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Aligning Finance and Data Management

Aligning Finance and Data Management

Many organizations want to adopt data management capabilities to current business functions, but do not know how to do that.  One proven approach combines techniques from finance and data management.

There are many discussions within various business departments about the value and need to align data management with that business operation.  In most cases, the concepts are known, but the challenge is to move from theoretical reasoning into practical execution.  One function that is frequently targeted for alignment with data management is finance (including accounting and related activities – FP&A).

This approach combines concepts from FP&A and Data Management: driver-based modeling and data lineage. Both concepts are relatively new for many business functions and require significant resources and effort to be implemented. There are commonalities with the approaches and combining the resources for implementing both concepts supports business effectiveness and alignment.

Important points to understand and implement this alignment:

  • similarities of the concepts of driver-based modeling and data lineage
  • the essence of each concept
  • the steps FP&A and data management professionals could take to create a mutually beneficial partnership

Common value proposition

An organization can discover the similarities of driver-based modeling and data lineage by analyzing the business lifecycle and value propositions of FP&A and data management.

Figure 1. Finance and Data management value propositions in the business cycle.

The strategic goal of any business is continued survival. To “survive”, a business must ensure a steady profit and proper asset management, including their data assets. Business management directly influences the realization of this key goal, which in turn, relies fully on the decisions that are made by the senior management and leadership. To make decisions, the decision-maker must have appropriate information, which is often expressed in performance indicators (KPIs).

To produce such information, one needs to ensure delivery of appropriate data, focused on key business drivers. Each business function, which is a combination of business processes and people, participates in data processing and delivery in its own way. Technology and resources enable business functions to process and analyze data.

The key value proposition of both data management and FP&A is support and improvement of decision making on all management levels.

The ways to deliver this value proposition can differ. Data management is accountable for optimization of the data and information value chain. A data and information value chain is a set of business capabilities that enables the transformation of data into meaningful information. Data lineage is the documentation of the data and information value chain.

FP&A is responsible for providing insight on information and advice on possible decisions to decision-makers at all levels. FP&A accomplishes this by applying relevant business planning and analysis techniques to the selected data. Driver-based modeling is one technique. It provides insights into dependencies between the key business drivers and performance indicators and forms the basis for effective financial planning and forecasting.

To deliver the value proposition, data management and FP&A should form a partnership to modern techniques, such as driver-based modeling and data lineage, and identify relationships between the functions and chosen techniques.

Driver-based modeling

It is important to understand the concepts of driver-based modeling. Driver-based modeling allows one to link business drivers with financial results. Consequently, it means identifying relationships between operational business drivers and anticipated financial outcomes. These relationships are expressed in mathematical formulas.  A set of drivers, financial outcomes, and relationships between them is called “a driver-based model”.

Thus, a driver-based model is a simplified mathematical representation of relationships between operational business drivers and consequently generated business outcome expressed in financial results and KPIs.

A simplified example of the driver-based model is seen in Figure 2:

Figure 2. A simplified driver-based model example.

The elements marked yellow are basic business drivers. Throughout the chain, business drivers have an influence on Net Profit which is one of the key company’s KPIs.

Data lineage

Data lineage is documentation which describes how data is being transformed into information on its way from its origin to its destination. It is a form of metadata. To describe this transformation path business processes, applications, and data elements should be linked. Data lineage allows an organization to:

  • comply with regulations
  • enable business changes
  • improve the quality of data
  • meet supervisor and audit requirements.

Looking at Figure 2, the business drivers (marked yellow) represent data elements that have been processed into meaningful information. Revenue, Cost of Sales and Net Profit represent the information produced from data transformation.

Data lineage for the same data elements as in Figure 2, could look like:

Figure 3. A simplified example of data lineage.

Comparing Figures 2 and 3, many similarities can be seen between the representations of driver-based modeling and data lineageSo, this is the best evidence that coordinated activities of finance and data management professionals will deliver mutual benefits for both functions and for the business.  There are a few practical tips for actions.

Tips for actions

1. Start the initiative: who is in charge?

Both finance (FP&A) and data management functions can profit from the initiative. But who has more power? Each company has finance as a business function. Not all companies have a formalized data management function. Not having a formal data management or data governance function does not mean that the company does not perform data management related activities. Hence, it would be more logical for finance to assume the sponsor role in such an initiative, considering its decision-making power within the company.

2. Scope the initiative

First, find the most critical performance indicators (KPIs) that are used in the decision-making process. For those critical elements, investigate the key business drivers by documenting data lineage. To learn more about the subject of critical data elements read “Scope your data management initiative by using Critical Data Elements’.

3. Specify the depth and length of data lineage.

The scope of data lineage starts with the original data sources and ends at the point of final usage. In large companies, especially with many subsidiaries, such chains are long and complicated. Therefore, a company often starts with a limited “length” of data lineage, for example, at some point of data aggregation.

4. Choose the method and tooling to document driver-based model and data lineage.

One of the prerequisites for creating data-lineage is to have a formal data management framework established, so finance can play a very important sponsor role for controlling data. Following development of the framework, the team finds feasible and “fit for purpose” resources and tooling to document driver-based model and data lineage.

Conclusion

There is value in aligning data management with that business operation, while focusing on the challenge of moving from theoretical reasoning into practical execution.  One proven approach is to use a combination of driver-based modeling and data lineage. Although focused on an example from an alignment of Finance (FP&A) and Data Management, all areas of the organization can benefit from adopting these concepts and techniques.

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Irina Steenbeek, Ph.D.

Irina Steenbeek, Ph.D. is a Senior Data Management professional, with many years’ experience in several multinational banks and with other complex industries.

She is also experienced in project management for software implementation, business consultancy and control, and data science, and serves as a consultant through Data Crossroads.

Irina is the author of two books: The Data Management Toolkit and The Data Management Cookbook, along with various white-papers and articles on the topics of data management and its implementation. As the result of her experience, she developed a generic data management implementation model applicable for medium-sized companies.

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