprises can cull information from these sources into a
consistent and searchable model of the marketplace. This method and
other methods are depicted as serially arranged modules. However,
modules of the methods may be reordered, or arranged for parallel
execution as appropriate.
[0085] In the example of FIG. 3, the flowchart 300 starts at module
302 with tagging external data records with job/outcome identifiers
using new algorithms that transform data into new data structures,
such as the data entities described with reference to FIG. 2. The
new algorithms begin with identifying whether a job or outcome of
interest relates to a specific field-of-use or solution context, or
to a general purpose solution such as a technology used by many
systems.
[0086] Depending on whether the answer is specific or general, the
system assigns an appropriate search strategy embodied within a
string of external data sources that can include appropriate
solutions. For outcomes associated with specific fields-of-use the
search strategy includes specific and highly qualified external
data sources which can include, for example, particular patent
classification sub classes, trade or academic publications, or
other applicable data.
[0087] For outcomes associated with general purpose needs the
system determines systematically the best sources for new enabling
technologies or solutions by automatically identifying and
weighting these through a routine like modern textual search. The
process continues by searching records found through this method
for text strings that include synonymous terms for the objects of
control or action from the particular outcome or job of interest.
The process completes by recording the existence of this match as a
data tag appended to the external data record identifying the
outcome/job that was matched and a score value is assigned
representing the closeness of this match.
[0088] In the example of FIG. 3, the flowchart 300 continues to
module 304 with estimating a level to which the solution described
in the external record satisfies the job/outcome of interest. The
solution can satisfy the job/outcome of interest either objectively
or subjectively by manual expert scoring or through available crowd
sourcing scores and recording this as coefficients. Crowd sourcing
scores might, for example, be derived from patent citations, web
page visits, records of the success of the inventor/author in the
field of use in general, novel real options based scores of large
communities of connected users, or other methods to capture group
opinion on the value of solutions to increase satisfaction of the
job/outcome of interest.
[0089] In the example of FIG. 3, the flowchart 300 continues to
module 306 with productionalizing in translation tables.
[0090] In the example of FIG. 3, the flowchart 300 continues to
module 308 with incorporating into query code. It is likely that
queries and reports will be desirable in a system implemented in
accordance with one or more of the techniques described in this
paper. Such reports may be ad hoc or pre-formed. Ad hoc reports may
include solution value added assessments, business case extracts,
marketing and sales campaigns needs extracts, or other queries as
necessary to provide functionality required or desired to perform
uses of a system implemented in accordance with one or more of the
techniques described in this paper. Some examples of ad hoc reports
are given below:
[0091] Solution value added assessment is an ad hoc use having the
same general purpose as a needs delivery enhancement strategy
report (see, e.g., FIG. 6), but constructed specifically to assess
the marketability of a particular solution concept during ideation.
It may incorporate a process such as that described with reference
to the example of FIG. 3, flowchart 300, module 304.
[0092] Business case extracts of the database is an ad hoc use to
assess the return on investment (ROI) of particular solutions. The
extracts can be used by other reports, or separate business case
models to facilitate or improve enterprise investment decision
making. The data values extracted include, for example, job/outcome
opportunity score, customer data, satisfaction improvement
estimates of solutions (see, e.g., FIG. 3), cost and pricing data,
and other applicable information.
[0093] Marketing and sales campaigns needs extracts are ad hoc
reports to assess the market effect of new marketing and sales
campaigns based on positioning a product to address unmet needs or
otherwise using similar insights to design and assess new marketing
and sales campaigns.
[0094] FIGS. 4-8 depict examples of pre-formed query methods. FIG.
4 depicts a flowchart 400 of an example of a competitive assessment
method. Advantageously, a competitive assessment will enable an
enterprise to quantitatively analyze a probable marketplace
effectiveness of known customer-facing activities of its
competitors, and forecast impacts to its own business plans.
[0095] In the example of FIG. 4, the flowchart 400 starts at module
402 with identifying competitors. Competitors can be identified
explicitly, found through search, ETL, or the like, or a
combination of these. Competitors can also be identified later in
the process, for example after a market becomes more defined.
[0096] In the example of FIG. 4, the flowchart 400 continues to
module 404 with populating product, market communications, service
programs, NPD tables, etc.
