6 experts reveal how to ensure your data strategy doesn’t fail
From looking at data holistically to leveraging the hybrid cloud, six data leaders explain how to ensure your data strategy doesn’t fail
An effective data strategy will allow organisations to harness their most valuable asset and drive transformation across the business, for both the employee and consumer.
However, too often, an organisation’s data strategy will fail. There are a number of reasons for this and within this feature, six data leaders will explain the challenges and how to overcome them.
Caroline Carruthers, chief executive at Carruthers and Jackson, explains that there are two main reasons why data strategies fail.
“First, one of the most common reasons for data strategies failing is that they are not data strategies at all. Lots of organisations come up with strategies for data tech or data management plans, but unless you’re looking at data holistically, from the collection of data through to the management of it and everything in between, then you’re essentially building a one-legged stool,” she says.
The other big reason that data strategies fail “is that they don’t underpin the overall business strategy,” continues Carruthers.
“Saying that you’re going to “do data” just for the sake of it is a sure-fire way of your strategy being a huge waste of time. If your data strategy isn’t fully integrated into the wider organisation’s goals and objectives, you’re just putting together a very hollow house of cards that will eventually fall down!”
Rich Pugh, chief data scientist and co-founder at Mango Solutions, reveals that a data strategy must have purpose. Otherwise it will fail.
“A data strategy should describe how data will be used as a strategic asset, enabling a shift to a more data-centric business model. If you’re not looking to change, you don’t need a “strategy” as such,” he says.
Pugh goes on to outline four steps organisations to ensure their data strategies succeed:
1. Alignment — a data strategy must align to, and enable, your Business Strategy. If it doesn’t describe how data will help to deliver your Business Objectives, it will have little impact.
2. Defence vs Attack — a data strategy must balance data “defence” (govern, manage, secure, protect) and “attack” (use, leverage, model, share, monetise). A “defence” with no “attack” is simply data governance, while an “attack” with no “defence” is unsustainable.
3. Balance — A data strategy must strike a balance across key pillars: data, technology, culture, delivery and capability. Without considering each of these aspects, a data strategy will fail to encompass the overall change needed to succeed.
4. Pragmatism — When creating a data strategy it is easy to leap ahead and get excited about AI & ML. A data strategy should be “pragmatic” above all things, and talk in practical terms about the iterative steps needed to transform towards a data-driven future.
Simon Asplen-Taylor, CEO and founder at Datatick believes “that organisations have the structure wrong and that data scientists are being called in to solve problems they are not necessarily equipped to solve.”
Like Carruthers, Asplen-Taylor suggests that to succeed, any data strategy must be aligned to the business goals. He goes further and says that this strategy should “be written by the CDO who is a business savvy person. Someone who can align all the capabilities of data to the business: increasing revenues, reducing costs, reducing risk, increasing customer and employee satisfaction.”
On a more practical level, Asplen-Taylor explains that all “data sets need to be accessible in order to generate value. It is thought that most data scientists spend only 20% of their actual time on data analysis and 80% of their time finding, cleaning and reorganising huge amounts of data, which is an inefficient data strategy.
“Data sets need to be built, automated and deployed to an environment where the data scientists can access them.
