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Posts Tagged ‘Support 2.0’

Strategic Financial Decisions – The Makers & the Breakers

September 16, 2011 Leave a comment

Taking the Shylock route today’s organizations embark on a journey that can directly drag the organization to perilous and disastrous route into a future that offsets the goals and the mission of the Board. The financial charter by process design, rests with the decision of a very few; these few sometimes do not get the pulse of the organization; they constantly vibrate at a frequency that is completely off course the Board and the Business. With the changing trends in the corporate leadership and their approach to profitability, the only line items these leaders  see are bottom line cost reductions, spree of acquisitions & improper integrations, investing into sales & marketing efforts of decade old services, misplaced S&M focus over geographies and services. They are not to be blamed, without any depth in the service LOB of the organization and the technology vertical comprehension skills these decision and their makers drag the organization down and ensure it sputters to an halt like an old blocked rusted carburetor engine.

I want to discuss a few key examples of such perilous decisions and the decision makers that have devastated tall standing organizations. These decisions and the decision makers have collapsed structures and impeded progress in the name of growth and profitability; bringing to the fore their cleaving to traditional business structures and not adopt to the dynamic business environments governing the markets today.

Nortel Networks: Nortel struggled with a string of weak leadership team who huffed and puffed their way through mistake after mistake until Nortel eventually collapsed. They were burdened with billions of dollars in debt after missing out to raise equity and till they became a penny stock. Poor management and governance saw class-action lawsuits that cost it billions of dollars. Nortel made a series of multi-billion dollar acquisitions that were spectacular failures. Causes: Incorrect and out-of-line Financial Decisions by the handful of leaders that devastated the growth Nortel had achieved over the decades.

Air India: Foremost the Public holding of a Commercial enterprise does never survive, except for entities like ISRO where scientists and industry experts rule the roost. As for Air India and Indian Airlines the decision on not to privatize proves costly. The Indian state with its plural and coalition driven political schema cannot in anyway manage to be commercial in the wake of massive industry competition. The rising debts over fuel, airport parking, salaries, maintenance, one does wonder the future course of the organization. Wages account for just 16% of total costs, so the scope for reducing losses through wage or employee reductions is quite small. Blunders aplenty thanks to the Union ministry like the; Air India – Indian merger, Not outsourcing of fringe and support work, severe misuse of perks, corruption driven losses from high places, No industry expert to run the show as bureaucrats and politicians CANNOT run an airline. The purchase of Boeing 777 and 787 Dream-liners far beyond the airlines need is another major factor in this. Recently reports indicate corruption in the sale of assets of the Organization. Causes: Well.. its political state leadership. What more can anyone expect?

The above listed instances clearly highlight the need to have a strategic leadership team that drives business performance and posses great acumen and wisdom. Having taken financial decisions based on traditional and primitive approaches do not help growth in organizations that operate in dynamic business environments, the need of the hour is to have a sane leadership that can rise up and adopt to the changes. In the name of Optimization – Penny Wise Pound Foolish. Whenever the corporate house starts building hurdles for business growth in any form with red tapes lined with laces preaching of optimization; its high time the second level leadership and mid management foresee the conclusion. In the above examples we see that in the dynamic business environments the decisions becoming debacles.

Jesu Valiant – 2011

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Developing Social Capital – An Approach

March 15, 2011 1 comment

Its all about building a soul into the spirit of the community, Social networks have been in existence for centuries; yes… its certainly true. Human communities settled and formed communities on river banks for thousands of years and were united as a social group with goals structured around food & hunting, shelter, security, mutual support, farming, etc., these communities grew big and formed distinct civilizations. In today’s world we try doing the same over a virtual platform, things can be so basic and simple provided right insight and approach is formulated.

The key in building social communities lies in the understanding the basic constituents. Every community and social grouping has fundamental units of functions [SFU] – Social fundamental unit,  addressing and enabling these units to function is key to the success of building community and social groups. Like in the real time communities there are some fundamentals that needs to be imbibed while forming communities.

1. Community Bonds among Individual elements.
Bonding within a community is very important, to create the bonding there needs to be goals defined and all in the community inadvertently pursue these set of goals. Its like floating a forum for users of one product series who want to achieve better product adoption and usage across as a group commune together. Some publish articles, some post questions, some answer, some pool in external references, etc., This is the key , build tools and work-flows around these goals to ensure that there is always an opportunity to debate, share, compliment, report, reference, etc., These help building relationships and bonding within a group. There is always Cohesion and Inclusion and no Confrontation or retributions.

2. Community Bonds between Groups of Elements.
Having these all out functional groups there is the next level of collaboration in the form of inter group bonding; in a community business support environment users will need each other strengths to collaborate for a solution. An example is a product buyer comes with an issue on the product compatibility with another brand over certain feature sets; he posts a question over the forum, over FB wall and possibly tweet too. Here there is an opportunity for this reported cause to get viral and many to see it for the good and for bad. There needs to be a response and this can be addressed by the Individual elements or Other Groups of Elements. There can be a technical support team that can respond; if an user reports an issue with much complexity like an interoperability then is not in the control of the support team, they can very well invoke the design & engineering team to advice and action. This is community supporting community, build bonds between groups of elements; this is also called as community support.

3. Platform as a Component.
With the complexities of real life engagements moving into a social application based interaction and responses, there needs to be a strong platform and governance model that helps in bridging the gaps between monitoring, responding, evaluating, enabling, analyzing etc., from a product manufacturer perspective. It also needs to take care of applying work-flows that will support knowledge sharing, collaboration, ideation and support, best practice mapping, etc., There are many a systems at play in the market there are a few that have listed below. These platforms offer the facility and functionalities to support most if not all of the above.

Social capital means the intensity, virality, capability of a Social community. The more the cohesive strength and collaborative spirit the higher the capital. Better Social capital translates to the following benefits for a Product & Technology Manufacturer.

1. Reduces transaction costs
2. Provides vivid and direct avenues for revenues
3. Provides a competitive edge
4. Social Capital with the best practices provides market differentiation
5. Social Capital with the right systems enable conducive collaboration environment
6. Helps build openness and transparency
7. Community Involvement Builds Social Capital
8. Promotes Accountability
9. Product and Logo Loyalty
10. Community Satisfaction & Delight
11. Direct channel with customers

Jesu Valiant – 2011

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

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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