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Posts Tagged ‘Business Analytics’

Social Media for the Enterprise – Strategies for Maximum ROI

Social Media dynamism, science and working is unique and can be adopted to Enterprises that have their own community, they can on-board existing traditional engagement channels into Social media and offer an unique customer experience. There are always huge benefits in closing out a successful Social Media channel for enterprises. Simply the Science of Social Media can allow a definitive and a sea of change for enterprises who will want to pursue new ways of global outreach and engagement of customer communities.

There are frameworks, methodologies, practices and process systems created to add tremendous value to enterprises seeking to deploy Social media strategies, With the application of Social Media strategies and by incorporating the best practices honed; enterprises can now pursue providing strong value to their engagement models by providing the need driven, value based solutions over Social media. Enterprises have a list of priorities that they should achieve by deploying Social Media platforms; these priorities are scenarios in which they get the maximum return over investment. These scenarios are the traditional engagement methods that are packaged and deployed over social media platforms; with a global community and outreach like never before. Have listed below few of the direct, maximum yield Social Media pursuits that I could research and list.

Investment and Maximum ROI Scenarios.

Enterprise Collaboration Enablement
Enterprise Support Enablement
Social Media Monitoring & Engagement
Social Media Sales Enablement
Web Collaboration & Intelligence

For detailed insight, visit my other Blog

Jesu Valiant – 2011

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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

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

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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