Recently, I have encountered a lot of discussions within the finance (FP&A) professionals community about business partnering. A business partnering between finance and data management is an example. The challenge in such discussions is to move from theoretical reasoning to practical execution. In this article, I would like to discuss one possible area where cooperation between FP&A and Data management professionals can deliver mutually beneficial results.
This area combines two independent FP&A and Data Management concepts: driver-based modeling and data lineage. Both concepts are relatively new for both business functions and require significant resources and effort to be implemented in practice. Business effectiveness and partnering are really about seeing the commonalities and combining the resources and approaches for implementing both concepts simultaneously.
In this article, we will investigate the following:
- the origin of the shared nature of the concepts of driver-based modeling and data lineage
- the essence of each concept
- the steps to create partnering between finance and data management professionals
A similar value proposition of data management and FP&A in the business cycle is the foundation for pragmatic business partnering.
You can discover the shared nature of driver-based modeling and data lineage by analyzing the business lifecycle and value propositions of FP&A and Data management.
The strategic goal of each business is survival in the long term. And in order to ‘survive,’ a business needs to ensure a steady profit and proper asset management, data being one of these assets. The management of the business directly influences the realization of the key goal, which in its turn, relies entirely on the decisions being made by the company’s top management. To make decisions, the ones who make those decisions need to get appropriate information, which is often expressed in performance indicators and/or KPIs. And in order to produce such information, first, you need to ensure the delivery of appropriate data, key business drivers in particular. Each business function, a combination of business processes and people involved, participates in data processing and delivery in its way. Technology and resources enable business functions to process and analyze data.
The key value proposition of both data management and FP&A is supporting and improving decision-making on all management levels.
The ways to deliver this value proposition can differ. Data management is accountable for optimizing the data & information value chain. A data & information value chain is a set of business capabilities that enables data transformation into meaningful information required for decision-making on different management levels. Data lineage is the documentation of the data & information value chain.
FP&A provides insight into information and advice on possible decisions to managers at different managerial levels. FP&A does it by applying relevant business planning and analysis techniques. Driver-based modeling is one such 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 apply modern techniques, of which driver-based modeling and data lineage are good examples. Let’s look at the essence of these two techniques and find interdependencies.
Let’s quickly recap the essence of driver-based modeling. Driver-based modeling allows you to link business drivers with financial results. Consequently, you use financial results to measure your company’s success. In this context, ‘modeling’ means the identification of relationships between operational business drivers and anticipated financial outcomes. These relationships are expressed in mathematical formulas. The set of drivers, financial results, 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 generates business outcomes expressed in financial results and KPIs.
A simplified example of the driver-based model can be seen in Figure 2:
The elements marked yellow are primary business drivers. Throughout the chain, you see which business drivers influence Net profit, which is one of the key company’s KPIs.
Data lineage is documentation that describes how data is being transformed into information on its way from its origin to its final destination. You describe this transformation path by linking business processes, applications, and data elements. You need to know data lineage to:
- comply with regulations
- enable business changes
- improve the quality of data
- meet supervisor and audit requirements.
If you take another look at Figure 2, the business drivers (marked yellow) represent data elements that have been processed into meaningful information. In our case, Revenue, Cost of sales, and Net Profit represent the data transformation information.
If I built data lineage for the same data elements as in Figure 2, it would look something like this:
Those who want to know more about data lineage can consult my recent ‘Data Lineage 101’ blog series.
If you compare Figures 2 and 3, you will see a lot of similarities between the representations of driver-based modeling and data lineage. So, this is the best evidence that coordinated activities of finance and data management professionals will deliver mutual benefits for both functions and the business as a whole. In the beginning, I promised to show you how to take theory and convert it into practice. So here are a few practical tips for action.
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? Let’s be honest. Each company has finance as a business function. Not all companies have a formal data management function. Not having a proper data management function does not mean that the company does not perform data management-related activities. But in any case, it would be more logical or finance to take on the sponsor role in such an initiative, considering its decision-making power within the company.
2. Scope the initiative
First, you should find the most critical performance indicators (KPIs) used in decision-making. You will investigate the key business drivers by documenting data lineage for those critical elements. You can read more on critical data elements in my article ‘Scope your data management initiative by using Critical Data Elements.’
3. Specify the depth and length of data lineage.
The whole 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 rather long and complicated. That is why a company often starts with a limited ‘length’ of data lineage, for example, at some point of data aggregation. More on the subject you can find in my article’ Data lineage 105: Implementation guidelines.
4. Choose the method and tooling to document the driver-based model and data lineage.
One of the prerequisites for creating data lineage is to have a formal data management framework in place. It means that finance can play a vital sponsor role in getting data under control by setting up such a framework.
The rest is simply the question of finding feasible and ‘fit for purpose’ resources and tooling to document driver-based models and data lineage.
The whole company will benefit from it.
I hope I have convinced you of the importance of partnering between finance and data management professionals.
For more insights, visit the Data Crossroads Academy site: