5 steps to demystify data in Defence

Vanessa Fernandes highlights five tips on how to demystify data and get yourself closer to the benefits it can bring, here

Data has created vast new technology corporations as powerful as any company in history; enabled huge leaps in efficiency from healthcare through to transport and retail; and transformed our day to day lives for the better. And it’s only just getting started.

Yet despite the obvious advantages to be gained from data-enabled decision making, harnessing data is far from straightforward. Across all industries, organisations are grappling with similar challenges: how is data best collected? And once we have it, how is it best analysed, stored and deployed?

The Defence industry is no different. If anything, the sensitive nature of information in Defence makes these challenges even more acute.

1) Wait – what’s the question?

Before you dive into data, it’s vital to understand what you’re trying to get from that data set in the first place – what problem are you trying to solve? Without knowing this, it’s very easy to get lost in reams of information, leading to a loss of focus and often a wasted effort.

This might be shaped by a specific key issue that you’re trying to address (‘Which capability development should be prioritised?’ or ‘Where do our strategic risks originate?’). It could be driven by departmental objectives and key performance indicators, such as a desire to increase efficiency, or by organisational strategy as a whole.

2) Manage data volume

Data can be surprisingly seductive. When dealing with it, organisations often become entranced, wanting more and more. Having vast data sets can – of course – lead to important insights, but for many organisations the problem isn’t having too little data: it’s having too much.

Everything we do potentially generates data. But not all data yields the same value. While it’s tempting to gather as much as possible and deal with its deciphering later, this merely postpones and prolongs the headache of actually deciding what you want from it. Mapping data points to the business problem helps identify which gaps to plug, in turn focusing effort.

3) Make quality your priority

It’s easy to get fixated on the analysis-side of data. But if the underlying data isn’t accurate, the derived insights can be dangerously misleading.

In the public sector, where national reputation is on the line, it’s absolutely vital to ensure the highest possible data quality. A 2019 National Audit Office report (‘Challenges in using data in government’) lists the misunderstanding of data quality in the public sector as one of its three most substantive problems.

To solve this, identifying data owners and undertaking regular reviews will result in improved data quality.

4) Consider your capabilities

Data is demanding. The process of sourcing, analysing and storing it requires a high level of expertise. New regulations such as GDPR are making the legal penalties surrounding data mishandling much tougher, making it more important than ever to have the right people and principles safeguarding your data.

And despite years of growth, there’s still a shortage of qualified data professionals. According to a recent IBM report, ‘The Quant Crunch: How the Demand for Data Science Skills is Disrupting the Job Market’, demand for data scientists will grow by almost a third in 2020.

So when developing your data strategy, be sure to understand your organisation’s grasp of the relevant skillsets. And if your in-house capabilities aren’t able to process data the way you need, external resourcing can be the answer.

5) Get big backing

It’s easy to assume that everyone will see the importance of harnessing and safeguarding data properly. But like most projects, data analytics can fail without the explicit support of senior leaders. According to McKinsey, the level of support (or lack of it) from senior leadership teams is a critical factor in the success of a data analytics project – more so than even technology or tools.

So what can you do to get them on board? First, approach senior figures as early as possible. It’s tempting to wait until you have some tangible benefits, but given that significant challenges are likely to emerge early on, their support could be crucial in initial stages.

Aligning your analytics strategy to wider business goals also helps get senior backing. Since it’s often senior leaders who develop this strategic vision in the first place, aligning your strategy can help to engage them in the project, while simultaneously demonstrating its importance to key organisational goals.

See you data

It might be tough at first, but following these steps can help you to take control of your data. So don’t be overawed or put off by the size of the task. Start small with your data efforts, fail early and learn from it, and remember that it usually gets easier in the long term.