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How to get good financial data: 4 typical challenges and how to solve them

Have you invested in a Business Intelligence (BI) financial solution for your reporting, but found that the solution is not working optimally in your everyday work? Or are you on your way to starting your BI journey, but don't quite understand the technology behind it and the language that IT speaks?
September 30, 2019 twoday kapacity

There are many good reasons why the finance function should use BI more in their daily work. Some companies have already started and implemented a Data Warehouse with financial data. However, many finance departments are still challenged with inadequate data structures to support their processes. In this blog, we will look at some of the typical challenges and how we can solve them.

Challenge 1: Data from acquisitions and subsidiaries are handled manually

For companies that are already using Business Intelligence in their finance department, problems arise when the daily rush and pressure set in, and as a result of natural impatience, quick fixes are made to data when reporting needs to be quickly delivered. This can, for example, be in connection with consolidating acquired and existing subsidiaries where their data needs to be included in the reporting, but the IT department does not have time to add the new data in time. Often, the responsible employee then manually adds the summarized data to the reporting. This means that the good financial data in the Data Warehouse is no longer the focal point of the common truth and only becomes valid again when new data is added to the data model..

Challenge 2: No drill-down to support causal explanations 

Here, the next challenge arises. When the finance employee chose to add summarized data directly to the reporting, it may not have been done with the same level of detail or grouping on accounts and financial dimensions as the actual dataset that IT receives and adds to the Data Warehouse. This means that the historical data is no longer consistent when comparing finance reporting to the Data Warehouse, and the finance function must therefore operate with this difference for the rest of the current year. This is not smart and becomes a bit of a time-waster.

Challenge 3: Master data is not maintained and does not match reporting and analysis needs

Another example can be the modification and maintenance of master data, including charts of accounts and other financial dimensions. Changes are often not implemented in the correct source system, and one forgets to update master data when reporting structures change, which often leads to manual groupings in Excel. It can be as simple as a new account being placed in the wrong place in the account hierarchy or simply missing a hierarchy. From there, the reporting account group in the BI data model is no longer fully up-to-date, leaving manual work in Excel until the error is corrected in the source system. It can also happen that a new department in the ERP system is used for accounting, but the department is not automatically added to the BI data model. In this case, reporting will no longer be in sync and accurate.

Challenge 4: Finance and IT don't speak the same language

In addition to the aforementioned time challenges in data and dimension values - often seen in connection with month-end closing - IT and the finance function are often far apart in terms of understanding. The finance function does not always manage to present all its requirements for the data model, such as content, concepts, history, update frequency, currency issues, etc. This often happens because they are not aware of all the requirements needed to ensure a good and solid model. At the same time, IT struggles to translate their technical language into business requirements that the finance department understands. IT might ask: "What is your expectation for relational reporting?" That is, translated into finance language: "Do you need to be able to see the accounting entries, receipt by receipt, in the data model?" This gives rise to misunderstandings and can challenge good cooperation.

Solution: Do it right from the beginning - it pays off in the long run.

Don't worry, it doesn't have to be like the above scenario if you ensure a good start, structure the data flow, and implement the right processes around your BI solution.

A robust Data Warehouse solution helps ensure consistent data and is the foundation for the finance data model of the finance function. During the implementation phase of the solution, it would be beneficial to appoint a working group that includes representatives from both IT and the finance function to quickly break down the understanding barrier. IT should listen and learn with patience, and the finance function should prioritize allocating time to be involved in the entire process from specification to final user acceptance testing.

If you are a company that frequently acquires other companies, it can streamline the process of adding new company data by incorporating it into the data model from the start. A simple solution with a loading sheet for the Data Warehouse, until the acquired company's data is converted to the ERP system, can be a good solution during the transitional period instead of manually handling it in reporting.

In addition to creating common understanding and engagement among IT and the finance function in the implementation phase, it is also important to have agreed-upon operation and maintenance of the solution in day-to-day operations. IT can contribute to setting up monitoring and quality control for both data, dimension values, and technical components, and by adding an email group to receive any error messages, both IT and the finance function receive ongoing messages and can jointly be proactive and fix errors and shortcomings. The finance function should prioritize ongoing time to troubleshoot and correct errors and shortcomings to minimize them over time. Through a joint effort, also in day-to-day operations, IT and the finance function learn from each other, which benefits in the long run.

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