Julian Elliott: Why your organisation needs a viable data strategy

About the author

Julian (Jules) Elliott is an experienced Chief Data / Analytics Officer with track record of successful data-driven transformation across multiple sectors. He has worked with Direct Line Group, British Gas, Lloyds Banking Group, L.E.K. Consulting and began his career with Schlumberger.

Most recently, Jules was Chief Data Officer at Dentsu Aegis Network.

Why your organisation needs a viable data strategy

No sector - and hence no organisation - is immune from the effects of wholesale digital disruption. But beneath digital lies data and its manipulation. As a result, there is an urgent need for Executive teams to develop a viable data strategy - and indeed place it at the core of their corporate outlook. Those that don’t will inevitably be left behind.

C-suite surveys undertaken by organisations like Gartner are revealing, for example:

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For companies without viable data strategies, executives struggle to answer questions like:

  • Do we have access to enough data?

  • Do we have the right data talent?

  • Where should we start with advanced analytics?

  • How should we define success?

Tellingly, different executives from the same company often give quite different answers to the same questions!

As Dan Ariely (Duke University) famously said, “Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”

Finding a cohesive approach

Most organisations already have multiple data-intensive initiatives underway, often in several different organisational “silos”. But fundamentally, the lack of a coherent enterprise-wide approach (aka a viable data strategy) limits the ability of this disparate and tactical set of projects to deliver the meaningful and scaled results the board desire to see.

In essence, strategy is about where to compete and how to compete, and making prioritisation calls. In the context of data we can use this working definition:

Data Strategy is … a central, integrated concept that articulates how data will enable and inspire business strategy
(source: MIT Data Board)

From this definition, it follows that a company’s data strategy needs to provide clear direction and answer key questions around how a company will generate value through data.

Most importantly, data strategy does not exist in a vacuum. Rather, its goal is both to support and enhance corporate strategy. It not only helps to achieve near-term business goals but generates new and valuable data-enabled options for the future.

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Taking stock of the competitive landscape

To be considered viable, the data strategy must future-proof the organisation against competitive threats.

Typically, in well-established organisations that have a long history of success, the corporate strategy may implicitly assume a slow / gradual change in the competitive landscape.

However, one of the first and key benefits a considered data strategy can deliver is a fresh and fundamental reconsideration of these assumptions. A data-led perspective should help companies become aware of, anticipate, and even lead disruptive processes.

“Face reality as it is, not as it was or as you wish it to be”. Jack Welch

Conversely, many apparently “low risk” data strategies – those that start with “safe” basecamps of available technology or available data – fail to assess the competitive environment from new angles. Building incrementally from these existing basecamps, these data strategies remain rooted in today’s reality and limited by existing capability. Adopting approaches that appear conservative in fact turns out to be high risk!

“Even if you are on the right track, you will get run over if you just sit there” – Will Rogers

Don’t delay, start today!
A viable data strategy is also one where deployment can – and should! – start today. The emphasis should be on immediate action – not waiting for next year’s budgets to be set or the next financial or calendar year to start. Hungry start-ups and the likes of Facebook, Amazon and Google won’t wait until the start of next year. This means sufficient resources (talent, tech, funds, etc.) must be allocated.

“The world is changing very fast. Big will not beat small anymore.
It will be the fast beating the slow.” – Rupert Murdoch

Viable data strategies can rapidly become self-funding. They can contribute financially by generating new revenues, or saving costs: returns ranging from 2x to 10x are not uncommon. Nonetheless, nearly all data strategies will require some level of initial investment to build momentum and deliver confidence to the organisation through quick wins.

Because data is needed right across the organisation, it is often the case that no one existing executive or department has overall responsibility for data and data strategy. For this reason, it is critical that data strategy has sponsorship and funding allocated by the Executive team right from the start.

Important initial investments might include:

  • People: Chief Data Officer or similar leader and supporting team or centre of excellence

  • Technology: funding for big-data tools and development / production environments

  • Data: data quality and data governance, licensing, data protection initiatives etc

The need for data leaders

The number one piece of advice from the most advanced players in driving cross-organisational value from data (e.g. IBM) is to hire senior talent in the field as a first step.

A strong, experienced data leader – whether permanent, interim, or consultant – can create (or update) a data strategy within 60-90 days, provided that they have the support and participation of the Executive team and a clear mandate to “rove” across the business.

With great data leadership in place, even the first iteration of a viable data strategy will bring sufficient clarity to identify the key strategic issues (eg. capability gaps, future competitors etc) and get analytics kick-started in areas with promising returns.

When you have the right talent, and a viable data strategy, it does not take long to move from ‘disrupted’ to disruptor.