The new paradigm of Business Intelligence

We have seen since many years how traditional Business Intelligence solutions have served industries for better decision making, especially the growth of analytics, both from the analysis and visualization point of view. Let us ask an honest question to ourselves: “Is this is enough in today’s business context across industries?” The answer is that it may be helpful, but only to a certain extent. The traditional enterprise data warehouse can store transactional information captured from multiple systems, including Excel/flat files that have some structure/pattern. An analytical model can be created with or without an OLAP layer to provide rich analytics in terms of dashboards and reports. Mining models can be created and integrated with  a data warehouse or OLAP layer to build predictive analytics. At the end, the target audience should be able to find out ‘What’, ‘When’, ‘How’, ‘Where’ and ‘What may (will)’ with respect to their business.

The Independent Software Vendors (ISVs) belonging to various industries have built their products and solutions along with Business Intelligence modules to provide better insights of the business processes. However, these insights are limited to the information that has been captured through the software that is structured.

The giant leap of information technology in terms of storing vast amounts of non-transactional, unstructured business data and capturing it from disparate sources has paved the way for a new generation of Business Intelligence technologies. The usage of ‘Hadoop’ as a Big Data platform either in addition to enterprise data warehouse or as substitute has started to rise. The key drivers for this paradigm shift are:

  1. New Business Insights (e.g. The Internet of Things)
  2. New Technologies
  3. Reduced Costs

The new technologies help in ‘Enhanced Data Management’, ‘New Deployment Options’ and ‘Advanced Analytics’ for ISVs to make their software products suitable for today’s business needs. This is the game changer for Independent Software Vendors (ISVs). Usually, in the Healthcare, Retail and e-commerce space, the applications have progressed by leaps and bounds. A few of the top 5 use cases which can be applicable for ISVs across industries are as below:

  1. Optimize Funnel conversionby achieving more growth in sales of product/service with lowered costs
  2. Behavioral analyticsto help analyze customer/employee behavior and customize offerings to maximize  gains
  3. Customer segmentationfor better targeting and reach of the right products/services at the right place and the right time. This will be more accurate than first generation BI as the results will be in combination with macro environmental non-transactional data associated with the transactional data captured through traditional source systems
  4. Predictive analyticsfor better planning and forecasting. The results of predictive models are more accurate when we feed a larger volume and a variety of data in contrast to the first generation of Business Intelligence technologies
  5. Market Basket Analysisto better bundle the service offerings to the right set of the target audience
  6. Predict security threatsby analyzing  historical breaches and prepare better for the future
  7. Fraud detectionto identify possible fraudulent activity beforehand and minimize losses

In this new era of SMAC, the data is generated through either sensors, systems, machines, mobiles, web logs or interactions through social media. This data is unstructured, voluminous, varied and yet critical when combined & analyzed with structured data captured through traditional systems. Now let us see and understand how each area of Business Intelligence has undergone a  transformation:

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If we try to put all these pieces together, the new eco system of Business Intelligence looks like:

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Let us detail the highlighted boxes:

Data Refinery

  • Ingests raw detailed data in batches and/or real-time into a managed data store
  • Distills the data into useful information and distributes results to other systems
  • Key use of Hadoop today

Computing Platform

  • Used for exploring data and developing new analyses and analytic models
  • May also be used for prototyping new analytics-driven LOB applications and for temporary analytic solutions
  • May employ an RDBMS or Hadoop
  • Enables users to blend new types of data with existing information to discover ways of improving business processes
  • Allows users to experiment with different types of data and analytics before committing to a particular solution
  • May employ a RDBMS or Hadoop-based solution running on premises or in the cloud – Hadoop is especially well-suited to processing large amounts of multi-structured data
  • Represents a shift in the way organizations build analytic solutions:
    1. Increases flexibility and provides faster time to value because data does not have to be modeled or integrated into an EDW before it can be analyzed
    2. Extends traditional business decision making with solutions that increase the use and business value of analytics throughout the enterprise

This way the paradigm shift has taken place in Business Intelligence and is creating a dire need for ISVs to modernize, build their software products and applications with above capabilities. The ISVs that  have sensed this and have started implementing the change/modernizing their products are bound to have a competitive advantage.

The rise of Cloud and Disrupting the BI / DW ecosystem

Business Intelligence analytics tools and BI analytics are ushering in a new age of business. Combined with mobile BI and open source BI tools, the BI market will surely surge ahead.

The ISVs have already realized the potential of cloud, and moved their software and applications there by offering SaaS based services to the customers. The next big question in their mind is: “Can a Business Intelligence module can be moved there?” This has its own advantages and apparent challenges that can be addressed. This has given rise to ‘AaaS – Analytics As a Service’. We shall talk in detail about his in the next blog…

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