With traditional reporting methods, a lot can go wrong as financial data moves up and down a company’s data chain on its way to becoming a financial report.
One department may recognize revenue or define customer segmentation one way while another defines those same segments differently. The same is true for calculating commonly referenced metrics such as gross margin or EBITDA. Data then passes through multiple systems and the room for error compounds with each import/export.
And where does this lead? CFOs and their teams have to spend time manually formatting and interpreting data in order to create useful business intelligence. The end result is often a misleading report or intelligence delivered too late to take action.
What traditional financial reporting lacks, enhanced financial reporting aims to fix. This includes three components:
The automation piece comes into play with data extraction, storage, and reporting—these actions are automated so that finance teams don’t have to spend countless hours performing manual tasks.
Availability is about getting useful data and actionable insights in near real-time, rather than waiting until the end of the month or quarter.
Finally, flexibility allows the organization and its users to adapt the reporting system to its current needs, without sacrificing shared definitions and ease of use.
The first step in achieving enhanced financial reporting is building a modern data architecture.
Private equity (PE) firms and the senior management of their portfolio companies need useful and contextual intelligence to make the right decisions. Most organizations gather this intelligence using traditional reporting tools and practices that paint an incomplete picture. While they’re able to deliver raw data quickly, contextualized intelligence based on that raw data often comes too late. That’s where modern data architecture comes into play.
Modern data architecture helps CFOs close the gap between data and intelligence, enabling them to report to PE partners with actionable insights faster.
Modern data architecture automates raw data extraction and transformation from an organization’s entire technology stack. Think enterprise resource center (ERP), point of sale (POS), and customer data information (CRM). After extraction, data is consolidated in a unified manner and then filtered through the pre-defined metrics that an organization defines as important. Only then is it presented through reporting and visualization, highlighting important intelligence that executives and partners can use to inform decisions.
There are many famous anecdotes in the business world about managers “lying to each other” by massaging financial reports in their favor. While this may be true in some instances, what often happens is each middle manager within the reporting chain contextualizes raw data a little bit differently than the next based on their department's KPI mismatched definition or personal biases. The CEO or partner at the top of the reporting chain ends up with competing viewpoints compounding on top of one another—and may have a hard time extracting useful intelligence.
Modern data architecture eliminates the biases and competing KPIs through the reporting chain by establishing a single source of truth for the entire company to report on, and it can do it much faster than traditional reporting systems. When intelligence flows as fast as the raw data that produces it, both CFOs and PE partners benefit. CFOs no longer have to rely on competing definitions to create intelligence that’s based on biased inputs, and partners can be sure that full data transparency is present at all times.
Faster access to insights helps companies make better and more informed decisions. When enhanced financial reports are delivered instantly (and constantly) private equity sponsors can expect higher returns on their investments with faster and more valuable exists.
Building a robust, modern data architecture is dependent on four key elements:
Once a company builds modern data architecture, they achieve one of the most highly beneficial parts of enhanced financial reporting (especially for PE firms), consistent observability.
Most organizations’ data exists in silos and relies on manual ETL (extract, transform and load) processing to get data into data warehouses, where it then goes into Excel or a business intelligence (BI) tool to create reports. This process can take hours, days, or even months to make data usable to extract insights from. Not only are these manual processes more prone to error, but companies and PE firms end up making decisions with historical data.
The problem with the legacy methods lies within the layers. In these disparate systems, data travels through multiple (sometimes hidden) layers on its journey to becoming a financial report. Data from ERPs, POS systems, and other sub-ledgers get mashed together through several manual processes. This process takes several hours to create reports, requires internal SQL skills and internal resources, and is prone to human error.
Enhanced financial reporting and modern data architecture address these problems head-on. Instead of a slow, manual process to extract insights, insights are observed in real-time.
When a new company is added to a private equity portfolio through acquisition, an ERP integration process is typically a part of their 100-day plans. The unfortunate reality for investors is that ERP integrations and accurate financial consolidation can take several months—if not a year—to complete. Businesses spend hundreds of thousands of dollars to integrate disparate systems.
With modern data architecture, disparate systems are connected through a data warehouse layer that standardizes the data for the necessary financial reports. This enables finance teams to start generating valuable insights within weeks of an acquisition, rather than months or years.