[0097] In the example of FIG. 4, the flowchart 400 continues to
module 406 with estimating satisfaction coefficients through
customer data analysis, manual estimation, or crowd sourcing and
may incorporate a process such as that described with reference to
the example of FIG. 3, flowchart 300, module 304.
[0098] In the example of FIG. 4, the flowchart 400 continues to
module 408 with productionalizing in translation tables.
[0099] In the example of FIG. 4, the flowchart 400 continues to
module 410 with incorporating into query code.
[0100] FIG. 5 depicts a flowchart 500 of an example of a needs
delivery of current products method. Advantageously, a needs
delivery of current products will provide an enterprise with a
flexible reporting engine to assess how well the current state of
products are fulfilling the needs of customers across many
different referential dimensions (e.g., functional needs, emotional
needs, consumption chain needs, platforms, market segments,
etc.).
[0101] In the example of FIG. 5, the flowchart 500 starts at module
502 with determining assessment and reporting criteria.
[0102] In the example of FIG. 5, the flowchart 500 continues to
module 504 with selecting a needs and product set based on the
criteria.
[0103] In the example of FIG. 5, the flowchart 500 continues to
module 506 with selecting meta-data for a report based on the
criteria.
[0104] In the example of FIG. 5, the flowchart 500 continues to
module 508 with analyzing and displaying importance, satisfaction,
and opportunity data.
[0105] In the example of FIG. 5, the flowchart 500 continues to
module 510 with preparing and displaying meta-data reports (e.g.,
un-penetrated economic opportunity).
[0106] FIG. 6 depicts a flowchart 600 of an example of a needs
delivery enhancement strategy method. Advantageously, a needs
delivery enhancement strategy builds upon the prior use to assess
the level of enhancement that pipeline innovations are likely to
deliver to the current business portfolio. This may include a
product roadmap and R&D.
[0107] In the example of FIG. 6, the flowchart 600 starts at module
602 with determining assessment and reporting criteria.
[0108] In the example of FIG. 6, the flowchart 600 continues to
module 604 with selecting needs and markets based on the
criteria.
[0109] In the example of FIG. 6, the flowchart 600 continues to
module 606 with querying current products, NPD projects, and/or
R&D initiatives for needs enhancements.
[0110] In the example of FIG. 6, the flowchart 600 continues to
module 608 with analyzing and displaying importance, satisfaction,
and opportunity data and can incorporate a process such as that
described with reference to FIG. 3, flowchart 300, module 304.
[0111] In the example of FIG. 6, the flowchart 600 continues to
module 610 with preparing and displaying a needs-gaps report.
[0112] In the example of FIG. 6, the flowchart 600 continues to
module 612 with preparing and displaying meta-data strategy reports
(e.g., un-penetrated economic opportunity).
[0113] FIG. 7 depicts a flowchart 700 of an example of a needs
based IP strategy method. Advantageously, a needs-based IP strategy
can enable an enterprise to efficiently scout internal and external
sources of IP, technologies, and other innovation solutions to
secure advantages in pursuing strategies to satisfy unmet market
needs.
[0114] In the example of FIG. 7, the flowchart 700 starts at module
702 with determining assessment and reporting criteria.
[0115] In the example of FIG. 7, the flowchart 700 continues to
module 704 with selecting a product or technology set based on the
criteria.
[0116] In the example of FIG. 7, the flowchart 700 continues to
module 706 with identifying matching enterprise IP.
[0117] In the example of FIG. 7, the flowchart 700 continues to
module 708 with displaying needs addressed by the IP.
[0118] In the example of FIG. 7, the flowchart 700 continues to
module 710 with analyzing and displaying needs un-addressed by the
IP with opportunity assessment data.
[0119] In the example of FIG. 7, the flowchart 700 continues to
module 712 with importing needs tagged external patent records and
outside innovation records.
[0120] In the example of FIG. 7, the flowchart 700 continues to
module 714 with preparing and displaying reports on IP acquisition,
defense, and development priorities.
[0121] FIG. 8 depicts a flowchart 800 of an example of a
consumption chain needs delivery method. Advantageously, a
consumption chain needs delivery can enable an enterprise to assess
disparate needs and associated importance levels of participants in
consumption chains of the enterprise's products in order to
optimize investments for sales impact.
[0122] In the example of FIG. 8, the flowchart 800 starts at module
802 with constructing a consumption chain job map with mapping
tools.
[0123] In the example of FIG. 8, the flowchart 800 continues to
module 804 with querying ODI needs data tables for matching
job/outcome data.
