CANDDi Blog

Analytics - Why Keeping It Simple Still Matters

Big data analytics has become a buzz-word in almost every industry. In many ways, the idea of analytics today is like the idea of having an online presence in the late 1990s. Everyone knows that it would be a good thing to have, but few people really understand why it matters and what it could do for them.

Finding Actionable Insights

If you have a poor big data analytics strategy, then it is all too easy to end up drowning in data. The goal with your analytics strategy should not be to ‘collect as much information as possible’ but rather to get accurate and actionable insights. To do that, you need to know what you are looking for and collect only that information rather than flooding yourself with statistics that are too hard to interpret.

There are two risks with going too big with your big data: one is getting massive data reserves that you simply don’t have the in-house expertise to process at all, and the other is drowning yourself in too much information, to the point that you cannot figure out what the data is trying to tell you.

The Data Lake

Data analysis specialists have called this the ‘data lake’, and they are focusing on new ways of figuring out how to handle the structured and unstructured data that is being housed in all those lakes (as well as the data in between), so that they can find a way to transform those lakes (which are essentially wastelands for companies that cannot navigate them) into useful data.

Simplicity should be at the core of any data analysis system these days. A good strategy is to figure out the questions that you want to answer and then to work out what information you need to collect to get those answers. Prioritise your analytics around that, rather than using a scatter-gun approach to collecting data. It is cheaper to just gather the information that you want, and it makes it much easier to interpret the information and to remove erroneous results, duplicate records and outliers.

Some companies - even small ones - use custom analytics suites because they can be tailored to the needs of their own business. With a more focused approach, untangling the web of big data becomes a much easier job, allowing you to focus on the core of your business.

Tim

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