Who’s Afraid Of The Big, Bad Data?

Who's Afraid Of The Big, Bad Data

Why are so many organisations so terrified of data and refuse to see the benefits it can bring? ‘Big data’ is now a common term, but traditionally, the treatment of the flow of information within an organisation has been comparable to that of a piece of radioactive material.

Not only has it been quarantined and isolated in the IT department, but access to it has typically been limited to only one individual – the data scientist – who is deemed to be the only person qualified enough to handle it.

The truth is that although most businesses are keen to understand how they can use the data more widely, the vast majority are still terrified by its complexity. As a result, many adopt an ‘out of sight, out of mind’ philosophy, deferring it to their IT department to handle it for them. This approach is, of course, as self-defeating as it is confusing, and it’s important to remember that there’s nothing to be scared about when handling data!

For the many businesses that wish to actually do something with the not inconsequential amounts of data they have, adoption and uptake needs to come in stages. The first stage is simply to understand why data is even being collected. Identify what questions are looking to be answered by having this data, and how it can help. Once this is understood, and the business knows what it wants to find out, it can go ahead and use business intelligence analytics tools.

The next stage is the analysis of the data, by giving employees the opportunity to investigate it and find their own answers. But how do you decide which people within an organisation should have access to the data? Easy! People in every job function, at every seniority level and from every skill background should have the opportunity to explore data.

Anybody who works with data, either in isolated circumstances, or in their everyday lives, whether they know it or not, will find that they have a question of that data 100% of the time – something they’re trying to find out. Unfortunately, when working with millions of rows of data, these same people can never, and will never, get the answers they seek 100% of the time.

Instead, they will either draw an incorrect conclusion, palm off their data to the ‘IT guy’ or worse still, just give up altogether.

That’s exactly why it’s so vital to democratise the access to the answers that data can provide. Why would you want to provide clunky ineffective analytical tools for your employees? It’s demotivating, inefficient and ineffective. In contrast the use of effective visualisation tools will motivate employees across the whole business to ask questions of their data themselves, get answers themselves, and improve their productivity themselves.

A question I was asked recently was ‘but what if two people reach two different conclusions? Wouldn’t this be a bad thing for management?’ I couldn’t disagree more with this thinking! Why would a company want to stop those points of view coming together and working out the best way forward with a fuller understanding of a situation?

Instead of treating data analytics like a harmful substance, perhaps businesses should be emphasising the importance of using tools that can extend the value of information across the entire organisation. By using tools that allow everyone to visualise data and interpret it easily, businesses can help employees out of the data dark ages. It will help them to ask more questions, feel more engaged and become data rock stars!

Jock Mackinlay is Tableau Software's Senior Director of Visual Analysis. At Stanford University he pioneered the automatic design of graphical presentations of relational information. He joined Xerox PARC in 1986, where he collaborated with the User Interface Research Group to develop many novel applications of computer graphics for information access, coining the term "Information Visualisation." Much of the fruits of this research can be seen in his book, "Readings in Information Visualization: Using Vision to Think." Jock has a Ph.D. in computer science from Stanford University.