A role for women in tech – exploiting it

I have, for many years, held the opinion that the IT industry has thought so highly of itself and its technologies – for their own sake – that it has often missed understanding its real place in life and the real contributions it could be making.

What is more, the adoption of such a mindset means that it also misses the opportunity to exploit the talents of people who could exploit the capabilities if IT despite the fact that they eschew the glorification of technology for its own sake.

For example, the other day I got an invitation to attend a conference in the USA about Big Data Innovation. Well yes, the technology is arguably important, but only as a facilitator of something else; and let’s also face up to the fact that `Big Data’ has been an issue for IT systems since the earliest computers running just a few Kbytes of core memory, where programmers wrote whole business systems in Assembler that ran in 4k of memory.

The issue here, of course, is that the data is pretty irrelevant, in and of itself, no matter how much any business or individual has of the stuff. The real trick is being able to analyse it effectively in order to get something of value out of it.

Yet even those companies that are supposed to be the sharpest tacks in the box at such a game – the major retailers – show that despite having huge quantities of information available their ability to find anything of business value within it all borders on the naive. Take, for example, the following recent plea from a Facebook friend:

“Why does advanced analytics of my buying patterns always seem to suggest that I should buy more of something I really could only need one of and have just bought?”

Who hasn’t been there? Having spent time trawling round the web to find the best price and source for a product, and with the purchase made, that lucky retailer then bombards you with blandishments and requests to buy some more of that product.

OK, if the purchase was, say, a bottle of rare whisky, or some difficult to source food stuff, suggesting buying something similar might make sense. But it depends on context: if my purchase was for a Christmas hamper, sending out the suggestion that I buy another or three is pretty dumb.

One sees the same effect with the advertising on many popular social media sites. Having bought one of product X, adverts from ninety-three other vendors selling product X suddenly appear when you log in.

So, if analytics tools can’t do context by now they should be unplugged and consigned to a life as shelfware.

But I suspect most of them can, it is just that they are not programmed to do so. In other words, the real issue with Big Data and analytics is what questions are actually asked of it all. And the answer would, for now at least, appear to be: `pretty dumb ones’.

And that leads me straight to the next question: how to create the right questions? And the answer to that emerged during a panel session at last week’s Fujitsu Forum in Munich, where the subject of women in technology came up for discussion.

Part of the issue, according to Citrix VP, Jacqueline de Rojas, is the way the tech industry presents itself makes it appear an unwelcoming place for women to want to work, and the situation shows no sign of improving. Yet perhaps part of the answer to that problem is that very tech-centricity the industry portrays – that doing anything using tech is fiercely complicated and `special’ in some way.

That may have actually been the case once upon a time, but it isn’t now. Indeed, most of the `fiercely complicated’ is now automated out of existence as either a skill requirement or a problem. Now the issue is what you want to make tech do in the real world, not how it is done. Big data analytics is a very good example of this.

It is what questions are asked, and why they are important, that are the key issues here. The `how they are asked and answered’ is just an issue of increasingly automated facilitation. As these real world roles start to emerge so they will, I suspect, be filled by women. Formulating the questions that really need to be asked of big data is likely to be an area where women shine brighter than men.

Yes, I know, men will say that they are more logical and it is logic that is needed in formulating big data analytics questions. To which I would venture the response: `complete tosh’. It is exactly that logic which, I suspect, creates the assumption that having purchased one product X, the customer must obviously needs more product X.

On the other hand, how many men are there who have not faced the situation where a wife, girlfriend, sister or mother has, after listening to 20 minutes or so of male waffling, asked that one question which neatly nails said male to the wall? Not many, I suspect.

Women can be truly expert at hitting the real point of an issue, and could be an important secret weapon in exploiting the technologies behind Big Data and analytical tools. Indeed, when it comes to really putting the technologies to work in the real world women may well prove to be far better at exploiting the potential than the majority of men.