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3 reasons why you should care about clean, and measurable data, and an example of where it’s working

The term Big Data is no longer a buzzword, it’s become an institution, and businesses all over the world are hiring Data Scientists, Chief Data Officers and the like to help them make sense of it all. But Big Data shouldn’t be thought of some scary, untouchable thing. We’ve been collecting data for decades and Big Data is well, just more of it.

Considering we now have a lot more data coming in, day on day, how best can we make it work for us? The first step is to ensure that the data you have and the new data you’re receiving all the time is clean, unduplicated and understandable. Data is also very vulnerable to decay and this is brought on by introducing new systems often, uncontrolled duplications and incomplete records, missing fields and different formats (to name a few). Even once you’ve finished with a data cleaning exercise, it will immediately start to decay once more. By implementing an automated data cleaning system, you can avoid this decay as much as possible.

There are many reasons to care about ‘data hygiene’ but the three below are my most important:

Make better decisions

A colleague of mine often says “om te meet is te weet” which translates to “to measure is to know”. If you’re not measuring the data you have, you may be missing out on making better, more informed decision for your business. If you’re not measuring something, how can you know the value it holds? When you measure your data, you understand it and by understanding it you will be able to make better decisions. Think about how much time you might be wasting on data that doesn’t make sense.

Optimise your data analytics

You want to make sense of the data you have so you run some form of analysis, you ask a certain question and you hope that the information you get is correct. If your data is dirty, to begin with, you can’t expect the analysis to be spotless. It’s like trying to sweep your bedroom floor with a garden rake, some things will get left behind and you’ll be left with less than desirable circumstances – your floor will still be dirty. Even if you have impeccably clean data, the system you’re using might not be the best to do the job so take that into consideration as well. Imagine trying to clean said bedroom floor with a small hand-held brush, it will do the job but heck it will take you a lot longer to come out with a clean bedroom floor.

Improve innovation and experience

There are two other players when it comes to your data and the use thereof, your staff and your customers. Having clean, measurable data that can be mined efficiently and even automatically will give your staff the opportunity to be innovative. With less manual work comes more brain work. This, in turn, creates a better experience for your customers and users.

There’s a real-life and incredibly interesting example of where measurement of data that is clean has made a massive impact on a company, its staff and its clients.

Up until the early 1990s, Rolls-Royce used to sell its engines and then offer maintenance, all this without knowing the state of the engines in question. There were occasions where engines would fail in remote areas and at an immense expense (of money AND time), the broken would be flown out and the replacement flown in. Relying on a breaking engine to generate revenue was not a good business strategy.

“The development of the aviation industry led to a situation by the 1990s where the majority of large airline operators had developed huge support infrastructure, creating multiple duplications across the supply chain, which they could no longer sustain as market pressures forced them to cut costs and focus on core business. To address this customer requirement and opportunity, Rolls-Royce began to work on a different concept in which better aligning the support network, supported by capture and use of data, would drive the process and make this support activity more intelligent and efficient.”[1]

Rolls-Royce, through a service called TotalCare, now hires out the majority of their engines, allowing customers to pay on a per hourly rate when the engines are in the sky.

All the while, Rolls-Royce teams all over the world are constantly reading, measuring and analysing the data sent from these engines. “An inspection can be scheduled or spare parts can be directed to the right destination even before the pilots or the airline know that one of their engines has a problem.”[2] The real beauty of this is that because of the constant surveillance and data-capturing that’s going on, Rolls-Royce now has a “treasure of engine operations data, which enables it to consult airlines on best practice.”[3] making it next to impossible for third party maintenance companies to steal Rolls-Royce business. They have made measurement the key resource to their client’s business, by treating data like an asset, not just a nice to have.

It turns out that using TotalCare actually enhances the value of the aircraft and can add up to $450K to the asset ‘blue book’ value. Once again proving, measuring your data is good for business.

Roll-Royce has now taken it a step further with their newly announced partnership between Singapore Airlines and Microsoft Azure. This is going to ultimately enhance the digital capacity in big data and the measurement thereof, leading Rolls-Royce and their partners to greater operational performance. I’d watch this space.

Now if you’re a small business you may be wondering how you could implement data measurement that’s not going to cost you too much. The simple starting point is to ask a question and look at a variable. If you’re a coffee shop, look at the data from the card payments received. What demographics are coming to your store and who are you missing out on? By starting with a simple question, imagine the insights you can generate by simply optimising the information that’s already around you. Dig in!

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