A couple of weeks ago, I met with Richard Belgrave, Head of Europe at LEVERTON, a company who use artificial intelligence to extract data from complex, unstructured documentation. Although most of our chat was about AI within the property industry, there was also a brief segway into Big Data, and Richard had an interesting take on this hot topic.
“I really struggle with this concept of Big Data’, he told me. ‘It seems to me to be a buzzword; one that gets in the way of a more important discussion around data.”
The concept of Big Data revolves around the idea that technology now allows us to gather all of the data our businesses or properties produce and then claims to tell us how best to use that data to our advantage.
However, Richard would argue that this fascination is premature because there’s no point in discussing what data can do when so many of us don’t even fully understand what our data means.
It’s not helpful, he says, to place all of this attention on the possible uses of large amounts of data. If we don’t take the time to understand what our data actually means, we can’t be truly effective in how we implement it.
“We live in a world where all data is represented on paper,” says Richard. “Big Data promises to unearth that data and help you make better decisions, but because the process of extracting, reading, and understanding documents is so cumbersome and costly, we’ve found that most companies we speak to confess that they don’t actually understand their data in the first place, mainly because the data they have access to is of such low quality.”
Because Big Data is a zeitgeist term, it’s easy to assume it’s a logical step for any company to start mining the value of their own data. But, the truth is, most companies are trying to go from No Data straight to Big Data in one single step.
That’s just not possible. First, you have to source high-quality data, then you can analyse it and use it to your advantage. Yet, continually, companies chase Big Data without even knowing what it actually means.
You can look at it like this; you have hundreds and hundreds of penny chews, you’ve been collecting them and not eating them for years. You don’t know exactly how much of each variety you have because you’ve just been throwing them in the bag without looking.
Now someone comes along and says, hey, you’ve got all these sweets and they’re just sitting there. How about I help you sell ‘em and we can both make a bit of money?
What you wouldn’t do is just blindly say yes. You don’t, after all, even know what the value of your sweets is because you’ve no idea what’s in the bag, you just know there’s a lot of them.
The more sensible thing to do would be to first get your head around exactly what your inventory of sweets is. Then, you can hand them over to the person offering to sell them fully aware of their potential value. You can even make an agreement with the seller that you want a guaranteed minimum return for your sweets.
If you know exactly what sweets you have, you can also think about who’s most likely to want them and where those people can be found.
What an incredible analogy, hey? I’m a pretty astute thinker.
But I think it demonstrates the point, kind of, that no matter how vast your data collection is, it means nothing if you don’t properly understand what you have. Too many companies are relying on the concept of Big Data to further their Digital Transformation, mainly because they’re being told this is the best way to do it. But it really isn’t.
Don’t dive straight into Big Data – first, take the time, and find the help, to better understand it. Once you do, you’ll be far more successful in extracting its value.