Data & Analytics

How to drive value from your data strategy

  • 03 Feb 2020

The most important single factor to generate value from data in a sustainable and scalable manner is to have a viable data strategy for your organisation. Viable in the sense of: feasible, funded, deployable, capable of future development etc.

No sector, and hence no company, is immune from the effects of wholesale digital disruption. But beneath digital lies data and analytics. 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.

In companies with no data strategies, executives struggle to answer questions like:

‘Do we have access to enough data, and is it of the right quality?’

‘Do we have the right data talent?’

‘Where should we start with advanced analytics?’

‘How should we define success?’

Tellingly, in these organisations without data strategies, different executives from the same company often give quite different answers to the same questions!


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.

In essence, strategy is about where to compete and how to compete, and making the prioritisation calls. In the context of data we can use this working definition: The MIT Data Board says a Data Strategy is … “a central, integrated concept that articulates how data will enable and inspire business strategy”

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. 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 business strategy.


Understanding the competitive landscape and its dynamics

To be considered viable, the data strategy must future-proof the organisation against reasonably foreseeable competitive threats and customer trends.

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 to a board 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 in their sector and geography.

“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 the “safe” basecamps of available technology or available data – fail to assess the competitive environment from new angles. Building incrementally from these existing assets, 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!

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 investment and commitment to build momentum and deliver confidence to the organisation through quick wins.


Increasingly, there are several “patterns” being recognised by consultancies to drive value from data. These patterns generally operate across sectors. Examples include:


1) Increased profit from products and services (a) new products and services (b) increased margin / value

2) Reduce operational costs (a) identification of rework and process exceptions (b) augment and automate processes

3) Monetise the data and derivatives


The Data Strategy will help determine which of these patterns are most applicable and genuine use cases which represent this pattern in your company are essential to get started. Most informed stakeholders will understand where there are limiting factors in current systems and products, so getting them to talk openly about existing issues and opportunities is very important to generate a “long list” which can be prioritised to short list.

With a clear sense of direction and priority, it’s important to sequence the capabilities the company will need into an overall roadmap. Capabilities can increase in sophistication and scale over time, in an iterative, crawl / walk / run manner. So, the roadmap should reflect this, and later capabilities can build on foundations of earlier capabilities. Plus, capacity can be scaled to provide increased coverage of business operations and revenues. Workshop sessions are a good way to bring the Data Strategy to life across key stakeholder groups by involving them in forming and shaping the Data roadmap.

Whilst business cases at an individual use-case level will inevitably be fuzzy, the more important realisation is that across a portfolio of data driven projects, returns can be quite high, even if some projects under perform. Obviously, chances of success and of material value delivery significantly increase when each project is well run in an overall programme and operationalised as part of a viable data strategy.


Author: Julian Elliott, part of our Data Leaders series.

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  • Data & Analytics