[0124] In the example of FIG. 8, the flowchart 800 continues to
module 806 with appending meta-data on importance level on
participant in consumption chain in purchasing decisions.
[0125] In the example of FIG. 8, the flowchart 800 continues to
module 808 with appending economic business case data quantifying
the particular consumption cases.
[0126] In the example of FIG. 8, the flowchart 800 continues to
module 810 with generating reports for price sensitivity data
collection.
[0127] In the example of FIG. 8, the flowchart 800 continues to
module 804 with generating lever reports to isolate economic
opportunities in satisfying consumption chain needs.
[0128] FIG. 9 depicts a flowchart 900 of an example of a method for
computationally enabling and enhancing an ODI process. In the
example of FIG. 9, the flowchart 900 starts at module 902 with
creating an innovation strategy.
[0129] FIG. 10 depicts a flowchart 1000 of an example of a method
for creating an innovation strategy. In the example of FIG. 10, the
flowchart 1000 starts at module 1002 with facilitating gathering of
baseline data on strategy variables. This may include, for example,
conducting an inventory of a current strategic roadmap for
qualitative impact assessment and/or conducting an inventory of
anecdotal data and hypotheses on unmet and over-served needs.
[0130] In the example of FIG. 10, the flowchart 1000 continues to
module 1004 with generating reports that facilitate the decision of
prioritizing projects to pursue viable objectives in a market
growth strategy. Five market growth strategies are provided as
examples herein, and it should be recognized that at module 1004,
reports could be generated to facilitate the decision of
prioritizing projects to pursue viable objectives in one or more
market growth strategies, with the number depending upon
implementation and/or configuration. The examples of market growth
strategies are: 1) grow or protect a high-share market, 2)
aggressively grow a low-share market the enterprise is already in,
3) enter an attractive market that others are already in, 4) enter
a new or emerging high growth market, 5) find a market for a new or
emerging technology.
[0131] FIGS. 11-15 depict flowcharts of examples of market growth
strategy methods that advantageously use ODI data to assess
economic valuation and risks of different market growth strategies.
FIG. 11 depicts a flowchart 1100 of an example of a method for
growing or protecting a high-share market. In the example of FIG.
11, the flowchart 1100 starts at module 1102 with reporting on key
trends and competitive position in core markets. For example, the
report can include share, position, response to key trends,
strengths, weaknesses, or other applicable information.
[0132] In the example of FIG. 11, the flowchart 1100 continues to
module 1104 with facilitating qualitative impact assessment of
developing core market innovations to a strategic roadmap. For
example, the assessment can include how many pipeline products are
touched, whether ODI projects will enhance or detract from the
pipeline product (and how much), the dollar value of pipeline
products touched, the revenue value of pipeline products touched,
and other applicable information.
[0133] In the example of FIG. 11, the flowchart 1100 continues to
module 1106 with facilitating inventory of value delivery platforms
within each core market.
[0134] In the example of FIG. 11, the flowchart 110 continues to
module 1108 with facilitating a qualitative risk, cost, and benefit
assessment of the different innovation strategies on the core
market platform. For example, the assessment can include platform
innovation, business model innovation, features, and other
applicable information.
[0135] FIG. 12 depicts a flowchart 1200 of an example of a method
for aggressively growing a low-share market the enterprise is
already in. In the example of FIG. 12, the flowchart 1200 starts at
module 1202 with facilitating inventory of attractive low share
markets.
[0136] In the example of FIG. 12, the flowchart 1200 continues to
module 1204 with reporting on key trends and competitive position
in underperforming markets. For example, the report can include
share, position, response to key trends, strengths, weaknesses, and
other applicable information.
[0137] In the example of FIG. 12, the flowchart 1200 continues to
module 1206 with facilitating inventory of value delivery platforms
within underperforming markets.
[0138] In the example of FIG. 12, the flowchart 1200 continues to
module 1208 with facilitating a qualitative risk, cost, and benefit
assessment of applying different innovation strategies to
underperforming market platforms. For example, the assessment can
include platform innovation, business model innovation, feature
development, and other applicable information.
[0139] FIG. 13 depicts a flowchart 1300 of an example of a method
for entering an attractive market that others are already in. In
the example of FIG. 13, the flowchart 1300 starts at module 1302
with facilitating inventory of attractive new but proven
markets.
