The importance of data & data literacy within organizations

Antony Cousins, CEO of Factmata, has been interviewed at our Decode the Future event. He answers questions around how AI can help analyzing big data.


  • How can AI and data surface actionable trends?

    So actual trends require data points and previously we've been using numbers, right? Quantitative data to get to those data points. But now with machine reading, machine learning and therefore AI, we can turn text and qualitative data also into data points. So AI really gives us access to a world of information we didn't really have access to it previously.

  • How can you measure the ROI of insights? (How can businesses set KPIs to ensure they are getting a good return on investment for data and AI?)

    So ROI, return on investment, requires KPIs. KPIs require data points. So really setting your ROI, working out what the value is you're trying to achieve early, working back from that to your KPIs, KPIs to the data you need to capture. That's critical. Getting that value path set, doing that first is critical to getting to KPIs.

  • How do businesses get clean and accurate data?

    So data is messy. Online data, whether it's textual, whether it's numbers based, it's increasingly complex and increasingly dirty and increasingly harmful, actually. We could, like humans to that problem. we could spend hours and we, and some companies still are spending hours researching that data, analyzing the data, cleansing that data.

    But we've now got access to machine learning and AI. So the grunt work, the heavy lifting should be done by the computers, allowing humans to allow the more creative aspects and the more value add aspects to come through.

  • What do businesses do if your data is misleading or inaccurate?

    Frstly recognize that the data isn't going to get less misleading. So if they're currently feeling like they're behind or that their skills and data capabilities, aren't quite there, that's only going to get worse. What they need to do is invest more heavily in those skills and resources, basic data and analyst skills. And I think actually the industry sort of recognized that that's a fundamental requirement for that staff, but I would go a step further and saying, it's not just data analysis skills we all need, it's now the fundamentals of machine learning, that we all need. So as an AI company, we're working on explainability. So we're trying to make sure that people understand we're not a black box, here's our algorithms work, but the challenge is we can only explain so much. So I think given every member of staff, every employee right now in any business is making use of AI and ML on multiple points in their day on across multiple systems.

    But they don't understand the basics of how their systems work. So actually it's across the board. Every member of staff needs basic understanding of machine learning principles and AI.

  • What skillsets do you need to recognize actionable data points?

    I think actually it's more of a grassroots. Every member of staff needs to have a basic understanding of how those tools are working, because really effectively, those chief data officers, those chief technical officers, the CEO, they're relying on data and importing coming up through, through the business. If those individuals, at the front line, aren't making best use of the data they have available to improve their daily decision-making, that is going to feed all of the business. I think fundamental data analysis skills, yes, but machine learning skills more so.

  • How do you raise the average level of data literacy across the business?

    It's interesting thing actually, weirdly it starts before the business and this is kind of a broader point, but I think we need to be starting in schools. We need to be starting in universities when you starting in apprenticeship schemes.

    So we've got to start much earlier in the spectrum. So basically people feel comfortable and interest and excited by data. And by the time they hit the workplace and then we'll hit the workplace. It's more about the, kind of how that data and how that analysis affects their day jobs. It's not the understanding, the basic concepts, which they should already have by that point.

  • How can intelligent automation (AI) and data help decode the future?

    I think freeing up humans to do the creative aspects, so machines, fast, but dumb, humans are slow but intelligent. So making sure that the machines are doing the work that machines are best catered for, so huge quantities of data, finding connections, finding correlations in huge quantities of data. That's kind of what machines are good for. And using machines to do all of that work, allowing humans to focus on the creative aspects, the insights, working out the outcomes and coming up with solutions. This is just a, this is the way it's going to go. I think the challenge is whether or not you're a business that gets that now, or whether you're going to a business that isn't going to get that until it's too late.

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