Further adventures on my quest for a data culture

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Content Copyright © 2018 Bloor. All Rights Reserved.

I am indebted to Accountagility, the vendors of the ORYX Financial analytics suite, and to Hans Gobin of the FP&A Board (the global think tank for senior Finance professionals) for the opportunity to attend a breakfast briefing session on achieving operational excellence in Financial Planning and Analysis. I have long felt that there is too much emphasis given to the technological element of a solution, and this has been to the detriment of recognising the significance that people and process play. It was encouraging therefore to spend time with others who share that sentiment.

A new model for finance

The FP&A Board has developed an analytical maturity model, based on the familiar Carnegie Mellon model of maturity stages, so it is a proven robust framework and one which is readily understood. The model has twin aims:

  • Firstly, it enables enterprises to objectively benchmark where they stand, and;
  • Secondly, it provides a guide to achieving the higher levels of maturity by following a sensible stepwise approach.

The latter point is very important. I see far too many people buy sophisticated technical solutions, when they are at a very immature state of preparedness to use the solution. They then plan to move from “laggard” to the “bleeding edge” in a short space of time, and ultimately profess amazement when it fails to achieve the stated outcomes.

From a survey of their members, the FP&A identified that 88% are still at the stage of using analytics to look in the rearview mirror and report on what has happened. 72% use that rearview to go on to explain why the historic result is what it is. Only 52% attempt to predict future outcomes, and that falls to 40% who look to define prescriptively the actions that are required to achieve the outcome.

Moving to deep thought

Noting that in most organisations, the finance department is a more advanced user of analytics, with more staff comfortable with using numeric analysis than is the case elsewhere, these numbers are what you would expect and at the high end of what is generally seen.

By looking at how time is spent on analytics they saw that the predominant use of time was spent gathering data, preparing it, and then running through the analytics tool (often a spreadsheet). This is low “value add” work which then squeezes the little time available for highly skilled domain experts to actually use the output to interpret the right results, and then to define actions based on them.

Again this is a pattern that is seen throughout enterprises. Even those working at the leading edge of machine learning and deep learning show the same issue, with their time spent predominantly on reaching the stage where things are in a good shape to actually model, and comparatively little time to add really high value thought-based interpretation. That gives pause for thought in a week when the hero of deep thought Stephen Hawking passed away.

How the model works

As all enterprises now face unprecedented levels of disruption, which form an ongoing competitive threat; this use of highly skilled resource is not sustainable in the long term. We all need to achieve higher levels of maturity and use our skills to better effect.

What the maturity model shows us is that at the Stage 1 Basic level there are no formal processes, no established measures, few tools, and no collaboration with people operating on best endeavours across silos. This is the world of the start up and hopefully a stage people can pass through rapidly on the way to greater control and effectiveness.

At Stage 2 the process is starting to be Defined, albeit in an inconsistent fashion, there are a few common tools, often shared spreadsheets, a few basic shared measures and definitions. Collaboration remains relatively low; people manage their own patch and only bring things together at summary level. It is laborious, it’s difficult, it lacks automation, people do it because they have to and don’t feel they derive great value from it. They get a picture of what is happening, but too far after the fact to do anything about it. This is where a lot of organisations sit.

By Stage 3 things are starting to be Better Defined. There is a defined planning process, the data is defined and is starting to be managed as a corporate asset. That allows people to share and work collaboratively. Things are getting done, but tend to be heavily reliant on IT, so people don’t feel they own the process and complain that things are too ponderous and difficult to change. People are starting to see what is possible and find it frustrating.

Stage 4 is when things start to Advance. There is multi-dimensional analysis, the drivers are universally agreed and applied, results are shared and understood across the enterprise. Much of what is done no longer requires constant IT intervention; instead IT is delivering the enablers in a well-managed timely fashion, which allows that self-service model to operate. People are using the analytics on a more day-to-day basis to gain insight and understanding. Few are here, but many companies have this as an aspiration in the short to medium term.

Finally, Stage 5 is the Leading state. There are integrated approaches, not least to planning, there is real time collaboration, the operational and financial management information is aligned and operates close to real time. People use analytics as part of the business as usual (BAU) activity, informing their decisions on an ongoing basis.

To achieve sustainable improvement in the use of analytics, and to enable our enterprises to withstand disruption, we need to create an environment in which organisations become changeable, intelligent entities able to transform themselves in the face of disruption, and able to spot opportunities to disrupt others. To do this requires technology! It is important that technology has to be put to productive purpose, it requires that people and processes work in conjunction with the technology.

People are still important

That culture change requires a commitment to review, enhance and sustain effective people, use processes that are fit for purpose, and are evolving to stay relevant. The business change for that level of commitment eludes most at present, as they seek short-term silver bullets. Then there are innumerable distractions, and as a consequence many of us remain mired at Stage 3. Glimpsing what might be, and bemoaning our ability to achieve our improvement goal. This is a major challenge that we must meet if we are to succeed.

This is a massive topic which I look forward to coming back to as 2018 unfolds.

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