Turning lead into gold: A data story

What can the magic of chemistry teach us about data? We look at how the right data can create an impressive commercial reaction.

Chemistry is – to quote many an enthusiastic high school science teacher – everywhere. It’s the air we breathe. It’s why we cry when we cut onions. It’s the processes that turn rotten grapes into booze, or old milk into £200 cheese.

What does this have to do with data? Well, we’re glad you asked…

Just like Chemistry, data is everywhere. And just like a chemical reaction, you can put your data through processes that transform it from raw information into something highly valuable for your business. But get those processes or ingredients wrong and your data can blow up in your face – and anyone who has experienced the effects of poor-quality data will know how explosive a single mistake can be (which is why we put together our 5 tips for better Data Quality to help you avoid them).

In this series of blogs, we’re looking at what the magic of chemistry can teach us about data: how we handle it, the processes we put it through, and the results we can expect to achieve.

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First up: Turning lead into gold – and data into £££

Back in the middle ages, pseudoscientists were obsessed with the pursuit of Alchemy: a ‘chemistry’ (kind of) that could turn virtually worthless metals into gold. But this process sat somewhere between science and magic, featuring equal parts logic and mystical nonsense.

We can’t help but feel that today, something similar is happening with data. Businesses are expecting their data to almost magically transform into ROI for their business, without really understanding the raw data they are dealing with, the processes it needs to go through, or the limitations of the data that’s at their disposal.

The pressure is on CDOs, CTOs and data technology providers to make that magic happen and transform a company’s data into pure gold. But just like alchemy, it’s not going to work. For a business to extract value from their data, they need a business-wide process to follow, and a realistic, achievable goal to work towards.

Reality check

While alchemists might not have succeeded in turning lead into gold, it is now scientifically possible – if you can collide neutrons with lead atoms at speed. Unfortunately, this method works out far more expensive than the minuscule amount of gold it creates is worth.

So why bother? Why not make that lead into something that does have value, or scrap the lead altogether and use something else?

We’ve seen businesses invest thousands of pounds and hours into data initiatives that are doomed to fail from the start. They start out with a wildly ambitious data goal, a database full of poor-quality records, and an expensive piece of technology that they anticipate will make anything possible.

Often, they do manage to extract some value from the data in the process – but they don’t get the results they thought they would. Over time, it becomes a disheartening, frustrating experience that damages a business’ perception of how valuable data can really be.

Here’s an example. If you’re setting out to implement AI to analyse your data, but the data you have is incomplete, inaccurate or irrelevant, your technology is not going to be worth the investment. Because what you are putting in will always impact what you are getting out. Mountains of lead = tiny amounts of gold. Heaps of poor-quality data = minimal results.

Creating real value with data

Our advice to businesses is this: stop gathering lead in the hope you can turn it to gold. Think about what you want to achieve with data, that will add value to your business – your customers, your employees, your suppliers, your products – and start addressing what you need to do to get to that goal. Defining what ‘good quality’ data needs to look like for your commercial goals – it’s relevance and timeliness, not just its accuracy – is crucial to getting value from your data.

What are you aiming for?

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Just because ‘everyone else’ is looking straight to AI or Data 360, doesn’t mean that’s the right step for your business. There could be other ways to get value from your data that give you a better return, from Product Information Management (PIM) to support eCommerce to MDM that manipulates customer data.

What data do you really need?

And is that data up to the task? Data quality is a crucial step, not only to bring your data up to standard, but to explore whether you are collecting the right data for your goals.

If you need a practical approach to data quality – whether you don’t know where to start, or need to boost what you’re already doing – download our handy guide, 5 tips for better Data Quality below, to get started.

5 tips for better Data Quality