What cleaners and Finance professionals have in common
Anders Liu-Lindberg
Leading advisor to senior Finance and FP&A leaders on creating impact through business partnering | Interim | VP Finance | Business Finance
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If data, information, and numbers are the most important tools in the Finance professional's arsenal, then it makes sense that we should look after them.
Just as cleaners and janitors are responsible for maintaining good operational efficiency and hygiene across a business by keeping its physical assets in top condition, Finance professionals must ensure that they are focused on maintaining good data hygiene.
Without clean, usable data, we can't do our jobs. And yet data hygiene is often overlooked.
Cleanliness is next to...
In order for it to be useful, data needs to be clean, accurate, and correctly formatted. This is the bread and butter of any Finance professional's work, and it is crucial that we understand how to achieve it - and that we know what clean data looks like.
This is a specific skill set and one that needs to be honed. It is also a process, and that process begins with the collection of the data itself. Without clearly-defined, well-designed data collection practices, we have no way of knowing whether the information we are gathering is reliable or accurate.
This is a fundamental consideration for both qualitative and quantitative data; while Finance professionals may be more familiar and comfortable with the latter, the importance of good qualitative data must not be forgotten. This requires knowledge of ethical and effective collection processes.
Beyond data collection, there are several key considerations when it comes to good data hygiene:
Automate mindfully
It is possible to automate the majority of this work, and the ongoing digital transformation of the Finance Function is making this easier by the day.
However, as we automate or 'process-ify' more and more activity, it becomes increasingly important that we understand the ways in which those processes work. In order to get the results that we need, we must design systems and processes that are not only efficient from a computing perspective but also comprehensible to humans.
In practice, this is likely to mean a tighter overlap between traditional Finance and Data & Analytics disciplines, with the former providing the knowledge of what the data should look like and how it will be used, and the latter bringing expertise in process design and programming.
Data transformation = insight
Finally, it's important to remember that data cleaning is not the same as data transformation. Rather, cleaning our data allows us to transform it - that is, to present it in such a way as to make it useful for every stakeholder.
Until very recently, data transformation has been taken to mean something like the creation of dashboards to support decision-making by other business functions. Today, though, not only is "dashboard fatigue" setting in but there is also a growing awareness that there are better and more effective ways to wrangle data into usable formats - and that the technical requirements and computational power associated with these practices are becoming much more accessible.
The ultimate goal is the delivery of "self-service" data solutions to every business function, supported by a "point person" whose expertise covers finance, data and analytics, and holistic business strategy. That point person is known as a business partner, and they are a crucial nexus of information.
Business partnering unlocks the power of the Finance Function by embedding it more deeply within every other part of the business. I believe business partnering is the future of the Finance Function, and I'm not alone - it's one of the biggest growth areas in the finance profession, and a new frontier for ambitious practitioners.
Want to become indispensable in any organization, even (or especially) as automation and related technologies take on more and more of the 'traditional' work of the Finance Function? Here are three simple steps to take right now:
How much time do you spend on cleaning data in an average week? Post your answer below and we bet it's a lot more than you'd want. There's still much to do in this space and it's critical that we succeed!
This was the second article in my latest series about the "Data to impact journey" You can read previous article(s) in the series below.
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While you await future articles why not read my previous series on Data & Analytics below?
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Anders Liu-Lindberg is the co-founder and a partner at the Business Partnering Institute and the owner of the largest group dedicated to Finance Business Partnering on LinkedIn with more than 10,000 members. I have ten years of experience as a business partner at the global transport and logistics company Maersk. I am the co-author of the book “Create Value as a Finance Business Partner” and a long-time Finance Blogger on LinkedIn with 110,000+ followers and 195,000+ subscribers to my blog. I am also an advisory board member at Born Capital where I help identify and grow the next big thing in #CFOTech. Finally, I'm a member of the board of directors at PACE - Profitability Analytics Center of Excellence where I support the development of new analytics frameworks that can improve profitability in companies around the world.
Global Process Owner, F&A Capability and Finance Transformation leader (CA, CPA, CIA)
2yData hygiene is definitely critical to convert big data from a challenge to an advantage!
📊 Analytical and Strategic Financial Leadership ▪️ SME CFO ▪️ FP&A ▪️ Digitalization & Standardization ▪️ Head of Finance at PRIMUS AERO ✈️
2yWhy that describes my feelings during work? What a luck that cleaning is like meditation for me 😅
Advance Your Finance/Data Career 📊 with English Communication Skills 📈 | Specialist English Communication Skills Coach
2yExpanding on your "data validation" point, Anders. It's becoming clear that data gathering in finance is less of a challenge than data prioritisation. This also filters into your point on "dashboard fatigue". The more metrics we are following, the more laborious the process is, and the harder it becomes to make strategic recommendations. That's the importance of identifying not alone accurate data, but the most relevant and representative metrics for the task at hand. This is a kind of financial "cleaning up".
Consultant Finance Stratégie et Projets
2yVery interesting.