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DataIQ Leaders briefing – Using value creation to support priority setting on data projects

At a DataIQ Leaders roundtable in February 2022, members discussed the best methods for using value creation to support priority setting on data projects. The conversation focused on the ruthless nature of prioritisation, the importance of collaboration and the need to collect evidence to support the process.
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One approach is to align proposals to value creation, but doing so is no easy task. Data is an intangible asset, and the metrics used to value it are subjective. In some organisations, data leaders have sought to anchor its value to a measurable output such as an agreed percentage of a given ROI. Others, particularly in the public sector, seek to illustrate data’s value through the lens of cost saving.

For some, data culture and literacy are valuable assets. In a recent round of DataIQ research, more than 90% of respondents indicated that they expect to see value creation in the form of faster decision-making result from improved data literacy throughout the organisation.

At a DataIQ Leaders roundtable in February 2022, members discussed the best methods for using value creation to support priority setting on data projects. The conversation focused on the ruthless nature of prioritisation, the importance of collaboration and the need to collect evidence to support the process.

Cutting the tail

Heightened demand is a high-quality problem for data teams. Demand means that the business is embracing data, but as maturity grows it becomes important for the data office to become experts in managing broader business expectations.

One method for doing so is to communicate with stakeholders when defining key priorities. One member, head of data strategy at a large insurer, has done so my creating a steering group comprised of senior leaders from around the organisation. “We come together once a month to chat customer data, analytics and data science. Through that we can outline holistically the type of work that should be prioritised in line with business objectives.”

This, the member admits, might require a ruthlessness that doesn’t come naturally to many data practitioners. A stretched data office is unlikely to be able to deliver the kind of quality solutions required to drive the business forward. Instead, it is important to “cut the tail” by only dedicating time and resource to the requests that align with broader business objectives.

This could also provide a psychological boost to analysts, who might otherwise feel that they’re making no progress on an ever-growing tide of tasks. “Inevitably, we will get things wrong, but it will mean that we’re broadly making better and more effective choices more often,” said the member.

What makes for a priority task?

Defining priorities is a nebulous task. Plenty of time can be spent, and wasted, on attempting to create a perfect formula for prioritisation, or for allocating value to data task based off a given percentage of ROI. The reality is that the ideal approach will vary from organisation to organisation, and in many cases from use case to use case. The task for the data department is to navigate this space in a way that maximises data’s impact in the business while minimising negative political risk.

One member organisation has done so by putting the ball in the requester’s court. “For us, it really depends on how well the requester can tell their story in terms of what they’re going to do off the back of the request. Are they going to drive a campaign? Is it going to drive new customer growth, or extra demand? We do this agilely, through a casual meeting, but without really demonstrating your use case, you won’t get far with us at the moment.”

That said, a degree of pragmatism will always be necessary. Senior leaders will always have ad-hoc ideas, and stakeholders will always feel that their request is of utmost importance. Many data leaders have sought to develop the relationship between business units and the data office by embedding analysts to facilitate better collaboration.

“Data science and analytics doesn’t happen in isolation, and it’s important to be as collaborative as possible,” said one member from a large telecommunications business. “It’s a fool’s errand to try attach a certain percentage of revenue to a use case, because it becomes too transactional in that case. Invest time in your relationships to understand how your solution is going to be used to drive impact.”

Banking the wins

Once the data office and its stakeholders have settled on a steady approach to prioritisation, it is important that evidence is collected to support it. This can help to ensure that the business is holding up its end of the bargain when it comes to deploying data to maximum impact.

One member from the retail sector has done so by creating whitepapers for select use cases. “We’ve got to be careful about being too granular, and so we focus on telling stories instead. By creating a whitepaper, we can tell the story of an initiative, the various departments involved and what the end point was.” Those whitepapers, produced at the end of each quarter as part of a structured review process, can then be used to support decisions around prioritisation further down the line.

This type of approach can help the data office to shake off its reputation for being the “Department of No.” Instead, by outlining the role of data in delivering high-value business solutions, the data team can cement its position within the value chain. One member from a large publisher has gone one further by embedding data-centric goals within personal targets. “This means we can justify prioritisation by considering whether a request will help to meet those targets. Often, this means that the requester will have self-filtered before they even reach out to us.”

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