With data and associated processes around data having historically been an IT function, it is easy to see why, as data permeates and proliferates the business world, data strategy has naturally fallen into the IT domain.
While IT has the vast know-how and technical expertise to ensure excellent execution of a data strategy, many data strategy investments fail to deliver on their promised return on investment, because the initial process of defining the ‘what’ of a data strategy has not been business-led.
When talking about data strategy as a technologist, terms such as data integration, data cataloguing, metadata management, data lakes, big data and data governance spring to mind.
Rather than being a ‘what’, I would argue that these processes and concepts are the ’how’. Too often, IT-led data strategies dive into the how, without enough initial and ongoing investment in understanding what data and information is required.
The result is a divergence of the business need versus the execution of data management, eventually leading to many business units working outside of the strategy in order to meet their information needs, creating the vicious cycle of data silos, lack of data integrity, several versions of the truth, and ultimately a failed data strategy.
A data strategy is as organic as a company: growing, changing and moving, responding to environmental and internal forces. Rather than being seen as separate from a business strategy, a data strategy should live symbiotically with the business strategy.
To quote Thomas H Davenport: “Every company has big data in its future, and every company will eventually be in the data business.”
This is an organisational mindset change where business has to understand that data is the key to executing successful business strategy, and IT has to align their data efforts with the value they will add to key business goals.
Paramita (Guha) Ghosh, in his article on data strategy versus business strategy, talks about the importance of linking data to business impact. He says: “In many cases, business leaders and operators fail to map their available data use cases with actual business needs, and this is why even the most planned business strategy fails to yield the expected results.”
Rather than being seen as separate from a business strategy, a data strategy should live symbiotically with the business strategy.
In my experience, data and information strategies have succeeded where their definition is spearheaded and sponsored by senior people with a solid understanding of the business, and the direction in which the business wants to go, who also live and breathe the concept of data-driven business decisions on a daily basis.
This does not mean that all senior business executives should be data scientists – rather that they are able to use self-service BI (for which they have driven the use case), or formulate their questions about business performance and opportunities in a data-focused way, allowing data scientists to do the necessary manipulations to answer them.
This points to a need for data literacy across all levels of the organisation. The definition of a data strategy has to touch all levels of the organisation, and ensure there is a plan in place for business people everywhere to know how to use data to achieve their goals.
Hal Varian, chief economist at Google, said: “The ability to take data – to be able to understand it, to process it, to extract value from it, to visualise it, to communicate it – is going to be a hugely important skill in the next decades.”
I’ve seen businesses where data is readily available, but simply isn’t being used to its potential because the people in the business don’t know how to harness it. This is usually a fundamental failing of purely IT-led data strategies – there is a chasm between IT providing self-service business intelligence, and business understanding how to use data to drive business decisions.
In order to bridge this gap, the data team must have an equal weighting of data and business skills. I’ve seen this work really well where teams are comprised of business analysts (each responsible for a different business function) with data skills, supported by technical data scientists and developers who help with complex transformations and predictive algorithms.
This establishes consistency across a data strategy, confirming that IT best practice is adhered to, while ensuring unique business function needs are met by someone who speaks the language of the business, and understands their ‘why’.
In summary, data strategy should be driven by a combination of business and IT people, with the weight of effort being distributed differently at different stages in the process.
Perform data projects that can directly be linked to business goals, and ensure the business is empowered to use it on a daily basis.