Key to bridging the analytics-software chasm: iterative approach + customized solutions, leading to self-service BI

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The world of software development and IT services have operated through well-defined requirements, scope and outcomes. 25 years of experience in software development have enabled IT services companies to significantly learn and achieve higher maturity. There are enough patterns and standards that one can leverage in-order to avoid scope-creep and make on-time delivery and quality a reality. This world has a fair order.

It is quite contrary to the Analytics world we operate in. Analytics as an industry itself is a relatively new kid on the block. Analytical outcomes are usually insights generated from historical data viz. a viz. descriptive and inquisitive analysis. With the advent of machine learning, the focus is gradually shifting towards predictive analytics and prescriptive analysis. What usually takes months or weeks in software development usually takes just days in the Analytics world. At best, this chaotic world posits the need for continuous experimentations.

The question enterprises need to ask is “how to leverage the best of both worlds to achieve the desired outcomes?”, “how do we bridge this analytics-software chasm?”

The answers require a fundamental shift in perception and approach towards problem solving and solution building. The time to move from what is generally a PPTware (in the world of analytics) to dashboards and furthermore a robust machine learning platform for predictive and prescriptive analyses needs to be as short as possible. The market is already moving towards this said purpose in the following ways:

  1. Data Lakes – These are on-premise and built mostly with the amalgamation of open source technologies and existing COST software’s – homegrown approach that provides single unified platform for rapid experimentation on data along with capability to move quickly towards scaled solutions
  2. Data Cafes / Hubs – Cloud-based SAAS-based approach that allows everything from data consolidation, analysis to visualizations
  3. Custom niche solutions that serve specific purpose

Over a series of blogs, we will explore the above approaches in detail. These blogs will give you an understanding of how integrated and interoperable systems rapidly allow you to take your experiments towards scaled solutions, in matter of days and in a collaborative manner.

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