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Digitization has a fundamental impact on every business, and it is estimated to be strongest on financial planning and analysis (FP&A). Most business units still look at FP&A professionals to lay out the company’s goals and map out a company’s financial future. According to IBM, more than 50 percent of finance professionals use traditional Microsoft Excel as their go-to tool to complete this task.
As the complexity of business increases, creating financial plans takes a massive amount of data from different sources, aggregating, cleansing, standardizing and then performing analytics. Finance professionals spend most of their time manually collecting, consolidating and validating data instead of finding insights. Excel lacks collaboration capabilities and causes delays of critical information that can cause a ripple effect, leading to poor decision making and poor outcomes. Adapting modern technology by focusing on digitization, process automation and data analytics can reduce Excel dependency.
Fifty percent of professionals spend most of their time collecting and validating data according to IBM. Directly connecting data from different sources to build a centralized repository can reduce collecting and validating data significantly. Building a centralized system is a role of data management professionals. Financial software can take the data from any source and put it in the desired format. However, FP&A professionals still don’t have to give up on Excel, as most finance software, like Hyperion, Tagetik and others, has a direct Excel interface. Instead of a standalone Excel application, having a database that sits on top of Excel can help in faster data processing that is also secure and reliable. Even if you are performing analysis, you can limit this to only the data you need. All the changes made on the Excel interface can be saved back into the system to build one source of truth, avoiding any conflicting data. Integrated planning helps in bringing collaboration, driving more access to information and faster insights. It also reduces dependency on Excel as data consolidation, collection and validation is done outside Excel, saving valuable time for FP&A professionals.
Learning Modern Programming Languages
In the age of data science, machine learning and artificial intelligence (AI), the best things to learn are programming languages like Python and R. R is preferred for statistics language, whereas Python is used as the universal programming language. These tools are agile, powerful, flexible and practically overcome all of Excel’s processing and analytics challenges. Understanding programming language can be daunting for most accounting professionals; however, it can take data analysis to a new level. Python has much better control over data when compared with Excel. Software packages like NumPy, Pandas and Scikit are excellent at data mining and analysis. These tools quickly process and consolidate data that would have taken hours when working with Excel. This can be connected to Excel, and business users can see whatever information they need. Python has the analytics library, which can be used to create statistical and predictive analytics to identify and assess trends and seasonality patterns in historical values, significantly improving forecast accuracy. It also reduces the time required to produce accurate profitability and balance sheet forecasts, allowing users to focus on process optimization, managing exceptions and making adjustments as appropriate. Once automated, these forecasts can refresh every week or even every day.
Using Modern Data Analytics Tools
Learning a programming language could be daunting for many finance professionals. Using modern data analytics tools like Alteryx, H2O and Rapid Miner can help overcome this issue and quickly provide insightful analysis. These tools have comprehensive data analytics packages like regression analysis, clustering and time series analysis so users have no requirement to go into Excel for analysis. Instead of coding, the user can drag and drop into the model. Once the model has been set up, it can be automated daily. This automation can provide financial professionals with the capability to forecast daily. Using these tools can significantly reduce the dependency on Excel as all processing analysis can done in the tool.
Data visualization is an essential aspect of any company. Efficient visualization can help to find the hidden trend, which could help determine a company’s future. Most FP&A professionals use standard Excel charts to display their forecast, lacking effectiveness to discover insights about products or services. While working with Excel for data visualization, users have to first manipulate data, and then different charts and graphs are created manually. Excel has limited features to build a dashboard, and to refresh it could be tedious and time-consuming. With the help of modern visualization tools like Tableau, Domo and Power BI, it can easily connect to centralized data and automatically update the reports. These tools can provide effective visualization like a heat map or scatter plot. In addition, users have analytics capabilities like creating “what-if” scenarios and correlation. These visualization tools are so advanced that they use natural language processing to deliver the results effectively. As the complexity of data increases with structured and unstructured data, traditional Excel can be too complicated to build effective visualization. However, on complex data sets, the modern tool can effectively create a dashboard with ease.
Excel Upgrades and Features
The biggest issue with most financial professionals is that they are well-versed in using traditional Excel. Since 2016, Microsoft has invested heavily in Excel to meet current user needs, but most financial professionals are unaware of these features. Many finance professionals are still using the same features created more than a decade ago.
With current features, users can connect to an external database or webpage and import data with a single click and keep the connection intact for future purposes. If there is a newsfeed, users can refresh the data instead of creating it from scratch. Also, with Excel Power Query, users can easily format and slice or dice the data based on the requirement. Microsoft also added the Power Pivot feature, which enables users to meet complex data needs. Practitioners can perform more powerful data analysis and create more sophisticated data models than standard Excel.
With Office365, Excel has started providing real-time collaboration. Power BI integration to Office365 has provided new visualization and dashboarding capabilities, which was the biggest drawback in using traditional Excel. Power BI can bring data from various sources together to make an interactive dashboard.
Digitization and process automation are key drivers for any business growth. Excel is excellent for ad hoc analysis as it provides users flexibility that no other tools can provide. But this flexibility comes at the cost of manual processes, poor security and poor use of resources, and it is time-consuming. Finance professionals’ tasks should be to provide real-time analysis; instead, most finance professionals are stuck cleaning and consolidating data. Using third-party financial software, understanding programming languages like R and Python, or using data analytics tools can quickly provide insight on the business’ performance, which will help users make quick business decisions. However, even if users want to stick with Excel, they need to understand the modern features available with Excel, which can simplify processes.