PIPL - Practice Area Business Intelligence

STRICTLY CONFIDENTIAL © Copyright 2014 Progressive Intelligence

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Progressive Intelligence

Partners in Achievement

Knowledge ManagementUsing “Business Intelligence” for Insights

Trusted Advisory Services Trusted Advisory Services

Tapping, Moulding and Utilizing Knowledge Assets

Dr. Sanjeev B. AhujaManaging Director

[email protected]

2 STRICTLY CONFIDENTIAL © Copyright 2014 Progressive Intelligence

Overview

Practical challenges

Scope and Strategy

Compendium of KM technologies

Background

Knowledge and its management


Data and information sources abound in a market san s frontières• Businesses gain competitive edge with timely and informed decision-making

− Heightened competition and market vulnerability has made intelligent assimilation and interpretation of strategic and tactical data a mission-critical requirement

Knowledge workers in the labour force have taken on renewed import • There is growing recognition that know-how, experience, and practices are

arguably the most significant corporate asset that must not be left tacit; it only becomes tangible once characterised, captured, and made operative

− Differentiating low-value tasks of data capture and reporting, from high-value sophisticated processes that generate business intelligence, requires domain knowledge and creative data/information engineering

Background

Current state of playYears of incorrect and misleading claims that fed a desire to “dumb down”inherent complexities in representing and using knowledge have created a

firestorm of expectations; suppliers are over-promising and under-delivering.


The big deal around Big Data Analysis (BDA)• It is neither the ability to process large volumes nor the clever analytical

techniques that is the raison d’être for BDA; both are but means to an end − Its purpose is “intelligent” or simply, informed decision making based on relevant

data, its interpretation (at times with visualization aids) to draw useful inferences, and creation of other facts and valuable information that are otherwise “hidden”.

− Once potentially useful data sources have been identified, the next challenge is to work one’s way through a forest of options for technology platforms, to select one that offers a range of appropriate tools and techniques.

− It is only with aligning and integrating BDA within critical business decision making processes that its goals come to fruition. It delivers maximal value when it not only supports business as usual but rather, influences the way it is done.

Background

Businesses must clearly articulate their objectives from using data, analytics, and knowledge to make decisions, along with a strategic plan for realizing them

After wading through ubiquitous hype about the “big data” tsunami and technology platforms for surfing it, one still cannot eke out a clear “so what” from it.


What is knowledge?• Knowledge is the set of characteristics about data and information that

determine their use, role, interdependencies and usefulness− Data are (un)structured facts (e.g., documents & content; audio & video files, text

in emails, calendars, contacts, notes, chats, messages, SMS; social network exchanges; blogs; etc.) that are usually unorganized from a business perspective and which provide little information regarding patterns, contexts, etc.

− Information results from applying knowledge to data by consolidating, optimizing, categorizing, contextualizing, correlating and drawing meaningful inferences

− Knowledge is the ability to identify relevant data, recognize useful relationships between them, understand their implications, and then apply business know-how to use all of that for creating “insights” that the organization can act upon

Background

KnowledgeData

Actionable

“Insights”

Knowledge Management is a conundrum for most businessesGathering correct, consistent and comprehensive knowledge is handicapped by naïve optimism stemming from an under-estimation of the scope and complexity of automating knowledge based decision tasks traditionally performed by people.


Knowledge and its management

Definition of Knowledge Management (KM)KM is the systematic management of an organization's “knowledge” assets for the purpose of creating useful information to address its tactical and strategic goals.

“Knowledge Management” is often being used as a misn omer• Full scope of KM is not something that is universally accepted

− The term KM is generally used to mean, making the right information available to the right people, in the right context, to make the right decisions

− It must enable an organization to identify existing and generate new information, quickly retrieving and using it as and when required by the business

− It is expected that both knowledge and information assets remain current and correct, improve over time, and add to an organizations’ learning and growth

• For effective KM, an organization must develop a deep understanding of what constitutes knowledge and information for its business and customers

− It requires a priori notions about various forms that knowledge can take, different ways in which it can be accessed, shared, and combined, as well as when, where and how it can be applied for generating useful information or competitive insights


Articulation of suitable knowledge artefacts is at the foundation• Abstract facts , e.g., concepts of price, product, market and region, or of

competitors, promotion and opportunity, or world events that are important for a business, such as wars, famines, natural catastrophes, etc.