Enhanced financial reporting through modern data architecture becomes even more crucial during add-on acquisitions, where regular, reliable insights and reporting can lead to faster value creation. If an enhanced reporting system is in place, bolt-on acquisitions can quickly be added to the existing schema. Financial reports are then readily available for buy-side due diligence, which allows the process to be expedited and completed for much less than the cost of hiring outside help. Faster access to financial reports help PE firms respond to market changes quicker than their competition during the deal cycle. Additionally, having this set up ahead of time can create a faster and more valuable exit.
While having a modern data architecture set up and extracting value through regular and reliable observability are key elements of enhanced financial reporting, one crucial concept is too often overlooked: the metric data layer.
Customer segmentation is more important than ever, especially in the realms of marketing and sales. Companies need to know more about different types of customers and how those customers make decisions so they can effectively market and sell to them. These decisions affect ad spend, outbound sales efforts, and have a large downstream impact on the bottom line.
The problem many companies have is a lack of cohesion in defining these customer segments. Imagine that a company wants to generate financial reports on ‘VIP customers’ that have purchased more than five times. Suppose VIP customers are defined by different metrics across marketing, sales, and finance departments. In that case, the financial reports will be inconsistent and may not provide accurate information, leading to faulty decision-making. The Metric Layer is the answer to this problem.
Prism, Overlay's financial metric layer, gathers data from a company's ERP and other disparate business applications and creates a standardized, unified data set. This cloud-based data set then becomes the go-to source for defining metrics and creating reports. The metric layer acts as a common financial schema used across the company, allowing all teams to work with aligned metrics to focus on generating outcomes, rather than finding and interpreting siloed data.
The metric layer contributes to enhanced financial reporting by
There are four key elements that make the metric layer work.
Once data is pulled from disparate sources via an extract and load process, it needs to be securely stored. Overlay provides a cloud data warehouse to store and access this data from. Now, when employees or private equity partners need to access a portfolio company’s financial data, they can turn to a single, secure source for everything they need.
All too often, ERPs and other business applications won’t have consistent fields or agreed-upon naming conventions. This causes data to get misread and is a blockage to getting useful insights in The metric layer takes fields from many different sources and transforms them into a standardized field for creating financial reports.
Additionally, other metrics, such as what constitutes a ‘VIP customer’ in our example above, can be created and data can be transformed to stay consistent with what ‘VIP customer’ means company-wide.
Now that the data has been extracted, loaded, and transformed within a data warehouse, the metric layer is then applied to incorporate each organization’s calculations of important metrics such as gross margin and EBITDA. Any user or any tool can pull data from this single source of truth. The company and its partners can be sure that all financial reports are consistent, accurate, and up-to-date.
When there is a single source of truth for all financial metrics and reports, executives are empowered to make more informed decisions faster and finance teams can deliver insights with confidence. This saves hundreds of hours from generating reports and allows teams to focus more on analysis and decision-making.
Executives and PE partners need a high-level snapshot of a portfolio company’s financial performance. They need to understand trends over time with revenue, profitability, margin, and more. After pulling all the relevant financial data and filtering it through a single source of truth metric layer, Overlay’s Pulse dashboards answer these questions with a few clicks—which is what enhanced financial reporting is meant to do.
Why has our margin changed?
Why is our customer acquisition cost going up?
Now that the answers to these first-level questions no longer need to be hunted down, executives and partners can start asking second-level questions, such as ‘Why has our margin for a certain product changed?' or ‘Why is our customer acquisition cost going up?’. The questions that matter get asked faster, leading to faster and more-informed business decisions that affect a company’s growth and valuation.
Using Overlay, CFOs can instantly stay on top of and report their most important KPIs. Some of the top KPIs they should track include:
With enhanced financial reporting, CFOs will always have access to these metrics. They’ll never have to hunt down these KPIs again and can purely focus on how to improve them instead.
Without a unified data layer, business intelligence tools such as PowerBI, Tableau, and Looker can only take you so far. You’ll still need to spend time gathering, interpreting, and feeding data into the tools. And instead of having clear definitions, business logic is scattered across various tools and spreadsheets.
In the past, companies had to either hire an expensive consulting firm to build a modern data architecture framework or build it themselves. Both options are costly and time-consuming. Overlay provides a third option: add a metric layer and get daily enhanced financial reports. Not only does enhanced financial reporting happen automatically, but it’s also built for a fraction of the cost when compared to other options.
Executives should always be on top of their KPIs, with full situational awareness of the state of their company or portfolio companies. Overlay provides this intelligence and awareness through the added metric layer and enhanced financial reporting.
See how Overlay can power your business intelligence with a metric layer.