The power of harnessing data for Finance
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The power of harnessing data for Finance

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Depending on the industry in which your organization operates, it may now be possible to measure every single activity that the organization carries out. If you can think it, you can probably measure it.

Indeed, modern organizations are often designed with ease of surveillance not only as an explicit goal but as a prerequisite for the organization's functioning. Highly automated businesses can only be automated because their activities are measurable; by the same token, highly-developed automation is only possible thanks to the collection of data. You can see, therefore, how the logic of data-driven organizations has developed.

This series of articles is looking at the journey from data to the all-important output: insight and ultimately impact. (Read an introduction to the series here). In this article, we're looking at the first step in that journey: gathering data in the first instance.

Think broadly about collecting data

Methods of data collection vary depending on the nature of the thing being measured. For illustrative purposes, let's consider a highly-automated factory or delivery warehouse. Built into the fabric of this organization we will find a huge number of sensors receiving an even larger number of inputs. These might include everything from stock levels to warehouse temperature; from raw material prices to staff absences; from order completion time to energy usage.

As you'll immediately notice, this information is extremely varied not only in its type and function but also in the systems required to gather it. Those systems might include conventional sensors (heat sensors; pressure sensors; barometers; etc), but to get a full picture of our factory's health we also need to be collecting information that might come to us as API outputs, feeds from key markets and indices, pricing updates from suppliers, and so on. And that's just the quantitative elements.

We can refer to the various types of sensors (including all those listed above, and remember that these are not always physical sensors) as the "sensing layer" of the organization. The techniques a business deploys to observe itself determine the knowledge it can gain about itself. That knowledge in turn determines the insight that we eventually generate, and the entire process ultimately determines the trajectory of a business. It is, therefore, absolutely vital that we think carefully about what we're measuring and why.

Data lakes in the cloud

Given the extreme heterogeneity of these data types, the next challenge is associated with storage. Once we've collected these inputs, how do we sort or format them in a way that makes it possible for them to interact? And just as importantly, where do we do that?

Increasingly, the answer to the latter question can be found in the cloud. Cloud-based (or, even better, fully decentralized) solutions are the only practicable future for data. Data "lakes", either hosted in off-site server farms or massively geographically distributed, provide the best possible balance between cost, speed, and reliability, particularly for SMEs that do not have the capacity to build their own in-house data solutions.

The ideal data lake provision will enable you to "plug in" every sensor your business is operating, and manipulate their outputs in an intuitive way. This process will likely make good use of Internet of Things protocols, but it will also rely heavily on APIs and other machine-readable methods of communication.

Now is a good time to pause and make some notes. Think about the following questions:

  • Where is data stored in my organization?
  • Who can access it, and for what purposes?
  • How simple is it for stakeholders to understand what data is being collected, and how they can build it into their workflows?

Now that we've thought about data collection, we can get onto the dirty work: cleaning it up. Subscribe to the series to get the next article, on data hygiene, as soon as it's published.

You can read previous article(s) in the series below.

From data to impact - the Holy Grail of Finance

While you await future articles why not read my previous series on Data & Analytics below?

Finance and Data & Analytics - better together

Does your company need a Data & Analytics team?

Can a finance professional "do" Data & Analytics?

Why Data & Analytics always wins

The case for Finance "owning" Data & Analytics

Why Data & Analytics struggle with the "last mile" of impact

Let's end the war between Finance and Data & Analytics

How to create value with Data & Analytics

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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 190,000+ 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.

Ryan Donaghy

Advance Your Finance/Data Career 📊 with English Communication Skills 📈 | Specialist English Communication Skills Coach

2y

Some thought-provoking questions on gathering and leveraging data to encourage informed, strategic decision-making, Anders. Learning to ask focused, specific questions, and coming to understand the business' income and expenditure in greater detail yields greater self-awareness (for the business). This helps position finance as a greater value-driver, and can allow operations to run more smoothly.

Jamie Genge

Commercial Senior Finance Professional with a passion for FP&A

2y

Great set of previous articles Anders, and looking forward to the up coming journey from gathering data to impact! We (finance professionals) should always remember the unique position finance has providing objective analysis of data. Putting the salient facts at the forefront of business decisions is so important for strategic decision making. If you can weave those facts into a compelling story that brings the data to life, the journey to impact is so much smoother for the business

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