Why we need more, better data

We’re at the early stages of the data revolution, but we need to ensure that the data we draw on is representative and that it doesn’t exclude certain people, including those for whom the traditional workplace doesn’t work.

Side View Of Businesswoman's Hand Analyzing Data On Computer Over Desk

 

There’s been a lot of concern of late about technology, its ability to change our lives for the worse and how it may embed bias. The author and campaigner Caroline Criado-Perez told a recent meeting of the Global Institute for Women’s Leadership that “AI trained predominantly on male data will be worse”. Calling for a gendered analysis of data and safeguards over the data used to fuel AI “to prevent us making the world worse”, she said: “Data is not just about abstract numbers. It drives our world and the decisions that affect every one of us. It really matters when you leave out half the world.”

Of course, data and technology is not always a bad thing. We are in an age of Big Data, where scientists can pull together lots more information than was previously available, in very little time, and track it to see what the latest trends are, how people respond to particular treatments and so on. The trouble is that data sounds scientific and objective, but, like all things, it depends on what the data is being collected for, whose questions it seeks to answer, what the data is used for and so forth.

A recent Chartered Institute of Management conference touched on the issue of how we quantify the number of people who drop out of the workforce, reduce their hours or become less productive because of a lack of diversity at all levels of an organisations and a failure to deal with the barriers particular people face because the traditional workplace [well, traditional for the last few centuries at least] doesn’t work for them.

It is vital that we find ways of providing the data on these issues because otherwise we see problem reproducing themselves, becoming more and more entrenched. The Centre for Cities’ recent study of the impact on productivity in London is a case in point. It draws on decades of research on productivity, but it cannot draw on data it doesn’t have because no-one has thought to keep it. It cannot draw on the people who left, who moved across the country, who took lower paid, lower skilled jobs or set up their own businesses because the system didn’t work for them. And yet these people are numerous.

There are so many areas where this could apply – to bullying, discrimination and so forth. How many highly productive people have left their jobs because they were effectively forced out by poor managers who their employers backed? And what about all the potential that has not been realised? How do we track that in the data? What kind of processes do we need to see in our organisations to make that possible? One start would be to track as much as possible why people are leaving an organisation – and not just in tickbox form. That information is surely as valuable as why they might want to join the organisation in the first place.

We are only at the very beginnings of the Big Data revolution, but it is absolutely vital that we question what is tracked, broaden the parameters of data, ask more searching questions and question everything. But we can only do that properly if we have diverse teams asking those questions.



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