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Understanding the role of automation in data management strategies

“The bigger the better,” so the saying goes. However, when it comes to data, it’s not so simple. We’ve ended up with bigger data, but have we really got better data?

In my experience, businesses are becoming more disillusioned with the potential of big data for precisely this reason. Despite having more data, it’s difficult to extract value from it in a timely fashion. However, as with many business challenges, sometimes inspiration for solutions is closer than you think.

Approach your data strategy the way you’d approach purchasing a new car.

Buying a car is a familiar process for most of us. We know how to go about it – choosing the right features for us, working out what gizmos we need, and which are just nice to have, and then finding the best options at our price point. If you’ve got a large family and a dog, perhaps a used minivan is fit for the purpose. If you’re looking for a leisure car you’re going to want to drive for fun, perhaps you want a sports car. But there’s no point putting the muddy dog in the front of your new 2-seater or rocking up to the race track with a battered old family car. You’re just not going to get the value out of it.

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And the same is true for the data strategy you pursue. If you want to be fast and agile with your data, you need a strategy built on enabling you to do that. But, just as a car needs more than just a big engine to go fast, your data strategy needs to include more than just raw processing power. It needs a way for the entire car to work together, as one synchronized machine.

This is where data warehousing comes in – offering unified, governed, large-scale support for analytics. If you’re a business that wants to move quickly with data, having an enterprise-wide data warehouse is a must for transforming data collected from far-reaching and diverse sources across your organization into one common, universal repository of insights. However, if you are manually coding every data warehousing change, each new IT addition or your data infrastructure migration to the cloud, it’s a bit like using a hand crank on a Ferrari – you’ll spend so much time trying to get it going, and very little time actually behind the wheel enjoying the experience.

When it comes to your data warehouse, you need a way to get it moving quickly – and automation can help. However, while data warehouse automation can be invaluable in the foundation of a data warehouse project – replacing the hand crank with key ignition, it can do so much more across the entire lifecycle of the data warehouse.

In driving, we’re starting to see automatic braking, parking assistance, lane assist – all technologies designed to make the driving experience safer and easier. In a similar vein, data warehouse automation can make your big data strategy easier, cheaper and more compliant with regulations like the GDPR.

This is because data warehouse automation can work, not just with the data you’re trying to ingest from all your different business channels, your social media, etc, but also with the metadata, change management and system documentation around it. This means it’s far easier to report on exactly what’s happening to your data than it would be if you were trying to report it by hand – especially when you’re dealing with the challenges of so-called “big” data.

And much like turbocharging your engine, data warehouse automation has also been credited with boosting developer productivity in organizations fivefold. If you’re looking to move fast, within your data warehousing efforts and within your IT organization, automation can be critical.

The considerations between buying a car and investing in a data strategy are similar. It’s just a case of working out the end experience you want to achieve, what it will be worth to your organization, and the features to invest in that will put you in that driver’s seat the most quickly.

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