There’s been plenty said about how the power of big data and analytics will drive efficiency and open up new avenues and business models for today’s organizations. Many times when discussing the topic of big data, the focus is on the volume of the data, the structure of the data or the near real-time analysis requirements of the data. It’s a daunting task to keep up with big data technology and its landscape as you can see here.
Taking small steps to Big Data
Challenge is drawing insights from big data and analytics rather than being overwhelmed by them.
We hear about a lot of buzzwords on big data, however, often times what is missed are the end goals of the big data and analytics challenge we are trying to tackle. I’m not advocating to ignore the big picture on big data technology or data sources, rather provide clarity on where and how to get started.
Any successful big data and analytics initiative needs to focus on how the data can impact a company’s top line, bottom line or both when it is conceived and constructed rather than focusing on how many petabytes of information is available and how advanced the big data environment or technology you have access to.
The best way to dive into big data is to start small. Not in terms of the amount of data, but rather of your focus. Pick one area where you can use extra insight and begin by targeting your efforts there. Companies would benefit by starting small with a use case or a business problem that would bring value to the business today. It is important to identify a use case that is not overly complex, but would deliver business value. This approach would allow organizations to mobilize leadership support and business “buy-in” incrementally while planning for a big “home run” in the big data journey.
Visioning Workshops
Conduct a visioning workshop to brainstorm a set of business use cases to explore the “art of the possible,” and identify and then prioritize business outcomes that big data & analytics can help achieve. Develop a value case and select a use case with a minimum complexity to get started on this journey.
Big Data and Analytics Innovation Cycle
Gartner’s bimodal IT approach, while perhaps not new, is especially relevant for the big data and analytics innovation cycle. It starts with taking an use case, developing a hypothesis and an analytics approach, followed by developing analytical models and experimenting the approach on the cloud.
Modern cloud big data and analytical platforms and services such as IBM Bluemix, Microsoft Azure and AWS offer big data and analytics capabilities that can be brought to bear on a problem in a matter of minutes. Companies can start generating useful answers in just days when the innovation cycle is deployed on modern big data cloud platforms.
“Two guys in a Starbucks can have access to the same computing power as a Fortune 500 company.” – Jim Deters, Founder, Galvanize
Such approaches can sidestep the long lead times and high cost of standing up a large-scale big data environments and focus on quickly proving (or disproving) hunches and hypotheses relating to the big data.
Big data doesn’t have to be daunting. By focusing and starting small, you can take your first steps into big data, and begin taking advantage of its big insights.
Srini Kasthoori 