Today we will talk about data management in finance. Nowadays, many concerns in the business environment are focused on data. There are several drivers for that, such as regulations (e.g., GDPR), the growing amount of data, new data processing and analysis technologies, etc. Many companies recognize data as an asset that has to be managed accordingly. The question remains: who is accountable for managing data within your company?

The reason why I have chosen the topic was a remark from a CFO of a leading international company (I’d instead not mention the name). He said that one of the severe obstacles to implementing artificial intelligence in business planning was the lack of data management, including governance, data integration, and data quality. My first thought was: “Bingo!!! a finance person mentioned the core areas of data management!”. I was also surprised, as finance professionals are primary data users and key stakeholders for data management but are not very actively involved in implementing data management within their companies.

So, if you are a finance professional, what are the main data-related obstacles, and what actions could you take to overcome them?

Understanding the essence of data management

First, let’s define what ‘managing data’ means.

Everybody in the company deals with data daily. You might have several key questions about your data:

  • Where do your data come from, and where does it go?
  • What does your data mean?
  • Who do you need to collaborate with to get quick wins?
  • Is the quality of your data good enough?

Data management is a tool to get an answer to these questions.

Make data management a part of your financial world

The main question remains: how could you incorporate data management in your company, particularly in finance? This is the question that thousands of data management professionals are contemplating nowadays.

I will share some of my conclusions based on my hands-on data management experience.

Data management setup means governing, recording, and handling the data value chain in your company.

Let’s see what happens with data in your company, how data management provides you with answers to the questions I mentioned above, and what the data value chain does mean.

Where do your data come from, and where does it go?

When entering your company, data is recorded in several administration systems. For example, a customer orders a product and other relevant information through your company’s website. It will be the first application in the chain that records data. Then this data will move, for example, into the CRM system where information about your customers is collected. Afterward, it travels into a plan to handle the order, payments, etc. Finally, all the data will end up in a reporting system. This is a process called data flow. As data is located and moves between different applications, we need a complete picture of your application landscape.



What does your data mean?

Data undergoes different transformations on its way. If you report on ‘Total Sales’ at the end of the chain, you first need to collect the data on each product you have sold.

To understand what happens to data on the way, you need to describe every step of transformation. You describe data by different means and on different levels of abstraction. For example, ‘Unit price’ is a business term you could put into a business glossary. Data management and IT specialists will go deeper and describe the same data elements at database levels using logical and physical data models. To explain how ‘Unit price’ will become ‘Total Sales,’ you will apply some aggregation or transformation rules. Data management and IT specialists will further specify them into business rules and ETLs.

Who do you need to collaborate with to get quick wins?

There are a lot of different people involved in data processing. All activities around data result from collaboration between data owners and users. Business process owners maintain the execution of all business activities. System owners will need to become responsible forever happens to applications, and in applications, such a collaboration is a complex system of distributed responsibilities between different business functions. This system is what is called data governance.

Is the quality of your data good enough?

To execute your duties as a finance professional, you need to get data of good quality on time and with the required frequency. It means this data would fit your purpose and correspond with the requirements you have set up and delivered to data owners. Data checks and controls must be built on the whole path from data origin to their destination to ensure it.

Organically, we came to a mutual understanding of the data value chain, a model that describes data movement across the enterprise and links data models, applications, organizations, roles, and data quality checks and controls. The definition is partly built on the definition from the DAMA dictionary [1].

Now you possess the key to incorporating data management in your practice:

The most important for your daily activities data you need to record, govern and then manage the data value chain by linking:

  • business processes and roles;
  • applications involved in the processes;
  • data described in business glossaries and data models;
  • data quality requirements, checks, and controls.

For those interested in more detailed elaboration on this subject, feel free to consult The Data Management Cookbook, available at




[1] –DAMA dictionary, p.141.

For more insights, visit the Data Crossroads Academy site: //