In our data-driven economy, the need for better data strategies, more advanced data centralization, and effective data consolidation practices keeps growing. For both individual businesses and private equity sponsors, finding synergies across internal units or a portfolio of companies is a must, as is having absolute transparency into “the numbers.” Some CFOs and their organizations enhance data operations for those purposes, but far too many are not. And that could be a costly mistake, especially if the company plans an exit in the near term.

Traditionally, management viewed data centralization platforms like data warehouses as cost centers under the chief technology officer’s purview. However, as businesses and sponsors face ongoing pressures to maximize return on their investments, strategies like data warehousing became increasingly part of a strategy to generate profits.

Without a data warehouse, operational insight into portfolio companies or lines of business is limited. That could hurt a company’s ability to act on market advantages. In an exit scenario, the consequences could be severe. A lack of insight could mean missing out on a higher premium. Additionally, it could lead to less-than-optimal exit timing or a lack of transparency, foreshadowing stagnant growth and a lower ROI.

Building a data warehouse and injecting a data-driven mindset into portfolio companies or a single corporate culture may sound daunting. But it doesn’t have to be. And the benefits can be numerous. 

For a company, a data warehouse can lift the topline and raise margins, streamline operations, find growth and profit drivers, and provide insight into critical performance metrics. Across a sponsor’s portfolio, a data warehouse can spotlight comprehensive market trends, help benchmark portfolio companies, identify optimal exit strategies, and find cross-company synergies. 

Contextualizing Sources

Contextualizing multiple data sources and historical data increases performance transparency and identifies growth drivers. Reaping these benefits requires asking the following questions:

1. What kind of data analytics and reporting system does the organization have, and is it up to the task? Many systems have built-in analytics and reporting capabilities but can’t contextualize across all of the portfolio’s enterprise resource planning (ERP), customer relationship management (CRM), and human resource (HR) systems. 

2. Do current approaches to data consolidation and streamlining meet the company’s needs?  Data warehousing applies to any form of a data consolidation platform (including data lakes) from which teams can perform analytics. Data warehousing allows for the centralization and consolidation of more structured data to streamline analytics. It can handle metrics such as sales leads vs. conversions and bookings versus revenue, measures traditionally hard to analyze because they come from disparate data sources. 

3. What are the data needs pursuant to the red flags and alerts required to optimize operations? For example, alerts to significant bumps or declines in a product’s sales can enhance agile supply chain and inventory decisions.

4. How will the company implement data warehouse best practices involving master data management, scalability, acquisition integration, change management, and data integrity/exception handling? Establishing defined roles, responsibilities, and reporting procedures — along with the consensus on the type and granularity of data required — is critical.

Benchmarking the Portfolio

Once the organization has determined the multiple data sources and historical data going into the data warehouse, it must consider how best to benchmark. The line of sight into operations provided by data warehouse inputs should be benchmarked across business units or holdings. Doing so helps ensure exit strategy timelines and projected returns. Here are the steps.

1. Establish streamlined data pipelines of transactional and granular data from each unit into the data warehouse. That entails identifying the data needed from each portfolio company or division and any “data gaps” that need filling in.

2. Analyze customer trends to understand market headwinds and tailwinds. What are the key performance indicators (KPI) for best understanding, foreseeing, sizing, and seizing opportunities?

3. Identify potential synergies across businesses. Relevant data sources and historical data may vary if, for example, a sponsor has a multiple-industry portfolio rather than a single-industry one.

A Robust Data Strategy

Decision-making rooted in granular, transactional data will only improve. Data analytics:

• Enables and enhances performance improvement. For example, in a takeover, it expedites sell-side plans by streamlining the sales process and preparing the confidential information memorandum (CIM) and other marketing materials more efficiently.

• Provides a pathway for increasing gross profit and revenue through pricing optimization, spend cube analysis, and stock-keeping unit (SKU) rationalization. When correctly leveraged, data can identify growth drivers and better craft the investment thesis and value proposition. 

• Increases margins by cost optimization. The insights generated can help reduce full-time equivalent (FTE) employees and customer acquisition costs and better inform SKU rationalization. 

Knowledge is power. Moving forward, a critical determinant of success will be getting smarter about converting data into knowledge. Those who continue to view data as only a cost will be passed by.

Steven Lee is a managing director with Alvarez & Marsal’s transaction advisory group. Joey Baruch is a chief technology officer with Alvarez & Marsal Data Intelligence Gateway (DIG).