Everyone’s talking about ‘big data’. There’s no doubt that there are more bits and bytes than ever before. As George Dyson writes ‘when the digital universe began, in 1951 in New Jersey, it was just 5 kilobytes in size’. Today, by some estimates, the total amount of information in the world adds up to 2.7 zettabytes. But what about insight, analysis, outcomes?
Making sense of data
But sequence is different from time, quantity is different from quality, data is different from information. More is not always better. If we’re going to live in an era of big data and machine learning, we need to find a way to democratise it and make it useful. Big corporations and governments shouldn’t have a monopoly.
There needs to be a human-scale interface for people to store, access, visualise and process data.
Insight not data
Businesses also need to find ways to translate big data into meaningful answers. By this I mean small excerpts or summaries of information drawn from larger databases which are useful to individuals.
Whether you’re a geek or a creative person, a techie or just a regular human being, you should be able to get the answers you need. People don’t huge spreadsheets of data. Sometimes, they need a single point of information to make a decision. Sometimes they need the answer to a specific problem.
Too often technology vendors talk about technology not about business outcomes. You are not your customer. You are not the user of the systems you build. An employee is not an entrepreneur. A manager doesn’t think like an accountant (or vice versa).
What we need is a heroic effort to put our selves in the other person’s shoes and see the world through their eyes.
Meaningful insights, not more spreadsheets
For example, on Turbine, useful data means:
- How many holiday days have I got left this year?
- Which colleagues are away from the office today?
- What’s the budget for this purchase?
- Did my boss approve my expense claim?
- When is it getting paid?
- Is my holiday request approved so I can book my summer holiday?
In fact, most of the time, what people want is not ‘big data’ but big answers.