Estimated reading time: 4 minutes
If you work as a finance professional or aspire to work as one, there are data fundamentals each finance professional should know. In addition, there are key data technologies finance professionals need to understand. Finance is a core function in any business, and you will be working on data-related finance projects. This article explains what we see as some of the core data fundamentals and key data concepts you will need to know. Most importantly, we explain why you need to know these as we cover each.
Table of contents
Technologies
Data technologies used extensively in organisations are databases, data warehouses, reporting tools, and ETL tools. What each one is and what they do is essential to understand. For example, you may rely on a report published in your reporting tool. All the technologies covered in this post can be used to create the report. An ETL tool can load financial data from your Finance system into the the data warehouse. A reporting tool then uses the data from the data warehouse to present you with your financial report.
Databases
Organisations use databases to store business data. Underpinning your core Finance system, or your ERP or CRM there will be an application database. Organisations traditionally stored all the data for the organisations in databases. This was before data warehouses and data lakes became much more prevalent.
Databases store data in the form of tables of data. The language used to query and manipulate data in a database is SQL. Every organisation will have several databases. The challenge is always getting the correct data out of an application database for analysis and insight.
Data warehouses
A data warehouse is a databases used to store huge volumes of data. Data warehouses are designed for reporting and analysis. A now very common cloud data warehouse that you may have heard about is Snowflake. Data & analytics teams or IT teams manage data warehouses in organisations. These teams will need subject matter expertise from Finance to understand financial data that is part of the warehouse.
ETL tools
ETL stands for Extract, Transform and Load. Organisations use an ETL tool to extract data from their various applications and load it to either a data warehouse or a data lake. IT or data teams are typically the ones who work with ETL tools. Transform refers to the business logic applied to the data to make it usable for reporting. The application the data is extracted from is known as the “Source”. The Target” is where the data is loaded to.
The “ETL didn’t run last night” or “there were issues with the overnight ETL” are typical phases you will hear. What this means is that errors occurred loading the data warehouse. After fixing the errors, it’s simply a matter of running the ETL again.
Reporting and Analytics
Reporting
In Finance, you may use various reporting tools. Your ERP or core Finance system may have operational reporting capabilities. This will allow you to run multiple standard financial reports from within the ERP or Finance system. Real time operational reporting is what this is refereed to. You also may have a business intelligence tool where you can run more sophisticated reports that blend financial data with non-financial data.
Depending on the size of your organisation, you may have a Corporate Performance Management (CPM) tool which you use for financial reporting. CPM tools are great for working with your core financial reports or Financial Statements such as the Income Statement, Balance Sheet and Cashflow. These tools have financial intelligence built-in, making working with financial data much more straightforward.
Business Intelligence
You can consider Business Intelligence (BI) the same as reporting. Software vendors introduced BI as a natural extension of reporting. Taking decisive action based on the information presented it the intention of BI. In some cases, integration to ERPs was supported, allowing user to navigate to carry out remediation action. In practice, this rarely happened, but it demonstrated extremely well. Essentially, Business Intelligence tools allow you to create reports and dashboards. The most common BI tool is Power BI from Microsoft.
Analytics
Analytics is a complex term to define clearly. It’s an umbrella term for collecting and analysing data and then using that data to make decisions. You can find definitions of different types of analytics. One of the most common types of analytics is Predictive Analytics. Predictive analytics, as its name implies, allows you to make predictions from data. For example, can we predict our sales revenue if we do x and y? Or can we predict how many employees we will need in 18 months?
In addition, marketers append the moniker “analytics” to several domains. This complicates the term analytics further. Examples of these are Customer Analytics, HR Analytics and even Financial Analytics.
Conclusion
To summarise, this article has covered the core data fundamentals you, as a Finance professional, need to understand. Armed with this knowledge, you will be in a stronger position to work with the other business functions in your organisation. Understanding the core technologies covered will help you in the future. Reporting tools and analytics will evolve, but the fundamentals covered here will remain the same.
Further explore the Data in Finance series.
