Background
Data and analytics (D&A) continue to attract investment as organisations either take their initial steps by establishing their first function or, at the other end of the spectrum, seek to bring disparate groups together into a data and analytics centre of excellence (DACE). Yet at the same time there are downward pressures on this function as some organisations seek to bear down on salary inflation or even break up their DACE.
As a visible cost centre, any enterprise-wide cost-saving exercise can easily identify D&A for cutbacks or recruitment freezes. During a recent DataIQ Leaders dinner to discuss the subject of how to make the business case, it was reported that one manufacturer has constrained the cost base for this function while another retailer is replanning its centralised model towards a federated structure.
So what are the core elements that keep the board backing D&A? Are teams taking the right measures of the impact so they can show a clear return on investment? And how closely aligned is this to embedding data and analytics into the DNA of the company so that other functions continue to demand that this resource be made available to them?
This white paper summarises the thoughts of DataIQ Leaders from that dinner table discussion and synthesises them into stable, universally-applicable concepts.
Four bases for business cases
As a general rule, the argument for investment into D&A (as with any other programme)breaks out into one of four arguments:
- to invest in technology;
- to invest in headcount;
- to invest in toolkits;
- to invest in innovation.
Each of these has a different principle and set of risk and success factors at its heart which need to be carefully considered.
1 – Technology investment:
Relatively straightforward as long as the use case can be substantiated and incremental gain demonstrated from automating a process or updating a technology platform used by D&A. A strong business need should play a central part in the argument with clear before and after measures applied.
- Principle: business enabler
- Risk: poor or incomplete metrics
- Success factor: demonstrating business returns
2 – Headcount investment:
Harder to argue because of the increase in costs which directly impacts on the bottom line. It should be recognised that there is even a personal dimension to this since senior executives often have their bonuses linked to the P&L. The focus of the business case should be its impact on incremental revenue, cost reduction or improved customer experience for which additional human resources in D&A are essential to manage the extra load. Involving stakeholders to support the argument for extra headcount is valuable. As one DataIQ Leaders member said: “Try to get them to do the ask.”
- Principle: supporting transformation
- Risk: getting the business to agree on the target and baseline
- Success factor: engaging stakeholders
3 – Toolkit investment:
This is a strategic business case, rather than one focused on incremental gains, which identifies new IT-supported capabilities, such as moving into the cloud or creating a big data platform. The investment required is large and the return is not always evident within the project itself. A good area is to focus on the replacement of sub-optimal tools, such as internally-built applications that have been superseded or external solutions which are no longer supported. Sometimes this case can be made more powerfully in the wake of a project failure where the business needs to rethink its strategy.
- Principle: strategic development
- Risk: business has had its fingers burned
- Success factor: new generation replacement for legacy systems
4 – Innovation investment:
Usually a less significant level of investment, innovation is essential to move the business forward and offers analytics the opportunity to find breakthrough solutions. In some businesses, innovation lives within a specific research and development (R&D) department. Where this is not present, the argument should be for a specific percentage of programme budgets to be set aside for exploratory projects, generally between 5% and 30%.
- Principle: future-focused, not business as usual
- Risk: often does not produce deliverables
- Success factor: comparison with sector/industry competitors and disruptors
Winning approaches for the business case
Financial specifics: all business cases are competing for the same investment pot, so the argument has to be made in the language of the business (ie, financial). While costs are relatively easy to identify, benefits can be harder to prove as they happen downstream from D&A, often without direct metrics from any process change. Putting a number – any number – on the return on investment is essential regardless.
Regulatory change: as has been seen recently with MIFID in the banking sector and GDPR across all organisations holding personal data, changes to the regulatory landscape unlock investment budgets. Moving early with a case that addresses new regulations, while also building new data foundations, is a shrewd move – but one which can only usually be made once.
Executive focus: understanding what the board and C-suite are concerned about (through vision statements, internal comms and the like) creates leverage for a business case. New faces around the boardroom table are often prime moments for new business cases to be made, especially if an incoming CEO confirms the importance of data and analytics.
Stakeholder cheerleaders: as noted above, having a representative from a line of business argue for the need to invest in data and analytics is highly persuasive. This may provide a base for the whole investment needed, or it may be possible to create a coalition who each agree to provide support and “donate” a percentage of the benefits that accrue as proofs of ROI.
Simple solutions: successful business cases are often those which the business finds easiest to imagine being delivered. A focus on simple solutions for pressing business problems is more likely to win backing as a result.
Obstacles for data and analytics business cases
Outside the C-suite: in most organisations, D&A does not have a voice on the board and in many it does not even have an advocate. As a result, it can be easier for investment to be directed towards projects with which the business is more familiar.
Fear of change: humans tend to view change as a threat, so making the case for a D&A project which might transform processes, replace jobs (as with artificial intelligence and automation) or even reduce bonuses may struggle to win support.
Behind the curve: while awareness of new techniques and tools, from big data through to AI and machine learning, may appear to be helpful for D&A investment, it is often too late to embark on such projects by the time the C-suite has absorbed the concept.
Business myths: all organisations have beliefs about what makes them successful. Data and analytics often contradict these myths with an evidence base for alternative explanations. But these organisational shibboleths tend to serve as the bases for political power which other leaders may not be willing to give up.
Too clever by half: even in data literate organisations, it is possible to propose new business projects which are simply over-complex or unlikely to be capable of being operationalised. Boards tend to take a helicopter view of the business and what will help it, with the consequence that they have a low tolerance of anything which can not be explained simply.
Conclusion
The skills and tools required to make a successful bid for investment into data and analytics are essentially the same as those needed by a leader to run the function successfully – technical skills (business analysis, financial modelling, data analysis) and soft skills (stakeholder engagement, communication, political nous).
In this respect, a D&A function leader with ambitions to grow the operation and embed it more deeply (and permanently) into the organisation would do well to ensure they have appropriate training. Just as their direct reports and teams benefit from networking with peers from other companies or industries in order to gain insight into what is currently possible, so should the leader be involved with a network that can help to provide examples and proofs.
Similarly, while technical skills will typically have been learned both academically and on the job, the soft skills required to thrive can be harder to learn. This is where the programme offered by DataIQ Leaders can be highly-effective, from workshops such as Stakeholder Management and Communication Skills, through to the peer-to-peer knowledge sharing over dinner which fed into this whitepaper. Our 2019 programme can be viewed here.
