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Sunday, 14 June 2015

Legos and Transformers

Analytics is the buzzword within every industry.  The transformation in Analytics field over the past couple of years have been dramatic.  The focus has shifted from Reports to Diagnosis to predications.  Remember the days of Data Warehousing (jobs that run over night) to create specific dimension of data for reporting purposes.  Those days are slowly taking a back seat. Before proceeding any further, let us review the types of analytics.  Analytics at the outset, tries to address few basic questions regarding a particular event

What happened :  Description of the event.  Often referred to as Descriptive Analytics.  
Why it happened :  Reasoning.  Often referred as Diagnostic Analytics

Exactly how kids react to anything.  Their basic questions at all time.  What and Why.  In fact kids focus on the why more than the what.  A normal conversation with my 4 year old prompted me to write this.  

Me :  Son, you have to start studying
Son :  Why?
Me :  When you grow up you can make Money
Son :  Why?
Me :  To buy things that you need
Son :  Like what
Me :  House, food, dress…..
Son :  Why?  I am already living in a house
Me :  This is my house.  You have to get one for your own
Son :  Why?  There are 4 rooms and i have my own bedroom.

You get the point.  Further analytics have taken a deep dive in to Predictions and prescription.

When will it happen again :  Will it happen again, if so when.  Often referred to as Predictive analytics
What should I do :  If it happens or how to avoid it.  More of prescription.  Often referred to as Prescriptive Analytics.

In the current environment of booming analytics, how can organizations truly succeed with leveraging analytics.  I read a good article on Gartner's take on Business Intelligence and Analytics.  Gartner talks about 2 sides of Analytics - The Dull Side and the Dark side.  

Dull Side :  Requirements and IT driven analytics.  Traditional way of doing analytics.  Business may not get what they need at the right time and there is always a time lapse due to requirements collection, development …. Hence the dull

Dark side :  With the rise in IT consumerization, Business can do their own analytics with their own data and tools without the knowledge of others.  This is going in to dark side.  

Clearly these are 2 extremes and neither of which can truly benefit the organization in today's dynamic environment.  The reasons for above extremes of working is driven by Volatile requirements and need for personalization.  

Volatile Requirements :  Business requirements are always changing.  It is even fair to say there is no requirement and everything is a requirement.  In the upstream environment, today they might be interested in Low producing wells, tomorrow, Wells with Category 1 integrity issues, day 3 something else.  It keeps changing depending on what they like to address.  Business is wanting a solution as quickly as the requirements changes. No time for requirement document and IT driven processes.

Personalization :  Everyone of them have their own way of doing analysis and would like to personalize for their need and ease.  A common implementation with standardized set of reports and analytics will drive their efficiencies and thought process out.  Hence business prefers the dark side.  

So what is the Solution.  Here is where Kids help us understand the business better. One day the kid wants a Train toy, the next day a car, and the list goes on.  The volatility of the requirements.  Once they have a toy, they want to change the shape of the toy, twist and turn.  Move few pieces across.  Personalization. 

The toys that satisfy them are Legos and Transformers.  Kids can build what they want and personalize the way they want.  So in short treat your business like Kids.

From an architectural and implementation purposes,

1. Create an Data Management engine / Platform.  Architect and design your Data Management platform to be flexible and scalable.   
2. Provide ability for the users to pick and chose the data through a open data model / Services from the Data management Platform
3. Keep the consuming applications open - range from spreadsheets to Statistical / Data science tools

4. Have a sustainable organization to manage Data (Quality and Availability) and Data Management Platform

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