The "A" in FP&A
FP&A - the “A” is more important than ever
FP&R - some of you might be wondering what FP&R stands for. FP&R stands for Financial Planning & Reporting, and 20 years ago, it was what FP&A was in real life. An example of this that has always stuck with me was seeing a 100+ page report floating around the office at my first FP&A role and wondering if anyone ever even looked at the report. I have heard numerous stories about how big fat reporting decks were shared with the business and how these reports took many hours to build but were hardly ever used. It was almost like if you were not producing lots of reports then you were not doing FP&A right.
Fortunately, FP&A has changed a lot over the last decade, and for many of us gone are the days of just producing reports that are often overlooked. The reality is that FP&A professionals today need to not only be business savvy and have strong people skills, but they need to be wizards at working with data.
To demonstrate the need for stronger analytical skills, I asked 3 generative AI tools (ChatGPT, Bard, and Claude AI) to provide a list comparing skills required in an FP&A role from 2005 and today. All three tools came back with similar answers about the need for better technical skills and the ability to work with larger datasets and provide more in-depth and meaningful analysis and insights from the data. The below quotes from generative AI highlight the need for more technical and analytical skills:
"The role has shifted from narrowly focused on budgeting/modeling to providing strategic insights through advanced analytics and enhanced technical skills."
- Claude AI
"To be successful in this new role, FP&As need to have a strong understanding of financial accounting and financial modeling. They also need to be able to use data analysis and visualization tools to communicate their findings to stakeholders."
"[We have seen a]... shift in FP&A roles from a predominantly Excel-based, traditional financial analysis towards a more technology-driven, data-centric, and strategic role."
- Chat GPT
For further evidence of the need for strong data analysis and analytics skills, all one needs to do is look at a listing of current FP&A Roles. A review of FP&A roles on LinkedIn (from analyst to VP) found nearly every role required strong analytical skills. Furthermore, many roles wanted candidates to possess basic programming skills such as Python, SQL, and/or knowledge of common BI tools, in addition to advanced Microsoft Excel skills. This continued need for stronger data analytical skills can feel daunting to the average FP&A professional, but the good news is many paths exist to develop and gain these skills.
Enhancing your Technical Skills
Many FP&A professionals wonder where to begin and ask themselves should I run out and take a programming course, do I need to become a programmer? The answer is learning basic programming skills could help with the job but no you do not need to become a programmer and for the average FP&A professional, the path to becoming more analytical and enhancing your technical skills begins with Microsoft Excel.
I recommend that most FP&A professionals start with enhancing their Microsoft Excel skills, as this is a natural bridge to grow and develop your data analytical skills.
What many people fail to realize is that the Microsoft Excel of today is not the tool it was 20 years ago. Today it includes the ability to develop and use many of the data analytical skills employers are seeking from FP&A Professionals.
Let us start by looking at what I call Modern Excel and a few of the tools available in Modern Excel
Power Query with the ability to write SQL directly in the tool
Python - Python is native to Excel and Excel includes multiple Python libraries
Dynamic Arrays - Ability to build dynamic formulas when working with data sets
PowerPivot - Ability to learn the basics of PowerBI and building data models directly in Excel
Without ever leaving Microsoft Excel, one can start the journey toward learning SQL, Python, and even BI tools such as Microsoft Power BI. This is not to say that one should not learn tools beyond Excel. In fact, I encourage people to learn new tools and broaden their horizons beyond Excel, but the reality is much of what we need to learn can and probably should begin with Excel.
Many of you are probably wondering where to start with Excel; you might be thinking I already understand many advanced concepts such as Pivot Tables, Macros, Index/Match, etc. do I really need to learn more? The answer is “Yes”, if you want to advance into the world of data analytics and increase your ability to more easily clean data and gain insights from the data.
While no one path exists for making the journey into more advanced analytics, I recommend people start by learning the basics about
Data and Data Structure - Start with learning the basic structure of data in data tables. This will allow you to better structure data in Excel Tables.
Excel Tables - Learn how to use Excel Tables to structure your data properly.
Power Query - Power Query is designed to help you clean and prep your data as it is an ETL Tool (Extract Transform Load). Learning how to clean and automate your data prep.
