Integrating Data Across Portfolio Companies: Best Practices and Tools

Every company operating in private equity is on a journey to become a data-driven organization. As portfolios expand and become more heterogeneous, firms need a way to connect data from portfolio companies.

Private equity firms have traditionally struggled with fragmented data across their portfolio companies. Legacy systems, disparate reporting tools and metrics, and a lack of centralized operational platforms have created organizational blind spots and hidden opportunities for value creation.

Enter data integration. 

Forward-thinking firms are working towards creating centralized data ecosystems backed by powerful analytics and scalable digital platforms.

Integrating data from portfolio companies allows firms to reduce dependency on spreadsheet-heavy reporting and transition towards continuous intelligence.

Companies like Hexaware empower private equity firms with digital engineering, data platforms, and analytics to help eliminate data fragmentation and accelerate business decisions.

Why Data Integration is Critical in Private Equity

Portfolio Complexity is Increasing Rapidly

PE firms invest across different industries, geographies, and stages of operational maturity. Each portfolio company may operate on different technologies and platforms, including:

• ERP software 
• CRM platforms 
• Finance tools 
• Data repositories 
• Analytics software 

This variation results in a data ecosystem that isn’t connected and takes longer to analyze and report.

Access to Continuous Investment Intelligence Is Required

Investors and operating partners have realized that quarterly reporting is not enough. They want visibility into important metrics on a continuous basis. Metrics such as: 

• Sales and revenue 
• Operational metrics 
• Customer acquisition costs 
• Supply chain efficiencies 
• Digital transformation status 

Having integrated data enables firms to monitor and report on these metrics in near real time.

Let’s take a look at what fragmented data looks like in private equity.

The Anatomy of Fragmented Data in Private Equity

Fragmented data exists when data points are siloed within systems and aren’t standardized or connected to other systems.

Sources of fragmented data in private equity include:

Outdated technology: Many portfolio companies operate on outdated software systems that don’t integrate well with others.

Erratic data definitions: Different companies may understand the same metric in different ways—e.g., net revenue. 

Manually created data: Data that is logged into Excel, Google Sheets, and other forms of spreadsheets.

Lack of data standards: Each system may have its own owner, allowing data standards to diverge.

Fragmented data causes several issues that affect firms’ operational efficiency.

Issues caused by fragmented data include: 

• Slow investment research 
• Inability to benchmark company KPIs against other investments
• Incomplete view of performance 
• Missed opportunities for cost savings and revenue generation
• High operational costs 
• Increased reporting time and effort due to messy data
• Loss of confidence in reporting 
• Greater risk of human error in spreadsheet-based reporting

Core Principles of Data Integration in Private Equity

Standardized Data Models

Data integration does not imply that all your portfolio companies should use the same systems. However, you should strive to build a centralized data layer that gives you access to information.

Common Data Definitions 

Common definitions should be agreed upon. For example, what does revenue or EBITDA mean to your organization? These definitions allow you to easily compare investments against each other.

API-first Integration 

Integration should not be done by forcing systems to communicate with each other. Adopting a strategy where integrations are powered by APIs allows for flexibility when connecting systems.

Cloud-native Infrastructure

When building your tech stack, try to use cloud platforms. They offer scalability, security, and allow you to access your data in real time.

Designing a Tech Stack for Data Integration 

An important part of building your data integration strategy is ensuring that your tech stack allows for data to be integrated and analyzed across your portfolio companies.

The following is an example tech stack that allows for data ingestion, processing, and analytics:

Data Ingestion Layer

Every modern tech stack should have a layer that automates the collection of data, such as: 

• ERP data 
• CRM data 
• Finance data 
• Market data 

Tools should be able to extract this data and transform it into a format that you can store.

Data Storage and Processing Layer

Once your data is extracted and transformed, it needs to be stored somewhere. This is where cloud data lakes and warehouses come in.

Advantages of having a cloud data lake include:

• Ability to scale 
• Highly processable 
• Easy to access

Integration Middleware Layer

Middleware acts as a glue that connects your software applications. Using an integration platform as a service (iPaaS), you can create workflows that utilize APIs to integrate your systems in real time.

Analytics Layer

You wouldn’t have collected data if you weren’t going to do something with it. Using a dashboard tool, you can visualize your data for your investment teams.

Automation and AI Layer

With machine learning, you can build models to forecast future revenue, analyze risk, and optimize performance.

