As promised in one of my previous articles, let us go deeper into the relationship between FP&A and Data management (DM) and see how Data management enriches your FP&A role and successes.
Using the FP&A Analytics Maturity Model, which the London FP&A Board developed, you have definitely checked which state of the FP&A maturity corresponds to your company and which goals will be reached in the nearest future.
Let’s now check:
- Which elements of DM touch your daily work
- How to measure DM maturity in the frame of the FP&A maturity model.
Before we continue with a definition of Data management, let me ask you three questions:
- Do you have a situation which at least 40-50% of your reports are made in Excel?
- Do you have reconciliation problems between different reports?
- Do you need to ask for some input information from your colleagues from other departments?
If all answers are "YES", then it means that you already deal with Data management daily, and there is a reason for you to continue reading the article.
As discussed in the article, you, as an FP&A professional, deal with company value. Data is also a company asset and to be treated and be managed as assets.
What are the main elements of Data management which might be of interest to and require your attention?
- Data governance rules and procedures that allow you to solve data-related issues with other departments
- Data architecture answers your questions: which data do I have, what does the data mean, where is data located, and how it streams from a source to the report along with different systems and departments.
- Data quality deals with assessing whether the data you get from your reports is at an acceptable level of accuracy and completeness. Do you receive it on time?
- DWH/BI provides you with reporting and historical, and analysis capabilities
- Security ensures that all your data is classified.
These are not all established Data management elements, but if you control these five elements for your data, you safeguard the outstanding quality of your FP&A work results.
To define the maturity, you need to know which deliverables of each DM area are to be measured.
So, let’s make a shortlist of the main deliverables.
The main deliverables can be briefly described as follows:
Data governance is measured by the presence of a Governance Policy, roles, and/or procedures that describe who is responsible for what regarding solving data-related issues among different departments;
Data architecture is measured by:
- Company business glossary – the list of terminology that eases communications and aligns the ‘local languages’ of different departments
- Catalog of reports and/or data elements – the list of reports used within a company with at least some information regarding it: who makes the reports, who are recipients etc.
- Catalog of software applications used – the list of applications used across the company and their owners
Data quality practices include:
- Data quality requirements – do we know what kind of controls are needed to ensure accurate and complete data?
- Data quality internal projects – are there some running projects across the company that aims to solve possible data quality issues?
- Data quality dashboard is the tooling to monitor the daily quality of data to undertake actions.
DWH/BI is measured by the availability of tooling for report generating and disseminating reports.
Data security can be assessed by the presence of policies and procedures, data classifications, monitoring, and issue resolution mechanisms.
Let’s now combine these areas with the FP&A maturity model:
Thanks in advance if you let us know at which stage of the Maturity level you will put your company.
Note: this article has also been published on FP&A Trends, 03/01/2017. Link to the article.
For more insights, visit the Data Crossroads Academy Site: //academy.datacrossroads.nl/courses/how-to-bring-data-management-into-finance-practices/lesson/data-management-fundamentals-for-finance/
