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Alan Crameri

It’s been argued that Excel experts should be adopting the tools of data scientists as the demands of business evolve.

But as technology advances, and the skills required to get the most out of data diminishes, maybe we should be making data more attainable instead?

Last year, Microsoft Excel – the ubiquitous spreadsheet tool – turned 30. Despite its age, Excel remains the tool of choice for enterprises around the world. But the problem with Excel is that it becomes time consuming, complicated and difficult to run at scale. And, with the digitisation of daily work accelerating at speed, the demands on our data are changing. So too are the demands placed on the workforce.

I recently read a post suggesting that people who use Excel should also be learning the programming language Python, which is used extensively in Data Science applications. The premise being that this type of language is relatively straightforward to learn – especially in comparison to Excel – and is becoming increasingly prevalent in today’s world of data science, predictive analytics and Artificial Intelligence. However, I disagree. In fact, I think the exact opposite!

Instead of spending valuable time gathering or manipulating data, I think we should be empowering our teams to spend more time adding value. While the data scientist may be the sexiest job of the 21st Century, it remains a specialised area. An area that, while increasingly important, is the preserve of data experts.

What we should be doing is making data more attainable to everyone. And in doing so, we empower those who are specialists in their own area to harness data to fuel better decision making. And as technology advances, the advanced skills required to get the most out of your data diminishes.Take the latest Cognos Analytics release for instance. It embeds AI functionality, drawing on IBM’s Watson technology to allow non-technical users to gain statistical and predictive insight from their data, simply by dragging and dropping the relevant fields. The AI components will then do the hard work for them, and present back the most appropriate insights, identify patterns and relationships within the data, and describe this in natural, understandable language.

Similarly, we can ask Cognos Analytics, in natural language, to tell us what we want to know, and it will identify relevant data and visualisations to answer our questions.

IBM’s Planning Analytics, using the TM1 engine, is demonstrating similar trends. Moving from a world, perhaps five years ago, where users needed to have at least strong Excel skills in order to develop new content, we now have simple ‘drag-and-drop’ canvases, recommended visualisation of data, and natural language searching of the data within the solution.

Another example? Take IBM Watson’s Virtual Assistant. This platform allows users to create chatbots, with an interface where non-technical users can build interactive solutions, using pre-configured components and visual connections. Again, moving away from the need to be an advanced software developer to build and configure these solutions.

Gartner have identified the emergence of ‘AI developer toolkits’ as a recent entrant in the AI space [Hype Cycle for Artificial Intelligence, 2018, Gartner], moving the application of AI from specialist data scientists to more generalist software developers. I expect that in future years we’ll see this move further into the realm of non-technical users.

At Barrachd, it’s our mission to design and implement solutions, using the available technology, which enable business users to understand, interact with, and gain insight from the data available to them, in an intuitive and clear way.

These users don’t need to be software developers, or programmers, and they don’t need to undergo extensive training. By doing this, we enable people who are specialists in their own areas, whether it’s sales, planning, marketing, HR, operations, manufacturing, finance or others, to make more effective and powerful use of the data within their organisations – a new generation of analytics, to support your business today.

First published by Barrachd, part of Capita.

Written by

Alan Crameri

Alan Crameri

Technical Director, Barrachd, part of Capita

Alan provides strategic guidance and leadership, so we can provide valuable insight to our clients. He brings over 15 years’ experience in designing and delivering planning, predictive, data warehouse, BI, dashboarding and advanced analytics solutions. Alan has an MBA from Lancaster University and a degree in Material Science from Clare College, Cambridge.


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