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Posts Tagged ‘Knowledge Management’

Strategic Knowledge Engineering

November 15, 2010 Leave a comment

The nature of work has evolved towards service and knowledge related contexts; in this scenario we need intense focus over the processes and frameworks that would define on how we harness and leverage information that is generated over all processes for driving more value. Organizations seeking to extend themselves into this critical area of analytics needs to focus on shaping the process and the people; the vital components. Organizations should be aware of the characteristics of its relative business knowledge and its sources, features and usability. It needs to shape methods that can collate data from extensive source area, link disparate data sources for a collective sense. There needs to be a governing process that evaluates, merges, researches, develops,  the data and comes out with options for business optimization.

Splitting the stages into functional units tied down to specific goals.

1. Knowledge Sourcing
2. Knowledge Abstraction
3. Knowledge Framing
4. Knowledge Warehousing
5. Knowledge Engineering

Knowledge Sourcing can be described as the identifying and acquiring historic and real-time data that are available from varied sources in the annals of any given business process. Data usually is available in the databases, files, logs, documents and they hold information transactions. The data is accumulated over time and the stores swell with size; all the while presenting an opportunity to present intelligent or informative analysis that could drive a stack of benefits. Other than the normal reporting structures built to mark the progress over Production, Quality and all in between; specific insight is never sought. The inference – insight that can be extracted remains an opportunity and for long. This data stacks and repositories are analyzed and identified as channels / sources; this source is the feeder for the analytics and the outcomes, process of source identification and data acquisition needs to be clinical.

Knowledge Abstraction helps in framing the insights and is completely skill dependent; this human skill is at an expert level on the domain of choice [SME  – Subject Matter Expertise] and the process relies heavily on the understanding and knowledge of the people resources. The data set is categorized based on the business case or the problem statement, data and information framework built here are weighed and categorized in order to support the reasoning and outcomes. The frame is built over an objective where the entire pursuit of intelligence is architected. All information here is bridged, connectors, the domino effect and factoring are all part of this abstraction process. Abstraction has two distinct process loops; one

Knowledge Framing ensures that abstracted data is further developed and refined through higher process routines to achieve anchoring over statistical data. The anchoring is vital as this builds the entire exercise over reasoning, analytics and recommendations on numerical realities. There effort here is predominantly built over mining [drilling down] data blocks to identify patterns, strings, values  to build neural relation that will help garner deep insight into all the facets.

Knowledge Warehousing comes in as the vital next step where the structured information and knowledge is stored into prescribed data structures that acts as the foundation for all the processed data. These individual ‘marts’ contain data stacks that are structured  over certain perspectives. Here the data stacks are linked, merged, to evolve and position the data for all analytics and intelligence extracts. This warehousing of the structured knowledge is done using enterprise warehousing applications, there is also a process layer to help drive Data reporting based on rule engines and business case.

Knowledge Engineering Analytics is the function where we have all the analytics process and frameworks deployed to extract intelligence out of the data warehouse. There is a lot of factor building, dependency tracking, correlations exercises that are done over analytics suites that help understanding all the different perspectives from an analytics standpoint. Causes, factors, symptoms, diagnosis, recommendations, solutioning are all the indispensable next steps that add a tremendous value to business and business operations.

Jesu Valiant

Service & Analytics recommendations:
To initiate a SKE – Strategic Knowledge Engineering for any of your data stacks, there is a comprehensive solution stack shaped over a decade of analytics at the CSS Knowledge Engineering & Research Labs.

Email: enterprise.ke@csscorp.com

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Enterprise Collaboration & Knowledge Management

November 4, 2010 Leave a comment

In a focused [or] applied environment i.e., [domain specific and task intensive] where the core function revolves around a knowledge intensive processes, there is a strong need to invest efforts in Capturing, Processing, Leveraging knowledge. Our products, services, and environment are more complex than ever before. Workforces are increasingly unstable leading to escalating demands for knowledge sharing / consumption. Knowledge management best practices are evolved after continuous exposure to a whole assortment of challenges addressing varied communities and supporting the end to end ecosystem of businesses. With new genre frameworks and process controls engineered in-house, we do see an opportunity across the industry spectrum to build knowledge management process that addresses the knowledge workflows, knowledge structures, knowledge categorization, content management, content evolution, instructional design, and a whole host of process blocks. With a robust knowledge base and a matured process, businesses get the opportunity to evolve a strong benefit stack.

