Sell-Side Readiness: How a Common Data Model Helped One Private Equity Company Achieve a Successful Exit
The Challenge
A private equity (PE)-backed global chemical manufacturing company was seeking to exit its investment and needed comprehensive financial information to evaluate the business and provide data to potential buyers. The company had been actively acquiring new businesses resulting in sales and product data representing 23 businesses stored in seven different databases worldwide. With disparate data sources, the company could not deliver key financial and operational key performance indicators (KPIs) to its PE sponsor and the investment bank leading the sell-side process.
To meet the PE sponsor’s requirements for consolidated financial reporting, the company engaged Riveron to transfer its segmented product and sales data into a single cohesive system of record. The system needed to be compatible with the company’s existing business intelligence application, Microsoft Power BI.
Reviewing data before initiating the sales process can maximize the business value and marketability. It can also streamline the due diligence process, reduce risk for buyers, and ensure a smooth transition.” – Benny Mo, Director, Transaction Services
How Riveron Helped
Applying extensive experience with database development and helping companies prepare their data for the rigors of a sell-side process, Riveron experts worked closely with the company’s technology and FP&A teams to get a complete inventory of all the global data sources. To create a consolidated database that could provide insights into the company’s sales and market trends, the team initiated a multipronged plan that would deliver a robust and comprehensive sales analytics tool.
Data acquisition, review, and analysis
As part of the process, Riveron reviewed global profit and loss (P&L) statements, balance sheets, and Quality of Earnings (or financial due diligence reports) and incorporated these elements into the new database. The team’s financial analysis also included identifying unusual trends and resolving previously undetected data mapping issues. They also consulted with the company’s CFO and corporate controller to understand the sales and general ledger data and map that to the financials to resolve unreconciled variances.
Incorporating siloed data into a common data model (CDM) enabled us to capture 100% of the company’s global sales, finance, and product information to support sales analytics in support of an exit of a portfolio company.
Optimizing the data to align with the deal thesis
The team determined that a common data model (CDM) would be the optimal solution. Through Microsoft Power BI, the CDM could warehouse the consolidated data and fulfill queries for more detailed global reports (e.g., brand, channel, geography, product, customer, end market, etc.).
Before populating the database, the team cleaned the data and determined its accuracy and reliability. The data was built into the CDM as a sales cube (a data structure that can be viewed in more than two dimensions) and layered in data from the multiple ERPs.
The approach was structured for better visibility of the data, and the team collaborated at a speed that enabled deal readiness. First, the consolidated data was reclassified by product type. The team further grouped the data into solution sets and partnered with the FP&A function to repackage the data for marketing the business. We identified which channel the products were going to and recategorized the data into solution groups by industry. With this new view of the data, the deal team could focus on the value proposition for each product and how bringing the solution to market fits the overall landscape. At this point, the team could work at deal speed to provide the necessary dashboards and data slices to the potential buyers.
Results
The collaborative efforts of the Riveron transaction and data analytics experts with the company’s IT and FP&A teams created a clean, consistent, and accurate set of consolidated data to facilitate the sales process and meet the requirements of buy-side due diligence. Now, the company’s new PE sponsor can quickly determine the businesses’ profitability by product. The team can also perform future forecasting to develop a more detailed investment thesis.
A Centralized Cohesive Dataset
After data integration and optimization, the company had 100% of its global sales, finance, and product data in a single database. Before this time, only 20% of the company’s data had been centralized.
Better Data Categorization Leading to Additional Financial Insights
By transitioning from product-based to solution-based data categorization, the company reduced inefficiencies and data loss impacting their sell-side M&A opportunities. The team could also record data at the transaction level rather than the product level, which enabled the sponsor to determine profitability by SKU information and other subsets of data.
With the new CDM, the sponsors can see operating and financial information that will provide value even after the sale. This allows the team to leverage the existing infrastructure to run financial reports every month.
Eliminating Manual Reporting Processes
With BI tools and automation, the company’s sales data can be manually refreshed monthly or quarterly.
Quantifying the Company’s Future Value
The CDM provided comprehensive reporting capabilities, enabling the sponsors to highlight the value proposition and how to bring the company to market, explain its fit into the overall landscape, and determine opportunities that can be explored by the buyer post-acquisition.