Post on 28-Nov-2014
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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
sanjeev.ahuja@piplinc.com
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Overview
Practical challenges
Scope and Strategy
Compendium of KM technologies
Background
Knowledge and its management
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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.
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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.
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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
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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.
Practical challenges
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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.
Practical challenges
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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.
Practical challenges
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Practical challenges
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
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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
Practical challenges
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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
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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
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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
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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.
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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.
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Progressive Intelligence, Ltd.