Ameen Ali considers how to map organisational data priorities using demand management principles
In 2017, The Economist published an article on the data economy titled ‘The world’s most valuable resource is no longer oil, but data’. A Forbes Council post followed suit, talking about data being the new oil as a common refrain of the current decade. Whilst the debates on its validity continue to abound, it is helpful to think of the manufacture of data products, like oil, in supply chain terms.
That’s because just like oil, sourcing ‘crude’ data and then producing and distributing useful data ‘commodities’ takes time, money, and effort. In government, where data is used to support public services, going by revenue alone to prioritise output doesn’t help. Moreover, this ‘crude’ data is often distributed across organisational siloes and requires lots of stakeholders to locate, refine, and deploy it. So, what do we do?
Enter the data roadmap. Starting from an organisation’s data strategy that sets out its data ambitions in value, quality, and impact terms, the data roadmap matches desired customer benefits with products that use data to deliver these benefits.
Rather than taking a purely demand-centric view and flogging one’s supply chain to deliver against it, developing a comprehensive and fair set of prioritisation criteria should be the next step. These criteria – such as customer value, delivery complexity, and supply innovation – need to establish parity between internal business groups, balance the organisation’s strategic objectives against business-as-usual tasks, and be able to flex around digital transformation activity.
The organisation’s Board should endorse them, with individuals at the very top of the data pyramid becoming responsible for their application. This assumes a level of data governance as already being in place. If not, roles and responsibilities – aligned to the organisation’s operating model – of the key actors involved ought to be carefully considered.
Now for the complicated bit: establishing a mechanism for operating the data roadmap at a level granular enough to mean something to project managers and product owners. This process should consider pieces of demand in tandem with the organisation’s data product catalogue to determine which offerings to pull through to consumers.
Organisations should be maximising the use of existing post-processed products, whilst prudently assessing the impact that creating new data products would have on its resources. Such an exercise will almost invariably flush out constraints that can then be resolved in context of the organisation’s technology and people strategies.
For example, a consumer may want their data at a higher frequency to make decisions quicker. They may also want an additional set of parameters. Delivering this will involve more work to modify the data model and increase output frequency. Alternatively, organisations can consider the requirement, determine why the consumer’s needs have changed, and prioritise them accordingly. This approach might well point to other products, perhaps delivered through channels such as Application Programming Interfaces.
To use an analogy, if a consumer asks for 100 horses, supplying them with a car from your garage could be better than procuring all those horses. Moreover, the revised solution could also generate appetite with other consumers.
As my colleague Jenny Matthews suggests, a roadmap offers direction, but it might not tell you precisely how to get to your destination. One of our clients proposed that they need to use the roadmap to figure out their road trip. A perceptive insight, but how will this work?
Managing demand in a data-centric organisation calls for getting a stronger grip on the data itself, before understanding how the data strategy will support the organisation’s business strategy. After clarifying the as-is data lineage and flow, there needs to be seismic shift in the enterprise architecture, technology infrastructure, operating culture, marketing and sales functions, and portfolio and product management processes.
All the elements of the data supply chain and the underlying data architecture should be identified, simplified, and streamlined. The aim is to remove duplication, improve output quality, and ultimately equip the organisation to support multiple use cases at pace and at scale.
Delivering sustainable change
This investment should be complemented with tools, dashboards, and an operating environment that enables the continuous free flow of demand signals from customers, such that the data supply chain is responding to the latest requirements in as near real-time as possible. Onboarding existing initiatives and building maturity iteratively, using methodologies such as DataOps for example, are great approaches to take when implementing change.
A blend of data architecture, business analysis, and change management support becomes advantageous in this quest, as these bring industry expertise that can accelerate implementation and reduce rework. Involving the data consumers is also recommended, as they could offer valuable insight that might not have surfaced otherwise.
Bring the two together – a data roadmap that outlines target outcomes in context of demand, and a set of capabilities and skilled teams who understand the role of data as a strategic asset, can interpret these outcomes, advise on suitable offerings, and deliver these to predefined parameters – and you have the ingredients for a successful data road trip.
There may be unexpected roadworks or even a blizzard along the way, but you will have your alternate routes planned and your snow tires ready for when you need to deal with them.
About the author
Ameen Ali is a Business Consultant at BAE Systems Applied Intelligence
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