Less Juggling, More Judgement: A Framework for managing your pipeline
By Janice Carey, Independent Strategic Advisor, Data and AI
IAPA Top 25 Analytics Leaders Awards, 2022
Analytics professionals, this is your reminder to step back and understand the problem to solve BEFORE you dive in! A witty and engaging conference presenter many years ago put it well when he said “The problem is not the problem. The problem is our inability to define the problem.” There’s also a great article on creating a strong reverse-brief in IAPA’s Thought Leadership Series, with insightful guidance on proving ROI and working on the right problems.
The other “problem that’s not the problem” is how we prioritise which problems get to the top of the pile when demand always outstrips capacity.
The 2022 Analytics Impact Index study by Kearney and Melbourne Business School emphasised findings consistent with past years of the study - that companies with leading analytics ROI have high maturity in these areas:
- A focus on strategy and leadership
- Understanding the importance of culture and governance
- A fit for purpose data ecosystem (under this topic, the authors of the study noted the importance of sophisticated tools, programs, platforms and advanced technologies)
- The right mix of talent and skills
With continued acceleration of change and new ways to innovate thanks to Generative and Agentic AI, all of these have been amplified. Now we are juggling even more moving parts, and greater expectations.
Where the rubber hits the road for Data and AI professionals, is how to cut through the busyness of the everyday to drive clarity on work priorities.
A key first step is to understand where the decision rights sit. There may be different sponsors with distinct accountabilities in different domains so be sure to work with people in your internal network if you need guidance on who they are and what they care about. The Data and AI professional may not be the decision maker: but we are uniquely placed to be the agents of well-informed decisions.
Introducing the Prioritisation Grid
Here’s a framework I’ve used to achieve cut-through on pipeline management. Learned originally from some consulting colleagues, I’ve since adapted it and found it impactful for several organisations. It’s important to tailor it to your specific context, including how you name its four quadrants to facilitate decision making.
The power of transparency
The power of the framework is as a tool for communication and alignment so be ready to put in the effort to do the cycles of socialisation, feedback and refinement of the grid and how it will be used. The benefit for the organisation, and time poor decision makers is aligning with their peers to agreed guardrails. This reduces friction and enables us to move quickly to the substance of the matter when trade-offs are being weighed. The benefit for Data and AI professionals is clearer direction, fewer false starts, more meaningful work. Best of all for practitioners, it strengthens line of sight to the meaningful impact of our work.
Tailoring your Prioritisation Grid
Why score out of five?
An odd number scale is deliberate to ensure good separation of items (problems / opportunities) to facilitate decision making. Five tends to be a happy medium - neither over simplified nor weighed down by so many factors that assessment bogs down in detail.
What rubric do I use for Business Strategy Alignment?
A classic way to score this axis is to map it to the organisation’s strategic objectives. There may not be exactly five, so some judgement will be needed, with help from whoever in your organisation sets and interprets the business strategy.
What rubric do I use for Exploitability?
Exploitability considers both readiness of the organisation to exploit the business opportunity and key resource availability. As a hypothetical example, a company might score one each for:
- We have the business appetite to pursue this and there are clear on success measures
- Specialist business and technology human resources are in place and have capacity
- Enabling technology is in place
- The data exists
- The data is accessible and of sufficient quality
Populating the Grid
When mapping the grid, particularly the first time, it's useful to populate the matrix as a facilitated workshop with the champions who know the problems well. The dynamics of the organisation are your best guide on doing this with the decision makers directly, or their delegates, for subsequent review and validation.
Have participants place each item (problem or opportunity) on the grid according to the rubric.
Good facilitation is important here. Encourage discussion on these points:
- Not all items are going to have the same alignment to the organisation's strategic goals or the same degree of exploitability
- There may be some highly exploitable items that measure low against the strategic objectives of the organisation which may raise the question ‘why would we bother doing this?’
- Alternatively, you may have an item that is highly aligned with organisational goals but a low degree of exploitability. This may then raise the question ‘how can we increase the exploitability of this item given its high value to the organisation?’
There may be items where the exploitability is unknown, record these beside the graph so that they are not lost and do the work offline to resolve their mapping.
Priority setting and governance
The mapping isn’t the decision. It’s the framing for deeper consideration by decision makers to inform their choices about:
- What problems to prioritise and confirm success measures
- Are any business cases needed or is this low risk, low cost or capacity funded
- Any experiments to unlock other possible value
- What problems to cull
- Problems to carry over to a future cycle when reviewing the pipeline of work
Think about what forum may already exist as a logical home for these decisions. Talk to the meeting sponsor for that forum about this becoming a regular agenda item and what frequency makes sense. Work out the colleagues that advise and review during each cycle to make the most of decision maker time.
Armed with this guidance we build out the sequence of value drops, considering technical and business dependencies. This becomes the roadmap for review, feedback and execution.
Keeping it fresh and building momentum
Few organisations go long without monitoring objectives, identifying changes in the market or discovering emergent problems to solve. Even mid-course corrections as interim value drops reveal new insight. That’s why the Prioritisation Grid isn’t a one-and-done. It’s a communication and review tool to govern and guide the pipeline of work and build a virtuous circle where each piece of value builds on the last. You can:
- Gain air cover with decision makers
- Understand and clearly articulate trade-offs when a pivot is being considered
- Create shared language across business and analytical disciplines
- Lean into constructive two-way challenge
- Strengthen business context within data and AI teams to solve more problems
- Drive transparency for the wider organisation to spark curiosity and uptake
One of the most energising parts of working in data and AI occurs when we can see our work deliver value in the eyes of our customers, communities, business, shareholders, colleagues.
How might you use this framework to magnify your impact and take your organisation up the analytics impact leader board?
About the Author
Janice Carey is a leader in data and AI. She partners with business leaders to drive innovation, performance and results in ways that are adaptable to change in a fast-moving world. Janice is currently a strategic adviser and speaker. She is the former Head of Advanced Analytics and AI at Coles, and a past advisory board member of the Centre for Business Analytics at Melbourne Business School. She has headed up Data Strategy, Data Science, and Customer Analytics teams at Bupa and Monash University. Prior to this she held senior appointments at IBM including with the Strategy and Analytics practice.
Janice’s earlier career experience in factory automation and operations management informs a strong connection with the “so what” of the real-world processes that generate data and deliver business value. Janice holds an MBA (Melbourne Business School) and a Bachelor of Engineering (Monash).