[0140] In the example of FIG. 13, the flowchart 1300 continues to
module 1304 with reporting on key trends and competitive position
in new markets. For example, the report can include share,
position, response to key trends, strengths, weaknesses, and other
applicable information.
[0141] In the example of FIG. 13, the flowchart 1300 continues to
module 1306 with facilitating a qualitative risk, cost, and benefit
assessment of developing new value delivery platforms for the new
market.
[0142] FIG. 14 depicts a flowchart 1400 of an example of a method
for entering a new or emerging high growth market. In the example
of FIG. 14, the flowchart 1400 starts at module 1402 with
facilitating inventory of new and emerging high growth markets.
[0143] In the example of FIG. 14, the flowchart 1400 continues to
module 1404 with reporting on key trends and competitive position
in emerging high growth markets. For example, the report can
include share, position, response to key trends, strengths,
weaknesses, and other applicable information.
[0144] In the example of FIG. 14, the flowchart 1400 continues to
module 1406 with facilitating a qualitative risk, cost, and benefit
assessment of developing new value delivery platforms for the new
market.
[0145] FIG. 15 depicts a flowchart 1500 of an example of a method
for finding a market for a new or emerging technology. In the
example of FIG. 15, the flowchart 1500 starts at module 1502 with
facilitating inventory of new and emerging technologies.
[0146] In the example of FIG. 15, the flowchart 1500 continues to
module 1504 with facilitating a qualitative risk, cost, and benefit
assessment of developing new value delivery platforms incorporating
the new technology.
[0147] Referring once again to the example of FIG. 10, the
flowchart 1000 continues to module 1006 with facilitating the
creation of an overall growth blueprint. Advantageously, the growth
blueprint can enable the business to orchestrate and prioritize the
market growth strategy through the use of the ODI data. If multiple
market growth strategies are pursued, multiple growth blueprints
may be created. The overall growth blueprint can, in addition,
identify or facilitate the identification of particular market
growth initiatives in implementing the market growth strategy
through the use of the ODI data.
[0148] FIG. 16 depicts a flowchart 1600 of an example of a method
for facilitating the creation of an overall growth blueprint. In
the example of FIG. 16, the flowchart 1600 starts at module 1602
with consolidating the inventory of jobs and demographics (the
markets) in which the company competes or seeks to compete. The
consolidation can include, for example, an evaluation of possible
market growth strategies.
[0149] In the example of FIG. 16, the flowchart 1600 continues to
module 1604 with facilitating support to pursue a market growth
initiative in a growth path model deemed available for the market
of interest. The support may include, for example, using a growth
paths model to facilitate a subjective assessment of the growth
blueprint for one or more markets of interest. It may be desirable
to evaluate the likelihood of desired outcomes, company-executable
actions, and costs to satisfy assumptions, conditions precedent,
and management decision criteria that must be present to support
pursuing the market growth initiative. This capability can be
controlled by user privileges.
[0150] In the example of FIG. 16, the flowchart 1600 continues to
module 1606 with generating a growth blueprint. The growth
blueprint can represent, for example, management's selection of
markets of interest, associated market growth initiatives with the
presumptive growth paths game plan, and other applicable data. To
generate the growth blueprint, it may be desirable to compile
actions on plan dependencies that must be taken to satisfy
conditions deemed necessary for the success of the game plan. This
capability can be controlled by user privileges.
[0151] Referring once again to the example of FIG. 10, the
flowchart 1000 continues to module 1008 with facilitating the
development of a consumption chain improvement strategy.
[0152] FIG. 17 depicts a flowchart 1700 of an example of a method
for facilitating the development of a consumption chain improvement
strategy. In the example of FIG. 17, the flowchart 1700 starts at
module 1702 with facilitating an estimation of a quantitative
business impact from known or suspected consumption chain
bottlenecks. Facilitating the estimation can include facilitating
an inventory of known and suspected consumption chain bottlenecks
to aid in the estimation. It may also be desirable to sort priority
bottlenecks into the consumption chain jobs of, for example:
purchase, receive, install, set-up, learn to use, interface,
transport, store, maintain, dispose, or some other applicable
category.
[0153] In the example of FIG. 17, the flowchart 1700 continues to
module 1704 with facilitating compilation of consumption chain
growth plan dependencies. The consumption chain growth plan
dependencies can be compiled automatically or by an expert user,
depending upon implementation and/or preference. Automated features
can search growth plan dependencies for terms that match or are
associated with the consumption chain jobs of, for example:
purchase, receive, install, set-up, learn to use, interface,
transport, store, maintain, dispose, or some other applicable
category.
