Your Checklist: Due Diligence in Private Equity
The Overlay Team
Private equity firms have long leveraged data analytics to ensure they maximize value and returns on each deal. However, the industry has seen a shift towards leveraging the latest big data technologies, such as machine learning, cloud computing, and virtualization.
In this checklist, we break down how operating partners can use new technologies and methodologies today to improve their investment decision-making process for better returns in the future.
Data volumes continue to expand at an unprecedented rate. According to a survey by Matillion and IDG, the average company draws data from over 400 sources.
Additionally, data professionals responding to the survey indicate their data volume increased by 63% per month.
The massive, unstructured data obtained right after acquisition can overwhelm operating partners for private equity firms looking to grasp the full scope of the company’s data in real-time.
The enterprise value of data is extremely time-sensitive. Information that’s invaluable today may become worthless tomorrow. Private equity firms can leverage modern data architectures to minimize the travel time from the data source to the point of consumption or decision making.
Using a data pipeline through an Extract, Transform, and Load (ETL) platform can increase the velocity of collection and data processing. Data lakes are becoming increasingly popular for PE firms that wish to cut their implementation time.
When private equity firms purchase companies, they typically inherit a variety of data sources. Your firm can begin receiving meaningful information from the silos or warehouses by leveraging modern data architectures.
The key is to identify the data sources that will provide the most value at each stage of the acquisition process and then ensure that those sources are accessible when you need them.
Key performance indicators (KPIs) provide insight into how well your company is performing relative to goals and objectives set by management. Once you have KPIs in place, you can use OKRs (objectives and key results) to determine how best to achieve those KPIs.
Cloud-based platforms like Overlay allow PE firms to receive daily KPI updates, keeping CFOs informed about performance changes in their portfolio management. You do not have to wait for EOW or EOM to act promptly on data received.
Integrations are an important part of any modern data architecture, but they can be difficult to implement. They are often expensive, time consuming, and hard to maintain. The integration process involves several steps that you must consider carefully for the overall strategy to work effectively.
It is vital to establish and maintain a clear understanding of your needs (and those of your partners) regarding data sharing and access rights.
Devise a technical plan with the right technology partner(s). You may want to consult with an experienced integration provider that has experience working with other private equity firms or other industry experts, such as system integrators or technology service providers, who have worked on similar implementations in other organizations.
You need to carefully plan the organizational components for the modern data platform you wish to build on.
Data lakes are cheaper options for PE firms that wish to create a large repository for all data collected, making metadata tags to identify when, where, and how the platform collected the information. Automation with business intelligence frameworks allows for faster processing of data.
These are modern data structures with unified security and governance platforms. Enterprise data architectures provide a one-stop shop for executives who wish to obtain all of the information about their portfolios within a single system.
Data fabrics are interconnected systems linking different information silos and disparate sources. You can achieve better visualization through access to layering options which create queries across multiple tables and platforms.
Planning your data management and integration early, before acquisition, can smoothen the process of receiving important insights on portfolio management.
Leveraging modern data architecture can enable you to maximize the deal value and returns by accommodating incremental changes. You do not need to have an all-or-nothing proposition.
You can build incrementally on your data management and analytics systems components. This can minimize the risk of jumping right into a deep upfront analysis during the acquisition phase, which could slow the process down.
As you hold onto your investments, you can gradually increase the number of subscription-based features that you can incorporate into the data system to improve the data quality you receive.
Incremental changes may also help you to plan your exits effectively. Cloud-based technologies enable you to scale up or down, depending on your needs. Access to interim products will allow you to test workable features for your final platform, creating a custom data structure for your PE firm.
According to IBM, more than 80% of enterprise data collected is unstructured. The non-traditional data sets may include call transcripts, system logs, IOT sensory data, customer complaints, etc. Sadly, most PE firms cannot use unstructured data.
Running your private equity firm on only 20% of available information is grossly suboptimal. You need to find ways to take advantage of the semi-structured or unstructured data.
Modern data architecture through data lakes can effectively store the information in its raw format within non-relational databases (NoSQL). Harnessing these alternative data sets can allow you to identify consumer behavior, purchasing patterns, and buyer sentiment for your investments.
The importance of quality and timely data to private equity firms cannot be overstated. The right architecture will enable the success of your business, but choosing which architecture is the right one can be a daunting task.
The idea is to balance your high-level business objectives and your immediate IT needs. Look at what types of systems you need, prioritize them based on risk and reward, then make sure they fit within an overall strategy that will enable future scalability.
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