Inside Analysis

Evolving Business Intelligence Ecosystems

The world of business has astounded leaders, followers and people in general with its influence over the markets and its associated behaviors. There have been several interesting occurrences that created a unique combination of events, which will dictate the next generation of web development and usage. The following is a list of significant trends that are continuing to evolve:

  • The free-ware market. The long-standing “crowd-source” driven innovation market is continuing to spur the next generation of creativity and innovation across markets.
  • The open-source innovation model of designing thought and creativity interspersed with depth of colors and a range of emotions.
  • The extensibility effect of drawing inspirations from outside and inside the circle of people, process and technology.
  • The next-generation effect. The ability to transparently imbibe the next-generation impact on thoughts and colors.

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The mega-trends that we are describing have been around for a long time and have become very attractive recently due to advances in technology and the availability of resources in the following areas:

  • Mobile infrastructure
  • Mobile devices
  • Web 2.0 business models
  • Social media
  • Search appliances
  • Location-based business intelligence

What are the drivers that got us to this point? Change is the only constant in the whole process. There are minimal changes that can lead to massive shifts in the cultural landscape, and massive changes that can lead to minimal shifts in the cultural landscape.  From an individual perspective, consumers discovered one of the benefits of the Internet was the existence of a virtual world where they could form communities based on common interest. This led to conversations that resulted in trust in the ability of the community to solve a problem or a set of problems.

Creating a community to solve a set of problems or ideate on solving a set of problems caught on like never before and has permeated the industry. The following are a few benefits that communities have created:

  • Longer Tail – In terms of marketing and sales, businesses started experiencing a longer and sustained tail of revenue and growth when they were able to market to a larger list of customers or prospects while selling at a lower price point. In other words, the higher volume of buyers at a lower price point provided better revenue. A great example of this is the use of social media in Presidential elections in the U.S. in 2008 and 2012 by President Obama’s campaign.
  • Communities of Interest – With the formation of communities, businesses identify potential opportunities for involving the leaders and trendsetters in these communities to become their brand ambassadors and provide an irreplaceable word-of-mouth marketing for their products and services. Business actively supports these initiatives and some have even opened innovation portals for the consumers to contribute to idea creation and thought design.
  • Gamification  – Another popular way for businesses to foster innovation and create trust within their communities is the gamification strategy where leaders are often rewarded for their participation and contributions.
  • Crowd-Sourced Innovation – By inviting an open community of scientists and engineers to solve a problem for prize money, the crowd-sourced innovation model has given birth to a generation of thinkers, the size and breadth of which has never before existed. For a few hours a day, anyone can offer innovative solutions in a common marketplace. Sometimes these solutions become trendsetters in their own right.

While these trends and the associated models of engaging in business are becoming clear, we have two significant problems that need to be understood

  • There are a lot of different types of data available for consumption by business today.
  • Today, much of the data cannot be derived from the business layers directly and needs processing.

This leads us to the critical question – “Big Data and Innovation – Why Bother?”

Let us look at the history of innovation and the pace of the delivery. While we have been delivering innovation since the beginning of time, until we discovered electronics and deployed the first generation of computers, the innovations occurred slowly. The two primary challenges to progress are the speed at which we can mass produce volumes of data and the cost of the data that was being produced. Today’s platforms are designed and built for handling scalability problems for search engines and social media platforms and provide the computing and storage needs for enterprise computing and processing for large, multiformat and multistructured datasets. The extensibility of these new platforms into the enterprise data repository is a “game changer” for many. Now you can access all the data needed for making informed business decisions, and this creates the perfect platform for innovation in new business models and revenue opportunities within the enterprise.

The overall availability of data allows knowledge workers to start creating a business scenario as an “experiment.” These tests let you experiment with creating the right segmentation strategy for your customer, the right market for your product or the right cross-sell strategy for your call center by selecting small to midsize sets of data. The biggest transformation that can be brought to bear is the overall approach of the business to its prospect or customer. Instead of asking about the “lifetime value of the customer” or “the profitability segment of the prospect,” now the question has shifted to “what is my value as a business to the customer or prospect?” This type of introspection provides the business with opportunities to adapt to different types of customers and offer personalized levels of marketing and services and directly increases the revenue from such an engagement.

As we look into this world of data and start examining the processes and workflow complexities, we can see that several steps are codified and implemented in trivial details, where the process complexity cannot be reduced or eliminated.

We are scratching the surface when it comes to implementing and monetizing big data. This is just the beginning and the possibilities are infinite. Big data definitely enables innovation and provides a scalable platform to create multiple successful strategies from one statistical model or one experiment. At the end of the day, the biggest risk is not doing anything. Remember the bottom line – people are the biggest success factors, both from a corporate and business-user perspective, for innovation to happen and thrive in the business.

As you read this article, remember the actions and outcomes need to be taken in small segments and be incremental in nature.  The next steps that people will take to go forward include the planning of data sources, the collection of data, the integration of steps to link the disparate sources of data, the metadata collection across the data parts, the metrics and lineage of the data. Once you get past these steps, you need to start modeling the data and integrate the results into dashboards.

Krish Krishnan

About Krish Krishnan

Krish Krishnan is Principal at Sixth Sense and a recognized expert in the strategy, architecture and implementation of high performance data warehousing solutions. He is a visionary data warehouse thought leader and an independent analyst, writing and speaking at industry leading conferences, user groups and trade publications. He co-authored a book with Bill Inmon entitled “Building The Unstructured Data Warehouse”
and has authored three eBooks, more than 75 articles, viewpoints and case studies on business intelligence, data warehousing, data warehouse appliances and architectures.

Krish Krishnan

About Krish Krishnan

Krish Krishnan is Principal at Sixth Sense and a recognized expert in the strategy, architecture and implementation of high performance data warehousing solutions. He is a visionary data warehouse thought leader and an independent analyst, writing and speaking at industry leading conferences, user groups and trade publications. He co-authored a book with Bill Inmon entitled “Building The Unstructured Data Warehouse”
and has authored three eBooks, more than 75 articles, viewpoints and case studies on business intelligence, data warehousing, data warehouse appliances and architectures.

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