Even as the buzz around Big Data keeps growing, a myriad of technical and management related issues continue to challenge its successful implementation. IT experts suggest only a handful of big data projects taste true success. Tapping into a truly large pool of data is no mean feat!
Begin on a Small Scale to See if Viable
Having access to vast unstructured data, thanks to cloud, social, and other technologies, isn’t a big deal anymore. The real challenge lies in processing the data for actionable insights. “A couple of years back the industry had a naïve approach towards Big Data. There were talks but CIOs and CEOs lacked perspective on how to derive their intrinsic value to add to their companies’ bottom line,” says one of our subject matter experts at DMI Kiran Jain. “Now they have learnt to invest in Big Data on a small scale to see if it holds out a viable solution,” he adds.
To understand how Big Data fits into the overall end-to-end solution, and further in the industry, it is advisable to:
- Break down the tasks and problems into sub-sets and start on a small-scale with limited investment
- Begin with a proof-of-concept project to see results within three to six months
- Start with the available data instead of waiting for the perfect set for speed, insight and eventually accuracy
Build a Road Map
Given the strategy, the next thing that begs an answer is how exactly to zero in on the right case for proof-of-concept? Simple enough. Start with the customer.
- Who are your core customers?
- How do they behave?
- What drives their behavior?
Building meaningful behavior-based segments (i.e. loyalty, price sensitivity) is typically a good starting point. It would help uncover immediate opportunities for retention and growth. One can get an idea from it to create a measurable test plan to implement upon a smaller subset of the segment.
One of the nation’s largest payment processors leveraged behavior data science to develop a merchant-centric pricing for more proactive and prioritized retention as well as cross-sell opportunities. The strategy led to a significant lift in revenue, while stemming customer attrition, in just three months’ time!
Coordinate Across Departments
Yet another aspect not to be overlooked is that building the road map from the strategy requires proper coordination between the business side, including marketing, sales and finance, on one hand, and IT, on the other. The IT department must have a clear idea of where they need to go with the help of data science and analytics algorithms, using them as tools, to arrive at crucial business solutions. Hence, they need important insights and pointers from the business side.
Forrester Research recommends integrating business intelligence and Big Data in a flexible hub-and-spoke architecture in their report Boost Your Business Insights By Converging Big Data And BI from earlier this year. This includes components such as: Data Storage, Data Preparation, Data Discovery Acceleration, Predictive Analytics and Data Visualization.
Scaling Big Data, if done right, can have huge benefits, but it must be built on a solid strategic foundation.
Are you ready to explore the power of Big Data but not sure which partner to go with? Check out our previous post, Data Dating: Finding the Right Partner to Take Your Data to Market.