Hexaware enables customers to architect and build data ecosystems leveraging cloud engineering, automation, and analytics. Learn how Hexaware’s data platform empowers you to build a single source of truth across your portfolio companies.  

Best Practices for Integrating Data Across Portfolio Companies

Align Data Integration With Your Strategy

Every integration effort should start with a strategy. Things to define in your strategy include: 

• Your objectives 
• Data owners 
• Data governance 
• Target architecture 

Create a Uniform Set of KPIs

KPIs are generally unique to each investment. However, creating uniform definitions allows you to benchmark portfolio companies against each other. Some examples for which you can create uniform KPIs include: 

• Revenue 
• EBITDA 
• Customer churn 
• Cost of operations 

Use a Modular Approach

Changing your systems all at once can be expensive and time-consuming. Approach your integration efforts by integrating one system at a time.

Invest in Data Governance

Data governance includes, but is not limited to:

• Data quality 
• Permissions and access 
• Regulatory compliance 
• Metadata management 

Enable Self-service Analytics

Analysts should not have to wait weeks for the data team to retrieve data. Building dashboards that allow your teams to access data will enable quicker decisions.

Private Equity Data Integration Use Cases

Below are some common ways investors integrate data across their portfolio companies:

Deal Sourcing and Screening

By integrating your market data with your portfolio data, you’ll have better visibility in identifying deals.

Due Diligence

Access to integrated data will help you spend less time digging through documents and more time working on the deal.

Portfolio Monitoring

Integrated data will allow you to create dashboards that give you real-time insight into your portfolio company’s performance.

Exit

Just like due diligence, access to integrated data will save you time when you’re planning your exit.

How AI Can Help You Eliminate Fragmented Data

AI solutions can help accelerate your data integration strategy by:

• Automatically mapping out data fields 
• Cleaning your data 
• Running predictive analysis 
• Helping you identify outliers in your data 

Integrating data manually is a laborious task. A better approach to data integration strategy is to leverage AI to automate tasks.

How People Impact Your Integration Strategy 

Technology is not the only factor to consider when integrating your data. Here are a few people-related considerations:

Collaboration between teams: Investment and technology teams should align when developing an integration strategy.

Change management: Portfolio companies may resist integration efforts, so proactive change management is essential.

Skills: Employees will need training in data engineering principles and programming language skills such as Python.

Governance Models for Your Integrated Data Platform

Building a governance model is critical to your integration strategy. Things you should define include: 

• Who owns what data 
• Security standards 
• Compliance checks 
• Continuous improvement processes

Data Integration Challenges

Like any other tech strategy you encounter, you will face some challenges when integrating your data.

Legacy Technology 

Solution: You can integrate your legacy technology using middleware.

Data Quality 

Solution: Validate and clean your data using AI data tools.

Complexity 

Solution: Build your integration platform on the cloud and use an API-first approach.

Culture 

Solution: Show your employees how integrated data can help them do their job better.

Partners Accelerate Data Integration in Private Equity

Every firm can leverage technology partners to accelerate its data integration plans. At Hexaware, we empower our customers to:

• Build data platforms 
• Modernize legacy systems 
• Leverage AI to build predictive analytics solutions 
• Drive digital transformation

Emerging Trends in Private Equity Data Integration

There are always new trends that come about when it comes to technology. Some trends to look out for with data integration include:

Data Mesh Architectures

A data mesh allows for a decentralized data ownership structure. However, using standardized data models and definitions, data can be integrated.

Real-time Data Streaming

Data streaming allows for data to be ingested in real time. Having an ingestion layer that collects data in real time allows you to monitor your portfolio companies on a continuous basis.

AI Co-pilots for Investment Teams

AI-driven tools can offer your investment teams contextual knowledge based on your integrated data.

Cross-portfolio Benchmarking Platforms

By analyzing your data in a centralized location, you can easily benchmark each portfolio company against the others.

Measuring the Success of Your Data Integration Strategy

You should always measure the success of your integration strategy. Some metrics you should measure include: 

• Reduction in time spent reporting 
• Data accuracy 
• Time to make a decision 
• View of performance 
• Operational efficiency 

Data integration done right will give you a competitive edge over your competitors. Access to a unified view of your portfolio companies allows you to make faster decisions and have a better view of your investment’s performance.

Conclusion

Data integration allows PE firms to transition from a world of siloed information to a world of connected intelligence. With the right strategy and tech stack, private equity firms can leverage data integration to create a single source of truth across their portfolio companies.