Skip to content


  • Add this article to your LinkedIn page
  • Add this article to your Twitter feed
  • Add this article to your Facebook page
  • Email this article
  • View or print a PDF of this page
  • Share further
  • Add this article to your Pinterest board
  • Add this article to your Google page
  • Share this article on Reddit
  • Share this article on StumbleUpon
  • Bookmark this page

The anatomy of data strategy

Elliot Rose, digital trust and cyber security service expert, and Adam Stringer, data privacy expert at PA Consulting, are quoted in a series on the anatomy of data storage.

Part 1: The Starting Point

Through a four-part series, GDR breaks down the key components of a successful data strategy – especially its creation and implementation. Part I looks at kicking the process off.

Behind the wall of cliché and spin associated with data-driven transformation lie two fundamental truths. One concerns the eventual aim, which is to make better decisions. The other is about the process – which is not about technology or regulations, but change management.

Few companies make big decisions about the direction of their business without first setting out a thorough strategy; to become a data-driven organisation therefore requires a data strategy, and the will and expertise to implement it. That in turn requires input and buy-in from across the organisation, especially the board – as one observer notes, a chief data officer (CDO) without the support of senior management is likely to encounter resistance and “political games”.

The questions to be answered relate broadly to the way data is monetised. Organisations must assess what data they have; whether they can use it in both a legal and technical sense; whether they need to collect more; how they can organise it and prepare it for analysis; how to analyse it; and finally how to make a decision based on that analysis. There are a multitude of concerns at each of these stages.

Making a start

Some believe defensive and offensive data usage are not necessarily distinct. Elliot argues it is “dangerous to consider the two separate”. “If you can show customers that you look after their data, then you have a better opportunity to make use of that data,” he says. One of Elliot’s clients, a large car manufacturer, he says, “sells cars today, but if they get [their data usage] right, they have a lot of other opportunities going forward. One can help drive and support the other.” 

Elliot notes too that in the pharmaceutical industry there are big issues around data and privacy, but once companies in the industry have gained the trust of their customers through strong data protection and governance policies, they are more able to pursue innovations like personalisation – a hugely important topic in the healthcare world.

For Adam the question is about who or what will benefit from data exploitation. “Is it just the bottom line? If there’s no value proposition for the customers, it’s unlikely to succeed. Internally, people have to translate that value proposition into an incentive for them.”

Part 2: Goals and Targets

Once organisations have committed to formulating a data strategy and taken the first steps, an important second stage is defining and formulating the aims of the strategy. Data leaders must decide, based on the unique needs of the organisation, what the strategy intends to do and how it will benefit the business. This is likely different for every organisation, can be different even within an organisation – and is likely to change over time.

There are tools that can be used in this endeavour, as well as important overarching principles that can help ensure its success even when circumstances change. Once the targets have been defined, organisations can start to see results which truly bring new value.

Goals and targets

One of the first steps Elliot takes when assisting clients is to carry out a data maturity assessment, he says, in which he assesses things like technical architecture, leadership and governance regarding data usage. “We look at and understand those dimensions: is there a business strategy around this, is there a governance process? Looking at business processes – we find that people tend to look at the data driving the existing old ways of working. That can be turned on its head, we can look instead at the new opportunities.”

Part 3: Data latency

Data latency ties in to one of the fundamental aims of a data strategy: better and faster decision-making. For many businesses, particularly those that are looking to gain better insight from data, rather than profit directly from it, data latency is hugely important. Being able to make near-real-time decisions is key, observers say, particularly when it comes to added-value services like personalisation.

Improving data latency isn’t a simple task, according to the experts – and is likely to be a long-term project that comes after the formulation of a clear data strategy and decisions about what exactly are the goals of the strategy.

Part 4: Technology and Success

Previous parts of this series have looked at the way in which a data strategy is formed, the people who form it, and how to communicate the strategy among the business in a way that will hopefully ensure its success. The series has avoided an undue focus on technology. For many of the experts GDR spoke to about data strategies, the move towards a data-driven organisation implied many considerations, with technology being just one of them.

But technology is nonetheless important, and the way it is used and viewed in relation to a data strategy can be key to the ultimate success of data projects. Observers note in particular that poor technology infrastructure can be a barrier to success, but also say the notion that a technology product can be relied upon to turn a company into a data-driven success story should be refuted. According to the experts, it is simply one piece in the puzzle.

Technology and success

Many observers are so keen to stress that the challenges are not purely technological that the issue is sometimes shunned. The ability to make fast and accurate data-driven decisions in any modern business is, of course, intricately linked to its ability to harness technology. That being said, the case for data strategy to be considered distinct from technology strategy is a strong one.

Elliot says that when he carries out data maturity assessments, technical architecture is “at the bottom”, while leadership, governance and business outcomes are at the top. And clients have often attempted to squeeze value from their data but not seen much success because of an undue focus on technology.

Elliot continues: “We get called in on a lot of occasions when organisations have tried to move to an environment where data is a corporate asset. We go in after they’ve bought the technology and spun it on its head. The technical architects had designed it from a technical point of view, but they weren’t in the business process or business language. It’s about outcomes."

In an ideal world, Elliot says, “you want the change management stuff to happen upfront. You have a lot of examples where organisations spent a lot of money on technology and it just doesn’t happen. In the majority of cases, the senior team don’t buy into it, and think it’s a technical issue.”

Read the full Global Data Review series here

A global movement towards increased data privacy is changing the way companies do business. Are you ready for the new era of data privacy?

Read more



The Integrated Review of Security, Defence, Development and Foreign Policy

Discover our insights

Contact the data privacy team