What can you achieve with the right data engineering strategy in place?

An obsession with ‘data’ isn’t digitally mature - in fact, it’s a sign that you’re trying to run before you’ve even learned how to walk. However, thinking about the intersection of data and engineering is far smarter, and a sign that you’re heading in the right direction when it comes to a broader organisation-wide data strategy. Indeed, it might not feel like that - but an awareness of all the things you don’t know, and the things you still need to put in place is going to be far more beneficial than a mix of enthusiasm and hubris.

But what should the end result look like? Where are you trying to get to? That’s something that the more cautious and digitally mature organisations often fail to really reckon with. In some senses, they’re so concerned that they do things properly, securely, correctly, that they lose sight of the end goal.

True - maybe there isn’t an end goal when everything will be complete. But any good strategy has a vision of how things will look once you reach a specific milestone or have completed a particular project or sustained piece of work.

In this post we’ll explore the five achievements that will underline that your data engineering strategy is successful. This is useful in two ways: it allows you to set a vision, but also gives you a bit of a roadmap for the focuses that a data or data engineering project needs to include.

You’re able to collect and process real-time data

First, A caveat: real-time data isn’t always necessary. But when thinking about data engineering, you should at least consider your ability to manage data in real-time. Such is the nature of today’s digital landscape, that any latency in the use and analysis of data can feel jarring - facilitating the movement of data in a way that accords with wider expectations (from customers to senior management) is incredibly important.

This means building an infrastructure that is able to handle speed alongside scale is important. It isn’t, of course, easy, and it requires some level of technical and architectural sensitivity. However, with the right people in your team set up to actually tackle these sorts of problems (rather than constantly firefighting) you’ll be well set to embrace a world where real-time is the norm, not simply a nice-to-have.

Read next: The SPAM framework: a tool for building better data strategies

You’re able to use data in products and services

Every organisation uses data. Let’s not pretend it’s new or exciting anymore. If anyone in your organisation uses a spreadsheet, you’re data-driven, to some extent at least. Where things are different is when you’re able to use data in products for the purposes of personalisation and recommendation.

This might feel ubiquitous today, but it’s worth acknowledging there are still a vast number of businesses out there that are failing to offer really personalised and finely honed customer experiences. That’s bad for them - but it’s a great opportunity for you.

Of course, actually building the mechanisms that enable this can be complicated - it also adds an additional layer of complexity to your applications and digital properties. This means you’ll need developers and engineers that are able to deliver consistently, but also recognise how the various components of an application are (or aren’t) working together.

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All parts of the business are enabled by data

This comes back to the earlier point about real-time data. It’s not just the scale of data you’re able to handle, but also the speed at which it can be processed and transformed into something useful, interpretable, and actionable.

While it’s one thing to accelerate your data pipelines, you also need to build the infrastructure and culture to ensure that it can make an impact across an organisation. There’s no point putting together a sophisticated data engineering project only to find that it’s keeping, say, a single data scientist happy.

True, this requires more than just a robust data engineering program. But it nevertheless emphasises the point that there are leadership initiatives that need to be happening alongside any infrastructure projects that will maximise its impact.

Your infrastructure is affordable and aligned with your business goals

Yes there are architectural patterns and best of breed products. But every cloud or data infrastructure is unique to the organisation that developed it. Sometimes it’s built in a very intentional way. But more often than not its evolution will look somewhat organic (ie. unplanned and just a little chaotic).

It’s not always easy to avoid this chaotic approach - even with the best will in the world. But it can be costly and limiting to a business. A good data engineering strategy will be able to be intentional in how something should be built. Indeed, it’s critical to get the right team in place for this - people who don’t simply have a checklist of experiences and skills, but people who are able to think critically about architectural and engineering decisions and explain why something should be done.

Even more importantly, all of this should be aligned to business goals - where do you need speed? Where do you really need accuracy? Where does reliability and availability matter? You can’t do everything - but you can balance competing priorities and needs to deliver the most benefit.

Your team is engaged and bought-in to the project

Finally, you can tell how successful your data engineering initiatives are by talking to those actually doing it. Are they happy with the work they’re doing? Do they feel the wider organisation respects and recognises the work they’re doing?

This is important culturally of course. But in terms of the future, ensuring you have the right people in place, and that they’re committed to the goals of the business means you can avoid the pitfalls of high turnover and disengagement.

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