Learning Big Data & analytics

I started to learn about Big Data & analytics and writing a few blogs to track my own learnings in future.

Big Data simply refers to massive amounts of information that, when combined, exceeds limits of traditional technologies and techniques to make the data useful. Currently businesses face significant challenges when it comes to V3true integration and collaboration. That is a huge missed opportunity.There are three “Vs”.

Volume  refers to the number of data sources and the sheer volume of data put is increasing exponentially, Velocity refers to the coming in extreme faster rate than most of us can imagine and Variety refers the different types of data – both structured and unstructured.

Whatever Vs say, we need to know where the data is and be able to access and understand it anywhere, any-time. Without that capability, you’re always looking in the rear view mirror and talking about what was instead of what can be. Can we have insight in to existing data followed by foresight?

Will Big Data enable us to stop looking at business from what has happened till now (Descriptive) and why it occurred (Diagnostic) and start  looking with a more proactive approach (Predictive) that asks, “What will happen and how can we make it happen?” (Prescriptive). For example Can we have a 360 degree view of your customers with respect to their influencers and behaviours which leads to success business?

Implementation of  analytics’s strategy  involves data, process, people

  • Data generated from business processes. It is difficult to bring data together across different data silos. Will storing more than 90 days’ worth of data not become expensive? 
  • We need to come with  the process for finding value in the data  and the process for adding analytics into the business. it is important that process starts small and fast and build credibility. The process needs to use good visualizations to takes audience on a data journey.
  • The team  needs knowledge of the problem domain and one  data scientist.  Every team member should be able to pull their own data and do some simple analytics. They also should be brought up on speed  on the domain knowledge asap. This team should have a decision maker or C level executive as team member to influence and create needed changes in business processes of the enterprise.

Be prepared to handle these challenges

  1. Machine learning or Data Science cannot replace people.
  2. When you accumulate content,the volume, velocity and variety of information getting collected is challenging people’s ability to manage it.
  3. When engagement with customers become more transparent, the back-end business process needs to be streamlined and optimized.