[0154] In the example of FIG. 17, the flowchart 1700 continues to
module 1706 with aggregating the consumption chain jobs requiring
opportunity research and prioritizing into a game plan.
[0155] Referring once again to the example of FIG. 9, the flowchart
900 continues to module 904 with aggregating outcomes. Aggregating
outcomes can include facilitating qualitative research (FIG. 18) or
quantitative research (FIG. 19).
[0156] FIG. 18 depicts a flowchart 1800 of an example of a method
for facilitating qualitative research. In the example of FIG. 18,
the flowchart 1800 starts at module 1802 with providing a generic
hierarchy of jobs job map template and note taking tools to map the
job of interest and important related jobs.
[0157] In the example of FIG. 18, the flowchart 1800 continues to
module 1804 with providing outcome gathering common questions to
ask.
[0158] In the example of FIG. 18, the flowchart 1800 continues to
module 1806 with providing a shared environment for users to net
outcomes down to the critical set. This capability can be
controlled by user privileges.
[0159] In the example of FIG. 18, the flowchart 1800 continues to
module 1808 with providing an automated tool to translate other
primary and secondary market research into outcome statements.
[0160] FIG. 19 depicts a flowchart 1900 of an example of a method
for facilitating quantitative research. In the example of FIG. 19,
the flowchart 1900 starts at module 1902 with extracting job and
outcome statements directly into web survey tools.
[0161] In the example of FIG. 19, the flowchart 1900 continues to
module 1904 with facilitating procurement of customer lists for
direct quantitative research. This capability can be controlled by
user privileges.
[0162] In the example of FIG. 19, the flowchart 1900 continues to
module 1906 with providing rule-based utilities to assign survey
participants to segments or groups for later analytical purposes.
It may be desirable to provide tools and utilities to tag survey
participants with screening and segmentation factors. This
capability can be controlled by user privileges.
[0163] In the example of FIG. 19, the flowchart 1900 continues to
module 1908 with providing tools to deploy surveys directly to
customers, screen out known survey abusers, and randomize data
collection for reliability. This capability can be controlled by
user privileges.
[0164] In the example of FIG. 19, the flowchart 1900 continues to
module 1910 with assessing customer response data validity through
co-variance assessments on like outcomes.
[0165] In the example of FIG. 19, the flowchart 1900 continues to
module 1912 with automating collection of price sensitivity input
during initial quantitative research.
[0166] In the example of FIG. 19, the flowchart 1900 continues to
module 1914 with providing tools to import survey response data
directly into the data model. This capability can be controlled by
user privileges.
[0167] In the example of FIG. 19, the flowchart 1900 continues to
module 1916 with providing tools to optionally distribute data with
population averages back to survey participants to encourage
customer engagement. These tools, while useful, are optional.
[0168] In the example of FIG. 19, the flowchart 1900 continues to
module 1918 with providing search tools to find other jobs and
outcomes from other company or benchmark research reports and a
utility for affinity-tagging. The search tools can be automated.
The benchmark research reports can be Strategyn.TM. benchmark
research reports. The affinity-tagging can be used to record
associations and facilitate insights and inferences between related
factors of separate studies. This capability can be controlled by
user privileges.
[0169] Referring once again to the example of FIG. 9, the flowchart
900 continues to module 906 with identifying opportunities.
[0170] FIG. 20 depicts a flowchart 2000 of an example of a method
for identifying opportunities. In the example of FIG. 20, the
flowchart 2000 starts at module 2002 with automating calculation,
ranking, and reporting of opportunity scores with associated
metadata. It may be desirable to provide report design tools to
customize query logic used to build reports.
[0171] In the example of FIG. 20, the flowchart 2000 continues to
module 2004 with building and displaying opportunity landscape
diagrams.
[0172] In the example of FIG. 20, the flowchart 2000 continues to
module 2006 with providing graphic based utilities to enhance and
interact with data depicted in the landscapes. This can facilitate
identifying, for example, affinities and correlation factors with
other data points in the landscape, particular solution concepts,
market growth strategies, market growth paths,
dependencies/insights on assumptions, conditions precedent,
decision criteria related to management investment decisions, and
other applicable information.
