dilbert_dashboard_data

12 Drivers of BigData Analytics

http://v1shal.com/content/12-drivers-bigdata-analytics/

So, why am I writing another blog on the importance of BigData & Analytics? A couple of days back I bumped into an executive, and a small talk went into an hour-long conversation on what is the business justification to starting the BigData initiative. Basically, what drives the BigData Analytics Strategy? It was an easy but uncomfortable decision and I thought sketching it down would help in giving an initial GPS if you are still not sure where to look for motivation on why BigData Analytics projects and what are it’s drivers.

Let’s look for these drivers from two different lenses: Business and Technology. Business entails market, sales and financial side of things, whereas, Technology has indicator/driver targeted towards technology and IT infrastructure side of things. Let’s get going on the business side first.

Business: So what drivers make businesses tick? 1. Data driven initiatives: They are primarily categorized into 3 broad areas: a. Data Driven Innovation: I particularly like the innovation aspect with being data driven. Imagine being able to learn from your customer first what they need and having the ability to drive innovation through those uber targeted data indicators. b. Data Driven Decision Making: Data driven decision-making is the inherent ability of analytics to sieve through globs of data and identify the best path forward. Whether in terms of finding the best route to validating the current route and estimating the success/failure in current strategy. It takes decision making away from gut and focus on data backed reasoning for higher chances of success. c. Data Driven Discovery: Your data know a whole lot about you than you image. Having a discovery mechanism will help you understand hidden insights that were not visible through traditional means.

Read Also:
The Unparalleled Utility of Graph Databases

2. Data Science as a competitive advantage: I had the fortune of interacting with couple of mid size company’s executives from commodity businesses. There had been a consistent outcry on having to build big data as a capability to add to their competitive advantage. With a proper data driven framework, businesses could build sustainable capabilities and further leverage these capabilities as a competitive edge. If businesses were able to master big data driven capabilities, businesses could use these capabilities to establish secondary source of revenues by selling it to other businesses.

3. Sustained processes: Data driven approach creates sustainable processes, which gives a huge endorsement to big data analytics strategy as a go for enterprise adoption. Randomness kills businesses and adds scary risks, while data driven strategy reduces the risk by bringing statistical models, which are measurable.

4. Cost advantages of commodity hardware & open source software: Cost advantage is music to CXO’s ears. How about the savings your IT will enjoy from moving things to commodity hardware and leverage more open source platforms for cost effective ways to achieve enterprise level computations and beyond. No more overpaying of premium hardware when similar or better analytical processing could be done using commodity and open source systems.

Read Also:
Splice Machine 2.0 combines HBase, Spark, NoSQL, relational...and goes open source

5. Quick turnaround and less bench times: Have you dealt with IT folks in your company? Mo and mo people, complex processes and communication charter gives you hard time connecting with someone who could get the task done. Things take forever long and cost fortunes with substandard quality.;

Read Full Story…

 

Leave a Reply

Your email address will not be published. Required fields are marked *