1. Managing stakeholders and partners
The basis of a well-run analytics function with positive engagement across its stakeholder base is to establish a clear process and ensure it is kept to. At the heart of this process is the way that project briefs are captured, defined, managed and realised.
In the early stages of the maturity of analytics, demand will outstrip supply. While analysts, managers and their leaders are always tempted to respond positively, even if it means operating at over-capacity, this is not sustainable in the long-term. A system of scoring projects needs to be developed in partnership with stakeholders which allows the workstream to be properly ordered.
Key dimensions of the scoring system include:
- business deadline
- business impact
- analytics resourcing required
- analytics timeline
In this way, the briefing and prioritisation process becomes a properly-managed, business-like negotiation where both parties understand and have visibility of what is being agreed. Collaboration tools can be deployed at this point to ensure there is visibility of the workstream and the priorities which are ahead of any new project in the queue.
2. Leading the analytics team
A typical characteristic of analysts is that they are very left-brain in their thinking – or “blue people” in colour psychology terms. This can mean they adopt a very binary view of projects and the options for how they can be realised. If an initial scoping exercise shows a timeline of ten weeks, for example, they can see this as fixed regardless of the resourcing applied or any flexibility within the specification.
For the analytics leader, this creates a challenge, but also an opportunity to make a real impact on the workload. A good example is around data conditioning, which typically absorbs a lot of analysts’ time at the start of a project. If data management and data engineering can be applied before the project is adopted – this may be a condition of accepting it into the workstream – this will accelerate throughput.
Similarly, analysts are often adverse to conflict and are therefore vulnerable to “just do it” demands, especially where these arrive via a senior business leader or a stakeholder using seniority by proxy. This is where soft skills development can provide particular benefits in building confidence and learning how to negotiate without conflict.
Leaders have a significant part to play here, not only in the provision of such soft skills training, but also by helping stakeholders to think laterally about their project, for example, by breaking it into smaller components, some of which may be able to be delivered rapidly while the more complex elements are worked on.
Perhaps the biggest challenge for an analytics leader is when a project does not confirm a stakeholder’s hypothesis or even reveals something negative. In this situation, the analyst needs to be sure that their leader has their back, or that they can call in the leader to deliver the bad news. Many stakeholders are unrealistic about what analytics can achieve or are highly-invested in an internal business myth. Giving a result which does not meet those expectations or which contradicts a passionately-held view is a real test of leadership.
3. Setting a long-term vision
With the constant demands and pressure from business-as-usual requirements, it is easy to lose track of what the analytics function would like to become and what it wants to achieve for the business, even if innovation projects are being tackled, since these can also be ad-hoc in nature. Even a well-run and effective analytics function needs a long-term vision to keep its focus and enthusiasm.
A vision should be aspirational in nature, yet realisable. The function leader needs to communicate it to the team clearly, concisely and often. Part of this will involve sharing successes and outcomes that align with the vision both internally and externally. Done right, this conditions the organisational view of analytics and the contribution it makes, making it easier to win further budget, resource and support. As one leader put it, “data and analytics should be the kings of evidence”.
The scope of the vision should include moving the analytics function from reactive to proactive in its engagement with the business, showing what opportunities exist and just how much impact could be achieved. For most functions this is an ideal state – few have managed to realise it yet.
4. Challenges to the vision
“Why are you the only function that is allowed to say no?” That comment was made to one analytics leader by a stakeholder frustrated at not getting their projects accepted into the workflow. While it clearly overlooks one major gatekeeper that routinely says no – finance – it does reflect pushback which the analytics function needs to be prepared for.
The appropriate response is firstly to understand what the issue really is that is being complained about, then seeking to resolve it. Often, a stakeholder complaining in this way may not be fully clear about the process and how to get a better score for their project. Working with them to ensure they are completely across it should lead to this block being removed from their path and a constructive relationship to ensue.
Success can also lead to its own problems, most notably an increase in demand as more lines of business seek to get their own projects adopted. This should come with a commitment for more budget and resourcing to support higher workloads. However, if this is not triggered centrally, a cross-charging approach may need to be adopted, but this usually also requires a switch towards service-level agreements. Adopting this kind of transactional relationship can feel antithetical to the proactive, engaged, supportive approach which the analytics function has in its long-term vision.
Conclusion
All business functions sit in an internal eco-system to which they contribute and derive benefit in turn. This is particularly true of analytics since it only exists to serve other functions, rather than having a purpose in its own right. So it will naturally come under pressure to respond to demands in a willing, timely and unquestioning way.
While a positive relationship is important. analytics must not become simply a service function with no goals or agency of its own. To this end, the introduction of a well-structured and rigorously-followed process will bring under control what might otherwise become an overwhelming flood of projects. From there, analytics can start to pursue its ultimate goal of bein a proactive, leading function that drives value, rather than just delivering insights.
