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Posts Tagged ‘Knowledge Engineering’

Open Source for Business Intelligence

January 29, 2011 Leave a comment

Gartner attributes the main challenge in deploying a Business Intelligence solution as the cost factor that is associated with the application products. With the minds of CIO’s focused on capex and opex optimization on one side of the balance but also needing to ensure that the competitive advantage grows. The economic downturn has in a way driven home the need to “Do more with less” across the Business Intelligence spectrum. The Business Intelligence workforce across the globe now is evolving into a lean, high yield, innovative, technology adopting, best practice mapping community. AMR Research says “The battleground for IT spending in 2010 is BI”. There certainly is a need for efficient churning out Business Intelligence and the parallel need to keep the cost of deployment and adoption under control. In the next posts we would look at the TCO of deploying Business Intelligence systems and services.

There has always been the existence of Open Source applications; the challenge however has been the flexibility of these applications complimenting the disparate blocks of a BI cycle. Even in the proprietary market there are only a few product companies that provide a high degree of flexibility, feature richness and an integrated solution stack. Looking at the Gartner’s MQ 2010, the way MicroStrategy has edged into the competitive ‘leaders’ landscape is note worthy, their huge advantage has remained their flexibility and comparative cost advantage in this fierce competitive landscape among Microsoft, IBM, Oracle, SAS, IB sharing the space here. Going back to Open Source, enterprises have always found it difficult to pursue adoption in this space owing to lack of skill, less management buy-in, security threats, lack of central governance, system integrations, application complexity, lack of support & professional services, etc.,

There however has been this trend in Open Source where micro bodies within enterprises have invested and integrated solution stacks to address the complexities. The pursuit of “enabling” open source BI is more rewarding than an early adoption of a proprietary system running into high ground. The ROI over this Open Source pursuit as personally experienced in setting up BI / KE systems for me has been quite a discovery. With Open Source BI applications maturing every day and as more enterprise drive towards this emerging arena, I take a look at the stack involved in BI / DW, tested and do prescribe few of the best options available as below. Adoption of these systems lowers your TCO and helps achieve vendor neutrality.

Jesu Valiant ~ enterprise.ke@csscorp.com

Jesu Valiant – 2011

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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

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

Enterprise Technical Support & Knowledge Engineering

Technical support has been a challenge to most of the enterprise product manufacturers, trying to keep pace with evolving technology to achieve greater spread and depth. With the focus on research and development along with evolving the standards, the key aspect of enterprise product support over Installations, Migrations, Integrations, Up-gradation across the product application spectrum across all industries remains a challenge.

Technical Support vendors / Solution Partners who emerge having setup their robust, certified and proven support model evolved by the depth of expertise they harbor over time;  function as a seamless & integrated arm as a technology & solutions partner of industry leaders across enterprise support services, carrier support, infrastructure management, applications management, networking / data / voice /video. These partners over their prolonged exposure to the varied communities, processes, practices of enterprise product manufacturers have evolved key support processes and frameworks.

Knowledge Engineering

The Process Frameworks over Knowledge engineering over enterprise support, the  Support 2.0 concept, Six – Sigma process adoption in high-end technology support,  are a few of the key critical client success factors driving market adoption of outsourced high-end technology support offering.

The Knowledge Engineering Framework that’s evolved over a decade of enterprise support experience has augmented the strength to deploy robust analytics – recommendation – solutioning models to address a complete host of evolving business challenges. This Knowledge engineering framework applied over certain key propositions has evolved astonishing results turning and tuning the tide.

With this new genre in approach, the Knowledge engineering process looks to optimize and contribute towards all the core functions of enterprise product manufacturers over Product / Design, Business / Strategies, Marketing / Sales and Operations & Delivery.

Jesu Valiant – 2010

Business Intelligence

August 7, 2009 1 comment

Business intelligence is the knowledge, skills, capacities, understanding, practices and relative technologies, applications that are used to understand and interpret markets, their behavior, business dynamics and context. Storing, analyzing, and providing access to this data helps enterprise users make better business decisions. This helps companies to make faster, smarter decisions, as well as increase revenue, build customer loyalty, streamline operations, improve risk management and even enable previously impossible business processes.

BI System

A BI System provides facilities to capture data and present it for analysis; data is made available in ‘Historical’, ‘Live’ and ‘Predictive’ forms. We can integrate the wholesome business performance management aspects involving Strategy, Balance Scorecard, Strategy Translation/Cascading them into operations and business intelligence culminating into one integrated business information system which can be used as a Decision Support System.

BI Growing in Importance

The amount of corporate data is doubling every 2-3 years
Barriers of entry (costs/technology) are being removed
Continued pressure on businesses to find efficiencies and new market opportunities, client expectations
More disparate data sources than ever before

BI Usage Statistics – Source: Forrester Research Inc., 2006 survey

BI Use by Small and Mid Market Companies
48% –  Using
10% –  Planning to implement solution in 2007.
40% –  Not using
Don’t know 2%

Jesu Valiant

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