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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.

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CRM’s in 2011 – What the Market needs?

April 14, 2011 4 comments

CRM applications have traditionally helped in issue tracking, tasks management, accounts management and have all their product feature appreciations over the decade. All such enhancements have addressed feature extensions and few rare cases of interops with other applications. In today’s scenario the CRM features are being looked at from multiple perspectives with the advent of newer engagement models and transaction channels. The market does have its share of feature packed systems but features have only looked at addressing the legacy use cases and have not looked at widening the visions over next generation market needs and emerging solution areas. With a late awakening we now see an activity spree in the market that does signal that product companies are trying to make the most, by way of new pursuits and with a load of investments.

Here we take a look at ‘What the market needs today?’, the product stacks prescribed by analysts and how they hold good against the needs.

Gartner in its MQ for 2010 lists the following set of product companies.

Leaders & Challengers per GMQ 2010
-Oracle Siebel
-Salesforce
-Microsoft Dynamics
-RightNow
-Pega Systems
-SAP
-Amdocs

Lets see what are the overall Gaps that are abound in these products with regards to the realities and engagements of today and tomorrow.

1. Integrated E-Support:
With high levels of adoption over self service modules and real-time on-line support systems growing in demand, many CRM’s in the market have not the ability to provide an integrated E-Support Module. E-Support will be the set of functions or features that will help in rendering real time support that will be part of the CRM, i.e., an extension. The functionalities does not end with ‘Incident Tracking’, ‘Data Captures’ and ‘Account details’ but goes on to provide a real time engagement console with the transactions being recorded. Features like ‘Create my Case’, ‘Chat now’, ‘Click to Call’, etc are opportunities to be pursued by CRM companies. There however has been few market leaders who have built capabilities on these lines but much of the market needs here remains to be addressed.

2. Process Automations & Management:
Every Product TAC, Sales & Marketing Teams, Engineering & Design Teams, and almost all the functional arms of any product company plays host to a wide variety of processes and procedures around the tracked CRM entries. There are numerous processes that are covered manually with the reports that are extracted from the CRM databases. CRM here typically functions as a storehouse of data and performs case captures, beyond that all processes are manually managed by the varied functional units within and organization. They have Excel based macros that process data outside CRM, they have audit screens done over third party applications, they utilize web based tools to automate other process and work-flows complimenting the data capture done by the ‘expensive’ CRM.
Here is an opportunity to fill the Gap and make available process / work-flow management functionality as part of the CRM to position product as a market differentiator.

3. Social Web Interoperability:
The last couple of years have seen the emergence of Social media, with direct identification of the individual profiles and engagements globally, this new medium demanded immediate attention from all of the enterprises and corporations. In the wake of Facebook, Twitter, third party forums & wikis, Collaboration platforms, there was an immediate need to listen, manage, engage, analyze these data streams that are out there. The negative impact that can be caused due to mis-management of this medium with its virality over comments and posts posed a very high risk proposition. Many CRM’s today fall absolutely short of capabilities to address this widening gap, however there are a few market leaders who have in the past months frantically went on an Acquisition spree or a partnership spree to add this strength. Salesforce & Raidan over Service Cloud 3, Lithium acquisition of Scoutlabs are few of the attempts made to address this gap.

4. Social Analytics:
With much said and more done over Social media on providing tools capable of listening, capturing, monitoring, engaging of conversation over the Social Web and third party portals and forums, there emerges the strong need to analyze these in line with the few key business cases and identify, get insight,  pursue opportunities, [Social Media Analytics features]. This key feature set is not offered by many in the market. There are only a few product based solutions that currently have tried to build capability here, this area is yet to evolve and the critical aspect of providing Business Intelligence over Social Media is an open opportunity for all the CRM’s out there. With the complexities and lack of analytical cases to pursue and render value there is whole lot of ideation and product features that sprout here with no comprehensive solutions. Again a very strong opportunity for CRM product manufacturers to set their pursuit on.

5. Data Reporting & Analytics:
Much of the CRM’s data reporting revolves around volume driven reporting and rule engine based reporting. Across all the CRM’s that are being adopted there exists a huge gap over data analytics and advanced reporting. We have modules on CRM today that provide the advanced users the ability to query the backend data stacks to procure reports and a very low level slice and dice capability. This gap here to run Analytics and Engineer Insightful details is a Grey area. Although the reporting capabilities have matured, the gaps are huge and every CRM user community over all the product pursue a separate MIS and Data analyst functions outside the CRM process. The market will lap up opportunities here and this can be a huge market differentiator for CRM product companies if they address this glaring gap.

6. Integrated Knowledge Solutions:
Based on an analysis performed over enterprise technical support customers, I could see exponentially at least 15% of customers demanding DIY kits – Self help systems for them to drill down into the solutions. There is also the tremendous gap between the product and its relative documentation that can be ‘called upon’ even by agents who work on incidents and tickets. Both for the customer to assist in better adoption of service or product and for the engagement teams to arrive at a solution to help in better and quicker closure the backend process of Knowledge creation, management, references, knowledge pushing, archiving, etc., remains much to be desired even with the market leaders and the challengers. Solutions over this can be a tremendous value.

Jesu Valiant

Social Media Analytics – Top Applications

March 1, 2011 3 comments

Every organization and corporate house is participating in the Social Media; its come to a point where PR’s focus now are into Social Media. Social media opens a direct channel with the customers with their respective direct identities and hence more ownership and interactions and suddenly a new transaction channel with direct implications. Value of Social media channels now has made the most stiff-necked CEO’s bend down and deploy Social media strategists, this channel has truly emerged and maturing quite fast.

With the Social media becoming a business accelerator and Gartner identifying that Social Communications and Collaboration & Social Analytics as the Top 10 strategic technologies for 2011; the game in this space is heated and the market is lapping up every possible skill to make the most of this new front. With all the transactions and the buzz around the social web that is happening there is a an immediate need to monitor, record, analyze, model, gather intel over these transactions. The market now has hundreds of Social Media analysis companies rendering services and providing products, of these products there are a host of features on offing, only few however provide the comprehensive solutions.

At the first look every product looks similar and there is not much difference; when drilled down over product usage, product adoption, flexibility, rule sets, parameters, people & pages analysis, integrations, key social web performance indicators; there are only a few that surface to meet the demands. With not in any particular order the following are the ‘surfaced’ and recommended products.

Top Social Media Analytics Products:
Sysomos Heartbeat 2.0
Sysomos MAP
Edge3
Kontagent
Radian6 Dashboard
Radian6 Engagement Console
Alterian SM2
Attentio
DNA13
Fangle
Research.ly
Beevolve
Social Mention
CollectiveIntellect
ScoutLab
BrandsEye

Most Wanted Social Media Feature Lists:
Timeline Cohort Analysis
Funnel Analysis
Viral Optimizer & Analysis
Revenue Tracking
Traffic Source Optimizer & Analysis
Retention Workflows
Competitive Analysis
Geographic and Demographic Data
Key Influencer Identification
Key User / App / Page Engagement
Comprehensive and Spam-Free Database
Real-time feeds & captures of blogs, message boards and other types of unstructured data
Real-time analysis of blogs, message boards and other types of unstructured data
Comprehensive and constant web coverage
Social Media Metrics
Data Filtering and Segmentation
Workflow Management
Social CRM and Web Analytics Integration
Automated Sentiment Analysis
Historical Data
Listening Grid
Real-Time Coordination
Case Based Reporting
Complete Activity & Conversation History
Social Profiles
Integration and Interops Capability
Efficiency and Productivity

Jesu Valiant – 2011

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

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