Amit Kumar
VP AppliedInsight, Arcadis Gen
Asset management. What does it mean to you?

For many organizations, asset management in 2021 is all about building resilience. It’s about being unshakably prepared for whatever challenges the future may hold – whether those challenges relate to shifting business conditions, or to something more fundamental and extreme, such as climate change or dwindling resources.

Fortunately, resilience doesn’t require that you plough endless cash into upgrading your infrastructure. The aim isn’t to buy a whole load of new assets, but to take better care of the assets that you already have; to be more efficient, selective and informed, so that you keep your existing assets healthy and optimized.

A resilient organization has a clear, comprehensive overview of the ongoing condition of its assets. It knows which assets are at risk of failing; when that failure might occur; how urgent any repair work is; and how much those repairs will cost. The resilient organization can then provide the exact right response, at the exact right time.

To truly stack the deck in your organization’s favor, you need data science

There’s only one surefire way to achieve this level of resilience, and that’s by leveraging the power of data. All those endless spreadsheets piling up in siloed departments can’t hope to provide you with the insights you need to stay one step ahead of your assets. To truly stack the deck in your favor, you need data science.

Now, that may not be something that you’re overly thrilled to hear. But data science needn’t feel intimidatingly complicated. For any organization of any size, harnessing data is a journey – and as with any journey, it begins with a single step, and moves forwards from there. 

Start small, start simple – just start

For many organizations, getting to grips with data science feels overwhelming for one of two reasons.

A. They have only a small amount of data – perhaps just a few sparse spreadsheets – and they believe that nothing worthwhile could be gleaned from it.

B. Their datasets are relatively mature and plentiful, but they’re drawn from siloed sources. Taken together, these disjointed datasets don’t appear to offer any cohesive or actionable insights – and making them make sense together feels unachievable.  
Whatever your organization’s datasets currently look like – however underfed or disorganized they may be – they will be good enough to act as a starting point. All you need to do is steadily work through the three tried-and-tested stages of attaining data maturity.

Stage one

Firstly, you accurately map out what’s going on with your assets right now. To do this you will, of course, need to take a look into your organization’s history. If you’re a water utility, for example, you’ll need to create an overview of when and where your pipes were laid, how often those pipes have been repaired over the years, what materials they’re likely to be made of, and so on. (That’s assuming you don’t already have all this data to hand, in one place.)

Once you’ve gathered all this information then, just like that, you have your first readily usable dataset. Straight away, you’re likely to find that useful insights leap out at you, leading to smart decisions that might otherwise never have been made. And you’re still only at the very beginning.

Stage two

Here’s where science starts to kick in. With all of your foundational information in place, you now begin collating and cross-referencing datasets to reveal hidden nuggets of information regarding the likely future health of your assets.

At this stage, you may still be dealing with poorly populated datasets. These may, in turn, occasionally generate inconclusive or incorrect recommendations. That’s both a normal and essential part of the process of optimizing your datasets. Once you’ve got your datasets running smoothly, you’ll want to keep them periodically maintained and refreshed so that they keep you on track, month in, month out.

Stage three

You’re now a few months into your journey, and by this point you’re collating and cross-referencing datasets with graceful, confident ease.

By harnessing data at this level, you’re not only imbuing your assets with rock-solid resilience, you’re also bolstering your organization at every level. With key decisions now based on objective, data-driven evidence, the people that you answer to – from shareholders to regulators – will be gaining newfound confidence in your future-proofed strategizing.

Embrace the data, reap the benefits

Granted, this is very much a whistle-stop tour of the long road to data-management mastery. Regardless, it’s a journey that shouldn’t feel daunting – and a journey that’s becoming increasingly essential, for all organizations, with every passing year.

For a more in-depth overview of best practices in relation to getting started with data science, take a look at our recent webinar, Make Your Data Meaningful: Solving Silos and Creating Visibility. It’s a helpful one-stop primer for those considering data – and the resilience it brings – for their organization.

Ready to take the next step? Then you should know that here at Gen, we’ve developed a full suite of process- and challenge-specific apps to help you extract those invaluable insights from your data. You might just be surprised at what you find.


Amit Kumar
VP AppliedInsight, Arcadis Gen

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