− Organized as a network of concepts with semantic relationships between them, into hierarchies with different levels of abstraction, or even as just a simple list

• Descriptions of know-how , e.g., data protection regulations and policies, economic models, local laws, or even basic principles of “if-then” logic, etc.

− In some contexts, descriptions of know-how might only be considered as data, to be further manipulated or interpreted, e.g., a list of outcomes from a Web search on legal proceedings that involved IP infringement or patent violation, etc.

Different knowledge is required for different tasks; all of it has to be managedKnowledge may be used to narrow the context when searching for relevant data, or to infer new facts from that data, or to present the data in a way that uncovers information which might otherwise not be obvious, and for a range of other tasks.



Searching data, information and document repositori es• Google search is an obvious benchmark for keyword based retrieval of

relevant data from the Web and even the desktop− With natural language processing functionality, auto-translation, thesauri, limited

semantic analysis, and use of “common sense” rules, Google search offers an effective engine for searching unstructured data and document repositories

Assimilating and interpreting retrieved data, infor mation and documents• Google-like search is not sufficient for addressing most business needs• A formalised knowledge base is crucial for systemic identification, generation

and interpretation of relevant, useful, and actionable insight

Although technical viability or computing power is becoming less of an issue, it is still only a rare company that has successfully integrated KM into its business

strategy for supporting day-to-day decision makingData of disparate types and formats, collected from different sources and at

different points in time, only become useful once filtered for relevance, subjected to selective analysis and contextually interpreted.



From data to business intelligence• Discovery of innate relationships between data through multi-dimensional

visualization of interesting patterns is becoming increasingly critical− Handling the exponential increase in data entering an enterprise from mobile

devices, social media, public internet, private repositories, etc., managing the range of data types, static/dynamic, structured/unstructured, multimedia, etc., knowing which (if any) of that data is relevant, sharing timely information derived from it with staff and most critically, its selective dissemination externally, enables a business to combat competitive forces and secure a leading market position

Once categorized and integrated data can be searched, “mined”, interpreted and analysed, using effective presentation metaphors to communicate information

Selecting meaningful dimensions for data modelling, recognizing relevant relationships, and knowing useful patterns on seeing them is knowledge; its application results in information, only a small portion of it is real insight.




D&A“Big Data”

Business Intelligence

Discovery, Categorizationand Interpretation

Accurate, correct, defensible & repeatable

Growth and utilization for business needs

Governance

Information

Analytic models and visualization

Knowledge

Domain-specific semantics & rules

ActionableInsights

Access, usage, and dissemination

LifecycleManagement


CAUTION: "tread with care, open mind and due consid eration"• IT-driven, user-expressed, or supplier-hyped initiatives• Retrofitting business needs• Unplanned lifecycle costs: development, deployment and upkeep• Ignoring future requirements• Doing it yourself• Ignoring knowledge assets altogether

Knowledge Systems are becoming increasingly complexBasic capabilities of information delivery, analysis and integration now include geospatial intelligence, complex analytics, heterogeneous data sources, hybrid

data modelling, big-data content, data discovery, and business needs of governance, security and scalability



Corporate

Perspectives

Technology

Environment

Functional

Management

Organization

Processes

Organization

Culture

KM

Strategy

KM

Scope and strategy

What are some of the key considerations for a KM system?Several aspects can impact the value derived from KM: a) chosen application area, b) user expectations, c) return on investment, d) practical constraints of business environment, e) domain knowledge and f) in-house competencies.

Information

Knowledge

Data

Insights

Implicit

Explicit


Scope and strategy

Recommended approach to establishing a knowledge competency. Considered though not slow, staged but not overly extended, and robust as

opposed to experimental, characterises an optimal approach to maximizing value and minimising runaway costs from strategic knowledge initiatives.

