GDS Consulting

Creating the right data management policies, procedures, and processes to ensure it remains accurate, secure, accessible, and usable for analytics insights should be a top priority.

I get asked a lot of questions by customers at all levels. They all want to know how to solve their problems, and it is part of my role to help them find answers.

The range of questions is extremely broad. Data scientists want to know how they can improve collaboration and speed up data preparation, or combine what they have with open source elements. More senior people have different questions.


CIOs want to know how to improve governance, ensure transparency, and reduce the number of vendors. They also want to make sure that whatever they choose to do is as future-proof as possible. Business users, such as heads of customer complaints, want to know how to improve productivity and repeatability.

A wide range of needs

In other words, a wide range of questions, showing a wide range of needs. What I see increasingly, however, is that the answer to many of these questions is actually the same. It hinges around the effective use of a high-quality analytics platform. This fits with the findings emerging from our recent study on artificial intelligence readiness, which suggested that platforms could help to meet a wide range of stakeholders’ needs.

It hinges around the effective use of a high-quality analytics platform.

For data scientists, for example, collaboration can be increased easily and quickly by having a single platform, and working across it. It does not matter whether you are using vendor software or open source, either: the platform should be able to manage and integrate both, enabling you to use the right tool for the job quickly and simply. It can also be made accessible to all users, speeding up data preparation. CIOs will be glad to know that a platform hugely improves governance, because everything is in one place. This also makes audit much easier. You can use as many or as few vendors as you wish, because everything is accessible and can be sensibly integrated on a platform.

That’s analytical users and senior data managers. But what about the end-user, such as customer complaints managers? Again, a platform could be the answer. It reduces manual work, because a platform enables analytics to be done more simply — and without relying on manual tools or Excel. Textual analysis can speed up analysis of customer complaints, and reduce what was often a major chore, done manually, to a quick and easy job. This, in turn, gives more time to spend on examining the insights and applying them, rather than carrying out the analysis. A platform can also improve productivity and repeatability, because the same process is available to everyone.

In other words, we are increasingly seeing that a wide range of problems can be solved by using an analytics platform. On the surface, the problem may look different, but dig down, and it is likely to be the same platform that delivers the solution.

A question of jargon and vocabulary?

This idea that the same solution can often be the answer to very different questions is quite an eye-opener, but I think it may also be an emerging sign of a much bigger issue. Collaboration is increasing in analytics because data scientists and business users increasingly need to work together to find and explore insights more effectively. As a result, we are all being forced to think beyond our own perspective, and start to look at situations more broadly, assessing the needs of multiple groups as part of the overarching whole.

We all have a tendency to see situations from our own point of view, or perhaps more accurately, to consider ourselves the centre of the situation. I do not mean that we lack empathy, but more that we tend to see the problem as our own, and want a solution that will address what we see. We then choose and use language to describe the situation to others, and those choices naturally reflect our perspectives.

This is partly shown by the way that individual job specialities and groups often develop their own jargon to describe both problems and solutions. It is, however, broader than that in its essence — and also in its effect. In practical terms, it often means that the same problem is being described in different terms by different groups. It is not hard to see improving collaboration and better governance as closely linked, for example.

It is, perhaps, no wonder that the same solution may be the answer to so many questions. It may be that the questions themselves are actually the same. In other words, closer collaboration could lead to better identification of problems, simply through shared use of language, a platform in itself.



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