A common challenge that many companies face in starting up their big data and business analytics programmes lies in understanding where to start and how to make it all come together. Business analytics requires the application of various skills that are normally found distributed across multiple functions in contemporary organizations. It doesn’t help that the availability of new technologies for and techniques is invariably accompanied by new jargon to describe them.

So if you’re one of those business leaders who is wondering how to begin, this post is the first in a series of six that will provide some handy pointers to understanding analytics and how to get started.

Step 1. Decide what the top one or two business questions that need answers are.

Several studies have found that a large number of business leaders think it is very important, and possibly critical for them to start using big data analytics to gain insights that can help them move forward with more clarity, faster or more efficiently. A common cause of early inertia, however, is figuring out how and where to start.

A recommended approach would be to aim for low hanging fruit, and further prioritise which are the one or two to aim at. As with any other new initiative being undertaken for the first time it would be helpful to start with something that can be treated as a limited scale pilot that will help understand the science and gain experience on how to convert ideas into execution.

The first step in getting a big data analytics project off the ground is to pick those top one or two questions that need answers. These are not just questions about what has already happened in the past (which your existing BI may have already answered), but questions about what can happen in the future given what you already know of the past. An example in the field of marketing would be a question about which geography or demographic group is more likely to buy what products in the future.

Once the question has been framed, the next step would be to consider what types of data might possibly hold the answers to that question. Why that is important will be dealt with in the next article in this series.