Pilot Sandbox

Proofs-of-Concept

Outcomes: Insights across issue areas

Actions: Tailored to client and the problem

Processes: Repeatable, Automated

Operating Model: “Solution Centre”Data: Internal, Client, Licensed

Security: Inside/Outside of eco-system

Production

Goals: User Communities

Software: Open, Proprietary, Hybrid

Competencies: Internal, External

Deployment: Cloud, Hosted, Managed

Scope: Services, Operations, Infrastructure,

Technology, Location

Environment: Dev., Staging, Production

DC: In-house, Managed, Cloud

Config: Private, Cloud (SaaS, PaaS, IaaS)

Certification: Governed, Secure, Compliant


Data, knowledge, information and insight must be characterised, captured, formalised, made operational, and preserved with the help of business experts

Knowledge can be explicit, i.e., codified in data structures, dictionaries, thesauri, (chains of) relationships, meta-data, rules, meta-knowledge, documents, etc., or tacit, i.e., intuitive, contextual, best practice, experience based, embedded, etc.

InfrastructureAutomated/ManualIntegratingDefinition

Applications, Platforms, ToolsOn-Demand (goal driven) MiningType

ArchitectureSpontaneous (data driven)SearchingOrganization/Categorization

Storage, Backup and DRAnalysingProtection (authorization &

dissemination)

Replication & DistributionReasoningLC Management (create, share,

update, archive, purge)

Simulation & WorkflowVerification & Validation

Management SystemsPresentingHandling Size/Volume

TransportationVisualizingSourcing (internal/external)

Technology EnvironmentControl Mechanisms in

Applying Knowledge

Processing for “emergent”

insights (Inferences,

Decisions, Learning)

Essential Activities for Data,

Information, and Knowledge

Management

Scope and strategy


Scope and strategy

What must a KM Strategy address?It must take a long-term view on knowledge management and its role in business

decision making and operations support, defining which knowledge is relevant and which is not. Strategic investments must be made in enabling KM processes.

Performance

Management

Technology

Investments

Security,

Governance

& Policies

Operations

Processes

KM

Processes

Business

Processes

Core

Competencies

Organization

Design

KM

Strategy


Compendium of KM technologies

Analytics

KB

Decision

Support

Workflow

Simulation

Collaboration

Groupware

E-mail &

Messaging

Databases

Document

& Content

Mgmt

Social

Media

Intelligent

Search

Extranets &

Intranets

Access

Mechanisms

& Devices

Visualization

Technology

Environment

Info “Bots”Robotic automation with context based search, rule based

workflows, analytics (n-dimensional) and knowledge based

decision support for correct, coherent and consistent

results.

“Emergent” InsightsPresentation, data

modelling, graphical metaphors and

interactive functions for visualization and

discovery.

Data/Info StoresPlatforms for

collaboration and groupware, social media, messaging, e-mails, documents, multimedia content, data/info sources.

Data Flow & SearchMiddleware for

device independent access over public, shared and private

networks, with intelligent search

engines to hone-in on what is relevant.


Credentials - Dr. Sanjeev B. Ahuja• CxO tenures at start-up/early-stage, mid-size, and large-scale global firms

− Managing Partner of PICG, a strategy and operations management consultancy offering advisory, technology and delivery services with 18 senior professionals

− Founder, President & CEO of a € 35M firm with 150 staff delivering technology solutions and professional services in CRM and Business Intelligence

− Global CIO & VP Business Ops. of a $4.3M mobile satellite communications company, managing its global billing and customer care operations, strategic partnerships and P&L responsibility for a shared services centre

− Prof. and Director of Graduate Studies at University of Maryland (USA); author of numerous articles and served on programme committees of Int’l conferences

− PhD (1985) & MS (1981) - Artificial Intelligence; BSc (1978) - Elec. Engineering

Trusted Advice, Hands-on Experience, Practical ApplicationPI Consulting Group (PICG) partners bring over 30 years of strategic problem

solving and implementation experience, with independent advisory, programme governance and leadership development services in business operations,

Telecoms & IT, information and knowledge management.

Progressive Intelligence, Ltd.


Progressive Intelligence, Ltd.

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PIPL - Practice Area Business Intelligence