Global economy has migrated from an Industrial Economy [Commercial Products] to a Knowledge Economy [Expertise based economy]. With new services and expertise that are in high demand in the marketplace, any organization needs to cultivate within its employee base a practice of Knowledge Sharing and collaboration. Every organization needs a logical long term plan for the intellectual assets, people are skilled and they address it as a commodity when walking in for an interview. This valuable commodity needs to be captured, it can be from individuals, groups, domain teams, etc.,Knowledge Management practice attempts to create strategies to ‘source – classify – warehouse – analyze – leverage – reuse’ knowledge with communities. Knowledge management and collaboration completely depends on the community and hence community leaders within the organization are key to drive this practice. With rewards, recognition, learning, sharing, collaborating opportunities; we would have woken up to a new reality.

Successful collaboration and strong knowledge management structures are essential to any well-functioning business enterprise, and information technology has become one of its key enablers. For establishing and enabling collaboration within the layers of organization or community there are methods and process centric, application suites structured over web 2.0 standards pre configured to handle specific enterprise workflows addressing access control, content management, intellectual property and security requirements.

Accelerating journey towards Enterprise 2.0

> Integrated social media solution for businesses that enables organizations to build communities.
> Encourage Interactions, monitor reactions, take feedback and comments, process solutions.
> Promote Information Exchange from among communities or between organization layers & community.
> Knowledge accumulation and usage is a key to business success.
> Create thriving online spaces that deliver measurable value.
> Separate your company from the competition by giving yourself tremendous credibility.
> Presents a free, fair, open & transparent culture; helps gain value.

A Product recommendation:

CSS Corporation presented the EDGE collaboration and knowledge management system.

http://www.csscorp.com/news&events/news-read.php?NID=139

Email:
enterprise.ke@csscorp.com

Web 2.0 has transformed the way we look at online community behavior and the possible implications of collective, collaborative knowledge management models. The power of the Web 2.0 model has been universally recognized, however, the implications for enterprise adoption suffer from a lack of immediate consensus. The CSS EDGE platform creates a working model for transforming the enterprise support function by providing a governance model for integrating internal and external communities or groups.

EDGE has been architected using open source components, configured to handle specific enterprise workflows and enables the enterprise to harness the power of Web 2.0 to drive customer loyalty, drive marketing and product management, as well as provide collaborative environments for promoting ideas that drive continuous innovation.

Jesu Valiant

Knowledge Engineering & Business Intelligence

October 30, 2010 Leave a comment

We say “Knowledge Engineering”; we then speak about “Business Intelligence”. Here are the Megladons, Great Whites and what’s in between!

Knowledge Engineering (KE) is an engineering discipline that involves integrating human knowledge into systems, frameworks, methods and processes in order to solve complex problems normally requiring a high level of human expertise. Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data. BI technologies provide historical, current, and predictive views of business operations.

With complex data sets businesses keep producing over transactions of varied kind and their kindred; BI at the first layer and KE at the next layer provide complex intelligence, decision support from the data sets. Knowledge Engineering is the collective stack of ‘Expertise + BI + Research’ which results in recommendations and solutions for evolving businesses from a 360° Perspective.
There is this deep need to research and develop process and systems to extract data from varied t’eco-systems, store, tune for building dominoes, and present data structures for expert human intervention from a SME perspective. EHI enables to connect and overlay these mined>domino-ed data sets with a solution stack driving complex decision support, savior strategies, threat negations, process frameworks that redefines and edifies. BI technically has a lot to contribute especially through its native adoption of Warehousing and Analytics systems and processes. This warehousing and analytics does augment research efforts for the EHI tasks; “What is” is what is needed in varies perspectives in comprehending the data stack and this the prelude to KE. How the data stack is structured involves EHI to identify the sources and to build relativity.

There is no dearth for systems and process available in the market today; from solutions and services ‘dime-a-dozen’ to what the market leaders have in offering the lack-the gap-the missing link has always been EHI processes with its diagnostic, anatomic, molecular specifics to structure business evolutions. Be it a profit Kalashnikov of a corporation or a loss attracting black hole enterprise, investments over BI cannot be complete if you are not adopting Knowledge Engineering practices. The need… well we have them already and one tailored KE framework will certainly nuke rigidity and blow away the comprehension veils.

– Jesu Valiant

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