Simplify so my grandmother can understand

It’s always interesting to watch as technologies and business strategies gain momentum and then ebb and flow in popularity. Big Data has been a prime example, with journals, blogs and social media displaying an ongoing variation between hype and stagnation. The result, especially in the intensive, opinion-rich on-line world, is a mix of views from “10 reasons why big data is failing” (e.g. Forbes} to examples portraying “the ten most valuable big data stories” (e.g. Information Age). Extremes always grab attention.

When digging a bit deeper, these conflicting views actually highlight some interesting similarities. Firstly, many of the reasons for failure are not big data or analytics-specific issues, but a reflection of poorer corporate or enterprise strategy and focus, that could relate to any project. I’d include in this factors such as a lack of clear business objectives, not considering an enterprise strategy but instead working in silos, or a lack of communication and misalignment of business and IT objectives. None of this is a big data problem.

An area that gets much attention is the skills needed for big data, with a specific focus on Data Science. This is much more of an analytical issue, but again digging into some depth reveals that the underlying issues are often communication and terminology. With a clear definition of what an organisation is trying to achieve, it becomes easier to understand what skills are needed. From this, like any project, it’s easy to develop a skills matrix and determine what skills are light in the organisation (or missing), and what can be addressed by training, and what by hiring. Organisations that assume the second step in defining a big data project is to hire a data scientist, will be on a futile unicorn quest.

A common theme in the successful criteria is for focus and clarity. A focus on objectives and a clarity of purpose, but without pre-supposing outcomes. This is a tricky balance and requires an open-minded approach – this is where the real skill necessity emerges: 

• How do you keep an open mind and not set-out with a preconception of what a piece of analytics will discover?
• How to let the data guide you, to read, analyse and interpret with a flexibility to move in new directions as the story unfolds?  
• Trying out new ideas, new techniques and incorporating new data : taking unexpected detours on the journey.
• To read data, but also avoid red-herrings, making sensible, reasoned observations and avoiding traps (correlation vs causation being a prime example).

The most telling way that this approach is successfully achieved is in how the final conclusions are stated. Ideally it will simplify and summarise, explain not just what, but why (and also why-not; to show what was tried and found not to be useful). “Simplify, so my grandmother can understand” is how one CEO put it to me.

As a direct consequence the emerging focus is on clearer communication of findings, and on topics like visualisation and storytelling. Many organisations are achieving much more with analytics and big data; their quest now is to expand the horizon through better communication, to ensure that projects become enterprise initiatives.