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Edge Business Solutions Framework

October 28, 2012 Leave a comment

EDGE has been structured to handle specific enterprise workflows addressing access control, content management, intellectual property and security requirements. EDGE 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.

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 model has been universally recognized. However, the implications for enterprise adoption suffer from a lack of immediate consensus.

Jesu Valiant – The Major Service Industry Trends

With the above trends shaping the services industry; there is a need to address the ever-increasing gap with a solution from a technological and logical perspective that would stand to evolve with the dynamics of the service industry. We tend to get attracted to terminologies in the services industry ‘use case’ like KCS, Social Media, Collaboration, Analytics, etc., forgetting that these idea bundles do not have a tested platform with observable data that can be adapted in similar environments, providing the same yield. With such a dearth of proven solution models; here I am looking at replicating the models of successes and to present the entire solution in a concise logical framework for global comprehension.

Introducing the Edge Business Solutions framework; I address the technology components, logical functions, ongoing support and evolution to the ‘services’ business dynamics. This is a working  solution model adapted by a few organizations; there is a mapped platform, defined process set, metrics & measurements and a visible ROI.

The Edge Solution Framework

Jesu Valiant_Edge_Framework

Edge new market solutions are designed to address a wide range of challenges and help users increase performance levels and streamline operations. Lean is in Edge Solutions DNA, this DNA ensures that we structure and deploy specific enterprise workflows addressing access control, content management, intellectual property and security requirements. EDGE framework enables the enterprise to harness the power of BPA-BPM and Analytics like never before; having a direct bearing on ROI. Solutions help drive customer loyalty, drive adoption and better product management, as well as provide collaborative environments for promoting ideas that drive continuous innovation. Edge Solutions have helped transform the way we look at Business Process Management and the possible implications of collective, best practice models.

Key Solutions:

  • Knowledge Systems
  • Data Collection & Capture
  • Online Collaboration
  • Learning Management
  • Social Media Solutions
  • Document Management
  • Enterprise Wiki
  • Enterprise Content Management
  • Analytics + Reporting Dashboards
  • Scorecards & KPI Management
  • Self help / DIY Solutions
  • Survey Management
  • Process Tracking & Scheduling

The range of Edge solutions and services are powered by a native Edge applications framework also named Edge; structured to handle three challenges: the intensive deadlines, the stringent checklist of flexibility & security, and a predefined set of functional classes written by experienced Web developers. Edge application framework lets us build high-performing, thoroughly flexible, deeply secure, elegantly designed, Web applications quickly.

Edge web application framework is a high level PHP framework that encourages rapid, expandable,  inter-operable, web applications. There are predefined modules addressing core functions of workflow, charting engines, access controls and the likes that augment any build and deploy scenario rather quickly.

For more information please feel free to reach to me at my business email address at jesu.valiant@csscorp.com.

Copyrights – Jesu Valiant 2012

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Service [TAC] oriented Knowledge Engineering – Framework & Process

January 27, 2012 Leave a comment

Knowledge Engineering for Services [TAC]

All the data that is being gathered over every possible transactions reside in the databases. These transactional data amassed over time has the potential to yield a lot of intelligence and provide key insight into the major four areas of Business.  [a]-Product Analytics, [b]-Customer Analytics, [c]-Process Analytics.

Technology manufacturers need to evolve their strategy every time and stay competitive, they desperately need to understand the perspectives from a VOC – Voice of Customer.

TAC Centric Knowledge Engineering will make use of a varied a host of ‘technology and product BI companies’ and ‘BI as a service companies’.
There are three types of BI as a service offerings:
1. Generic BI platform capabilities (for example, online analytical processing [OLAP], reporting, analysis, data mining)
2. Application-specific offerings (for example, Web analytics, fraud analysis, risk analysis, benchmark analysis)
3. Combination of both application BI services & product specific analytics with a expert recommendations and consulting  [KE].

Knowledge Engineering will supply the all essential human expertise to build the knowledge analytics architecture based on the business case / SWOT and  conclude recommendations.

From a Services Industry perspective; I recommend pursuits be set over three specific areas. 1. Customer Analytics, 2. Product Analytics, 3.Process Analytics. This can help accelerate service excellence and optimize operations.

Product Analytics: Solutions that allow them to analyze across a series of product performance dimensions ‘end to end’ in the product’s lifecycle. Analytics over Product reliability, third party environments, bug impacts, causes to resolutions, escalations and factors, time to resolve, and maintainability requirements, while all the time focusing on lowering support lifecycle. Product analytics will bend the traditional value chain into a “feedback loop”; evolving into product intelligence.

Customer Analytics: Customer intelligence visibility, customer satisfaction RCA & recommendations, customers and prospects, customers’ likes and dislikes, cases history and trend, as well as future wants and needs, by consolidating customer information currently in multiple silos and mining information.

Process Analytics: Business Process Analytics provides drill-down and slice and dice capabilities from various perspectives for extensive process analysis and reporting. Derivations of general and specialist advisory based on analytics rendered over  historic and real-time data.

Knowledge Engineering Framework:

JesuValiant_KnowledgeEngineering_Framework

JesuValiant_KnowledgeEngineeringFramework

Knowledge Engineering – What it takes?
•    Enumerate, analyze, catalog, and suggest improvements to the core and support processes of the business unit.
•    Ability to assimilate and correlate disconnected and and articulate their collective relevance.
•    The ability to visualize and create high-level models to extend and mature the business architecture.
•    Technical knowledge over technologies covered in the product stack.
•    Importing, cleaning, transforming, validating or modeling data with the purpose of understanding or making conclusions from the data for decision making purposes.
•    Presenting data in charts, graphs, tables, designing and developing relational databases for collecting data.
•    Information management, relational database design and development, business intelligence, data mining or statistics.
•    Utilizes data analysis techniques or best practices and draw inferences and present comprehensive analysis.
•    Critically evaluate information gathered from multiple sources, reconcile conflicts, Decompose high-level information into details, abstract up from low-level information to a general understanding.
•    Prepare reports of findings, illustrating data graphically and translating complex findings into written text.

Do you have a pain point today and are you a technology product manufacturer? Reach out to me @ jesu.valiant@csscorp.com.

Thanks for reading.

Copyrights – Jesu Valiant 2012

*Logos in the Framework diagram belong to the respective owners. Here its to highlight and recommend adoption of these systems and tools to perform 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

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

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

Knowledge Engineering – An Introduction

August 6, 2009 Leave a comment

In simplest terms, knowledge is the ability of an actor to respond to a body of facts and principles accumulated over a period of time. One way to look at knowledge is as the apogee of the following continuum – data > information > knowledge.

Data=1 unit of fact; information=aggregation of data; knowledge=potential for action on information.

Data and information have intrinsic properties, the quality of knowledge depends on the properties of the agent.

There is no universal definition for knowledge management. At its broadest, KM is the ‘process through which organizations generate value from intellectual and knowledge based assets’. There are two types of knowledge assets –

1. Explicit or formal assets like copyrights, patents, templates, publications, reports, archives, etc.

2. Tacit or informal assets that are rooted in human experience and include personal belief, perspective, and values.

It is important to manage knowledge assets because –

Organizations compete increasingly on the base of knowledge (the only sustainable competitive advantage, according to some). Most of our work is information based (and often immersed in a computing environment). Our products, services, and environment are more complex than ever before. Workforces are increasingly unstable leading to escalating demands for knowledge replacement/acquisition.

KE

Jesu Valiant

Categories: Knowledge Engineering
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