[0173] In the example of FIG. 20, the flowchart 2000 continues to
module 2008 with providing graphic based utilities to enhance and
modify visual representations of data within landscape diagrams.
This can enable a user to accentuate, for example, relationships,
properties of data points, or other insights. Other capabilities of
the utilities can include enabling visualization of economic
opportunity from satisfying unmet needs and integration of
statistical modeling methods to a project. This capability can be
controlled by user privileges.
[0174] Referring once again to the example of FIG. 9, the flowchart
900 continues to module 908 with segmenting the market.
[0175] FIG. 21 depicts a flowchart 2100 of an example of a method
for segmenting the market. In the example of FIG. 21, the flowchart
2100 starts at module 2102 with providing data manipulation tools
to facilitate compiling and loading of datasets into external
statistical analysis packages.
[0176] In the example of FIG. 21, the flowchart 2100 continues to
module 2104 with providing tools to interact with statistical
analysis and modeling packages and import additional metadata tags
into a job/outcome data schema. The metadata tags may include, for
example, cluster affinity scores. This capability can be controlled
by user privileges.
[0177] In the example of FIG. 21, the flowchart 2100 continues to
module 2106 with providing utilities to enhance the visual
representation and tabular reporting of the statistical data
properties.
[0178] Referring once again to the example of FIG. 9, the flowchart
900 continues to module 910 with defining the targeting
strategy.
[0179] FIG. 22 depicts a flowchart 2200 of an example of a method
for defining the targeting strategy. In the example of FIG. 22, the
flowchart 2200 starts at module 2202 with providing a tool to
meta-tag jobs and outcome statements in respective data entities
with correlation values. The correlation values are useful to
assess alignment of current and future solutions with opportunities
of interest. This capability can be controlled by user
privileges.
[0180] In the example of FIG. 22, the flowchart 2200 continues to
module 2204 with providing a tool for end users to meta-tag jobs
and outcome statements in respective data entities with other
thematic tags. The thematic tags are useful to facilitate
collaborative ideation and business case development. This
capability can be controlled by user privileges.
[0181] In the example of FIG. 22, the flowchart 2200 continues to
module 2206 with delivering real-time tabular and visual
representations of the tagged jobs and outcomes to facilitate
innovation collaboration.
[0182] In the example of FIG. 22, the flowchart 2200 continues to
module 2208 with providing exports of jobs and outcomes. The
exports can be useful to facilitate external solution sourcing and
imports of respondent solutions into the data entities supporting
reporting. This capability can be controlled by user
privileges.
[0183] In the example of FIG. 22, the flowchart 2200 continues to
module 2210 with providing a utility to scout, cull, assess the
value of, and organize external sources of pre-made solutions
against jobs and outcomes. The jobs and outcomes can include, for
example, new technologies and inventions.
[0184] In the example of FIG. 9, the flowchart 900 continues to
module 912 with positioning current offerings. This can be
automated using capabilities described above with reference to one
or more of modules 902-910.
[0185] In the example of FIG. 9, the flowchart 900 continues to
module 914 with prioritizing the pipeline. Prioritizing the
pipeline can include providing a tool set to automate
prioritization of a business' new product development, R&D, and
business development by leveraging the capability. This may involve
a process similar to that described above with reference to FIG.
6.
[0186] In the example of FIG. 9, the flowchart 900 continues to
module 916 with conceptualizing breakthroughs.
[0187] FIG. 23 depicts a flowchart 2300 of an example of a method
for conceptualizing breakthroughs. In the example of FIG. 23, the
flowchart 2300 starts at module 2302 with synthesizing the
preceding functionality of FIGS. 20-22 to facilitate collaborative
discovery of innovation breakthroughs addressing considerable unmet
market needs.
[0188] In the example of FIG. 23, the flowchart 2300 continues to
module 2304 with isolating particularly attractive opportunities.
Opportunities can be attractive, for example, to disrupt current
platforms with new platforms or technologies having advantages in
cost over current platforms yet delivering satisfaction along
outcomes and jobs that are balanced with importance.
[0189] In the example of FIG. 23, the flowchart 2300 continues to
module 2306 with providing tools that compare similar jobs in
markets having similar outcomes, and sharing related
platform-enabling-technology-paradigms. The tools can look across
similar jobs in markets using either internal or external sources.
