The IT industry is full of contradictions. The latest ironically named tool to emerge is the Infographic.
In theory, this is a device to help you visualise a story. In practice, it does exactly the opposite. Most infographics that I get sent, to help me understand the sterling work of the big data vendors, achieve exactly the opposite. After looking at the hastily cobbled together chart, I’m usually none the wiser. More confused in fact. And a bit angry and resentful.
It’s something of a relief to learn that I’m not the only one who doesn’t understand big data properly. Having talked to experts in the telecoms industry, I’m now confident enough to come out and admit it.
The root of the problem – as ever in the IT industry - is there are too many choices. There’s so much you can do with big data people don’t know where to start and, when they do, they inevitably get their priorities wrong.
It’s often liberating when we don’t have any choice. The biggest big data strategy mistake is to run all their programmes at once says Ravi Kumar Palepu, head of telco solutions for management system vendor Virtusa.
Telecoms can be improved on many fronts by big data. They could improve customer experience, boost revenue, fine tune their networking operations or (my least favourite option, as a subscriber) create pro-active marketing. But you can’t do them all at once as they affect each other, says Kumar.
But all the various departments of the telco client will be shouting for their project to go first. Once you have decided whose seam of data is to be mined first, the next priority is assembling the tools and the personnel.
Here the mobile world exemplifies an industry wide problem. Mobile telecoms operators often lack the expertise needed to create commercial advantages out of big data. There’s a small talent pool, says Matthew Roberts, director of marketing at Amdocs’ big data analytics and strategic innovations division. “Data scientists are in high demand, particularly those with a telecoms specialism who can understand the data in context,” says Roberts.
As we’ve seen with Infographics, the talent for visualizing data patterns is massively over rated. Most data visualizations are like those inkspots that psychologists use to test their mental associations of their patients. You could read all kinds of nonsense into them.
Meanwhile there is a lucky few, confident enough not to feel guilty about their exploitation of this new area, who convince others they are finding golden nuggets of insight while mining the vast seams of data.
Roberts liken it to another form of alchemy. “[Data analysis is] like a cocktail, blending different ingredients and you don’t know what works until you’ve found the winning formula. The trick is to find it fast before you drown in data,” he says.
Ben Parker, chief technologist for Guavus, says we cannot profit from big data until we become more narrowly focused.
“When panning for gold, you do it in the river, sifting out the excess, and bringing home the nuggets – you don’t shovel all the soil into a truck to take back home and sort, that would be a waste time and money,” says Parker.
Yet this data hoarding is exactly how many companies approach to data analytics. They don’t try to boil the ocean – they want to historically examine every molecule.
The past is the past - you are better off dealing with the here and now, says Parker. “By applying streaming analytics at the edge, sifting out the useless data and keeping the nuggets of gold, companies are better set up for success,” says Parker.
The most immediate way to tackle, say, network operations, customer experience and revenue streams is to work on what’s happening now, which you can find in the data streams, not the stored silos, says Parker.
For big data’s benefits to be fully realized, more people need access to insight, which includes marketers and customer care organizations, who will want immediate and continuous use, says Mark Davis, Citrix’s senior director of product marketing. “There’s a shortage of big data scientists but a new class of big data analytics platforms that makes the insights more widely available,” says Davis.
Maybe so, but can we make sense of them? Personally I doubt it. And no, an infographic won’t make things any clearer.