Power Pivot - Learning Power Pivot allows you to take what you learned above and start analyzing data in new ways and building more advanced and automated reporting
As one begins this journey, more resources than ever before exist for learning each of these tools, and you can also look to Generative AI tools to help you learn how to perform tasks when you get stuck in your learning journey. Over time as one continues to develop, one will start to move beyond Excel, and one of the natural progressions is to start using Business Intelligence tools such as Power BI, Tableau, Looker, Domo, etc.
My Journey of Data Discovery
My personal journey started in earnest in early 2017. I had always had a strong interest in technology and was familiar with SQL, and Microsoft Access but had not realized the true power of Microsoft Excel.
I was tasked with developing a tool that could help our FP&A team more easily review accounting information as they researched month-end results. My desire to build something our team could use led to my discovery of Power Query and Power Pivot. I wholeheartedly embraced these tools and was able to start creating new and powerful reports for the business. Within my first year on the job, I had helped build a data cube that we deployed company-wide. This allowed everyone to research results in Excel and made the research of month-end results much easier for our FP&A team. Over the next several years, I continued to expand my knowledge of these tools in an effort to provide new data insights that could help move the business forward.
In time this led to me building reports in Power BI and providing insights the business had not had before because nobody had taken the time to truly dig into the data and learn how to combine the different data sources using Excel, Power Query, and Power BI. Some of you are probably wondering why we did not undergo a digital transformation and build a proper data warehouse to manage the reporting. This would have been the ideal scenario, but at the time, we did not have the funds or people resources to embark on a full digital transformation which would have included a new billing platform and data warehouse,. I had to start my journey with what was available.
Business Intelligence Tools and Power BI
For most of us, learning the basics of a business intelligence (BI) tool is the natural next step in the process. This was the case for me, as can be seen in the story of my data journey above. Business Intelligence tools can help us automate and streamline our reporting needs and gain insights into data through in-depth data analysis. Some of the many benefits of BI tools include:
Enable automation of reporting
Improve the accuracy and timeliness of financial and operational reporting
Enable real-time reporting and analysis
Provides the ability to analyze and drill into various datasets easily
Make data visualization easier
Enable organizational-wide transparency and accountability of the numbers
After working with a well-implemented BI tool, it is hard to go back to trying to use a tool such as Excel to build all your reports and distribute data to the organization. The reality is Excel is not a scalable enterprise solution for your data analytics needs, and that is why using Excel along with a BI tool enhances your capabilities.
I believe the best tool for many FP&A professionals to learn first is Power BI. The reason for this is many of us will have already learned many of the basic skills needed to use this powerful analytical tool by learning the basics of Power Query and Power Pivot within Microsoft Excel.
Furthermore, many companies are Microsoft shops, so Power BI is available to learn as part of the overall tech stack and is the dominant tool in the space, as demonstrated by its continued dominance in the Gartner Magic Quadrant rankings.
As we progress on this journey, we will find that our ability to gain insights will increase, and we will be able to deliver more value to the business. We will also discover the need for a more robust tech stack to support the ever-increasing data demands placed on us as FP&A Professionals.
The ever-growing Tech Stack
As the business continues to scale, not only will we need to improve our ability to analyze data, but the tech stack we use to analyze data will also need to improve. In addition to a spreadsheet, and BI tool, most companies will need a planning tool, often known as an Enterprise Performance Management (EPM) tool.
Spreadsheet tools are great for ad hoc analysis and building special use-case models, BI tools are great for drilling down into data to discover patterns and trends and great for managing our reporting and dashboarding needs, and planning tools help us manage the company planning process. As we look at each of these tools, we want to focus on finding tools that work well together, and one trend we are seeing is a convergence of FP&A tools and BI Tools. This is happening because planning and analysis often go together and having the ability to do both with relative ease makes us more efficient. Some strengths of this approach include:
Data model - the data model is built as part of the tool and so when you connect it with Power BI you can focus on the dashboards/reports vs building out the data model.
Dimensions - With a comprehensive BI data model design new dimensions and information become available for analysis like customers, product, etc that is often not available in your ERP.
It is for the above reasons that tools like Exopen have combined strong modeling platforms with Power BI. This gives you the benefit of having a strong modeling tool tightly integrated with a world-class BI tool such as Power BI and makes data analytics and analysis easier to perform across your financial and operational metrics.