The same or related platform-enabling-technology-paradigms might
include, for example, technologies that are associated with
electronic storage media. This tool is useful to postulate
technology redeployment strategies and chart potential pathways of
technology-based disruption and new platform breakthroughs.
[0190] FIG. 24 depicts a conceptual diagram 2400 of an example of a
data structure that could be used with a USIMS having data entities
interconnected, and normalized by an Outcome Driven reference
model. Data and information included within the data structure is
defined as "data entities." In the example of FIG. 24, the diagram
2400 includes a customer jobs/outcomes data entity 2402, a
strategic market data entity 2404, a macroscopic market data entity
2406, a products data entity 2408, an innovation management data
entity 2410, a research and development (R&D) data entity 2412,
a sales entity 2414, a marketing communications data entity 2416,
and a financial data entity 2418. It should be noted that these
data entities are intended to serve as examples; there may be more
or fewer data entities. When reference is made to a data entity
that is not depicted in the example of FIG. 24, the data entity is
referred to as an other data entity 2420.
[0191] The diagram 2400 illustrates, by way of example but not
limitation, a "star schema," though some other applicable data
schema could be implemented in a computer-readable medium. Since
relational databases are more common and less expensive to
implement than multi-dimensional databases, it may be desirable to
implement multi-dimensional data in a relational database. The star
schema is one way to implement multi-dimensional data in a
relational database. Another reason to use a star schema is that
queries are relatively simple because only joins and conditions
involve a fact table and a single level of dimension tables.
[0192] A star schema is a data warehouse schema that typically
includes one or more fact tables and any number of dimension
tables. (An example implementation is illustrated in FIGS. 26A to
26F.) The fact tables hold data and dimension tables describe each
value of a dimension and can be joined to fact tables if desired.
Typically, dimension tables have a simple primary key, while fact
tables have a compound primary key comprising the aggregate of
relevant dimension keys.
[0193] In the example of FIG. 24, at the "hub" of the diagram 2400
is customer jobs/outcomes data entity 2402. The customer
jobs/outcomes data entity 2402 includes referential data pertaining
to jobs and outcomes that the rest of a system implementing the
techniques described in this paper can invoke. For example, the
customer jobs/outcomes data entity 2402 can include scored results
of ODI based market studies used in conducting business impact
analyses. The customer jobs/outcomes data entity 2402, and other
data entities described in this paper, can include data regions,
which are partitioned data areas (whether physical or through
electronic means) of an information system containing like types or
classes of data; data domains, which are data regions that share a
particular relationship and information design; data tables, which
are structured data entities containing actual data that have data
fields for searching and are populated with data records; ODI
translation tables where each applicable data region is
cross-referenced to, e.g., customer jobs and outcomes through
coefficients of estimated or measured satisfaction levels; and
other data structures that are sufficiently different from the
others so as to warrant their own class, such as may be the case
for machine code, and that are deemed necessary or desirable in
implementing a system in accordance with the techniques described
in this paper.
[0194] The customer jobs/outcomes data entity 2402 facilitates the
indexing, storage, retrieval, and analysis functions of a system
implementing the data structure. The outcome driven reference model
that normalizes a USMIS can be manifested in the Customer Jobs and
Outcomes 2402. Some examples of portions of the customer
jobs/outcomes data entity 2402 include: a customer profile region,
a customer jobs region, a customer outcomes region, price
sensitivity data tables, an ODI translation data region. It may be
noted that these portions of the customer jobs/outcomes data entity
2402 could themselves be referred to as data entities (in
accordance with the definition provided previously for the term
"data entity") or data sub-entities. The customer profile region
can include, for example, attributes such as customer ID, customer
demographic attributes, products the customer uses, the role of the
customer in a job and in a customer value chain, membership in
segments as defined by the outcome driven needs analysis process
and attitudes, and/or other applicable data sub-entities. The
customer jobs region can include, for example, attributes such as
customer ID, customer type, data including job map models and raw
or scored data from ODI studies, and/or other applicable data
sub-entities. The customer outcomes region can include, for
example, attributes such as customer ID, customer type, data
including job referenced outcome statements and raw or scored data
from ODI studies, and other applicable data sub-entities. The price
sensitivity region may include, for example, attributes such as
customer ID, customer type, data including job or outcome statement
referenced willingness-to-pay data from ODI studies, and/or other
applicable sub-entities. The ODI translation region may include,
for example, reference tables to relate customers into customer
types or segments, relate products of the company, its competitors,
and its pipeline of new product and other innovation or business
developments into jobs and outcomes, relate the relevance of sales
and marketing campaigns of the company and its competitors to jobs
and outcomes, and relate records in external data bases such as may
pertain to patents and new technologies, to jobs and outcomes,
and/or to enable other applicable tasks.
[0195] The strategic market data entity 2404 includes market data
pertaining to specific markets. For example, the strategic market
data entity 2404 can include a competitors data region, a new and
adjacent markets data region, a business development data region
(e.g., M&A landscape, partners, etc.), or other applicable data
sub-entities.
[0196] The macroscopic market data entity 2406 includes macroscopic
data pertaining to overall market size and composition. For
example, the macroscopic market data entity 2406 can include a
market segments data region, a market demographics data region, a
monetary market sizing data region, a customer data region
(including, e.g., a product ownership table, an acquisition
campaign table, a satisfaction survey table, etc.), or other
applicable data sub-entities.
[0197] The products data entity 2408 includes data pertaining to
products of competitors and, assuming the system is implemented at
a company with products, the company. For example, the products
data entity 2408 can include product tables, an NPD data region
(e.g., needs delivery concept testing impact studies, new product
solution tables, etc.), or other applicable data sub-entities.
[0198] The innovation management data entity 2410 includes data
pertaining to ideation and collaboration. For example, the
innovation management data entity 2410 can include an idea table,
needs delivery export/import tables for intra- and extra-enterprise
collaboration, or other applicable data sub-entities.
[0199] The R&D data entity 2412 includes data pertaining to
projects and initiatives. For example, the R&D data entity 2412
can include a project pipeline data region, an emerging
technologies data region, or other applicable data
sub-entities.
[0200] The sales data entity 2414 includes data pertaining sales
associated with competitors and, assuming the system is implemented
at a company with sales, the company. For example, the sales data
entity 2414 can include sales organization data tables, sales
campaign data tables, sales results data tables (e.g., for the
company and for competitors), or other applicable data
sub-entities.
[0201] The marketing communications data entity 2416 can include,
for example, marketing communication campaign data tables,
competitor campaign data tables, or other applicable data
sub-entities.
[0202] The financial data entity 2418 can include, for example,
pricing data tables, costs data tables, or other applicable data
sub-entities.
[0203] The other data entity 2420 can include just about anything
else, such as, by way of example but not limitation, a suppliers
data region.
[0204] FIG. 25 depicts an example of a system 2500. The system 2500
includes an innovation strategy creation engine 2502, an outcome
aggregation engine 2504, an opportunity identification engine 2506,
a market segmentation engine 2508, a target strategy definition
engine 2510, a pipeline prioritization engine 2512, a breakthrough
conceptualization engine 2514, an extraction/translation/load
engine 2516, a database administration engine 2518, an open
innovation and ideation engine 2520, a metadata engine 2522, and an
ODI transformation rules engine 2524. The engines are coupled to
one another in a known or convenient fashion.
[0205] Engines, as used in this paper, refer to computer-readable
media coupled to a processor. The computer-readable media have
data, including executable files, that the processor can use to
transform the data and create new data. In the example of FIG. 24,
the engines transform data and create new data using implemented
data structures, such as is described with reference to FIG. 2, and
implemented methods, such as are described with reference to FIGS.
3-23.
[0206] The detailed description discloses examples and techniques,
but it will be appreciated by those skilled in the relevant art
that modifications, permutations, and equivalents thereof are
within the scope of the teachings. It is therefore intended that
the following appended claims include all such modifications,
permutations, and equivalents. While certain aspects of the
invention are presented below in certain claim forms, the applicant
contemplates the various aspects of the invention in any number of
claim forms. For example, while only one aspect of the invention is
recited as a means-plus-function claim under 35 U.S.C sec. 112,
sixth paragraph, other aspects may likewise be embodied as a
means-plus-function claim, or in other forms, such as being
embodied in a computer-readable medium. (Any claims intended to be
treated under 35 U.S.C. .sctn.112, 6 will begin with the words
"means for", but use of the term "for" in any other context is not
intended to invoke treatment under 35 U.S.C. .sctn.112, 6.)
Accordingly, the applicant reserves the right to add additional
claims after filing the application to pursue such additional claim
forms for other aspects of the invention.
* * * * *