How private equity firms can drive value through strong data strategies in their portfolio | Baker Tilly

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How private equity firms can drive value through strong data strategies in their portfolio | Baker Tilly

Data as an asset

Over the past 50 years, in S&P 500 companies there has been a significant shift between the balance of tangible and intangible assets. According to a report by consultant Ocean Tomo, in 1975, 17% of these companies’ asset value was in intangible assets; by 2020, the percentage of assets that were intangible had increased to 90%. The top U.S. companies by market capitalization in 2020 – companies like Apple, Facebook and Google – book the majority of their value in the data that they capture and by leveraging that data.

From a private equity fund perspective, thinking about data as an asset means empowering its portfolio companies to invest properly in its people, process and technology not just to improve operations but to leverage data as an asset, which may improve the valuation of the portfolio company.

Data driving value

The three main areas a company can drive value using its data are through its customers, suppliers, and operations.

Companies can use their customer’s data to improve the acquisition and retention of customers; to enhance existing products or introduce new ones; or by bartering or exchanging value with other companies in a way that mutually beneficial.

Better use of their supplier’s data can help a company negotiate more favorable terms, conditions, and relationships. Companies can also collect information on how their suppliers are performing to improve their collective buying behavior with them.

Properly using operations data can help companies reduce maintenance costs and improve identification of risk and fraud.

One way for a company to show that it is treating data as an asset is to make a proper investment in their people and technology to better identify, curate and protect data. Treating data as an asset also means making it more accessible throughout the organization to enable people to make strategic decisions faster - such as determining the right markets to expand to or learning about the buying habits of customers.

Data maturity model

When it comes to data maturity, not all organizations are in the same place. We have identified four stages of data maturity:

Descriptive: where an organization is looking at data in hindsight, determining “what happened?”
Diagnostic: based on historical data, the organization asks, “why did something happen?”
Prescriptive: where an organization, with more insight and control over its data, can make a more educated guess on “what will happen?”
Predictive: by using more advanced data analytics, the organization can effectively create a strategy around “how can we make it happen?”

Building the data strategy

Data strategy should be considered a part of the strategic landscape, like marketing and HR. Key elements of a data strategy include data governance, data monetization and data management. The strategy could be as simple as reducing the number of steps to complete a process. It could be something tactical, that needs to be completed in the short term, or a strategy that will be fulfilled over many months.

A proper data strategy commonly has four components that work in tandem:

People: Employees must understand and properly leverage data to make informed decisions.
Process: Well-established processes must be in place to ensure data is ingested, stored, delivered and consumed properly.
Technology: The right tools must be in place to allow data efforts to launch, evolve, mature and scale with ease.
Organization: From the top down, the organization must be aligned, compatible and committed to the strategy.

If an organization uses only a couple of these components in creating their data strategy, they may find themselves in the descriptive or diagnostic side of the maturity model, using a team of people or a specific technology tool to simply manage data but without the ability to use data to drive growth.

Data strategy approach

The elements for a successful data strategy include discovery, analysis and design, and prioritization and planning.

The key objective in the discovery stage is to identify stakeholders and interview them to understand the overall current state of data usage and how it aligns with the overall business strategy.

In the analysis and design stage the organization will classify data domains that need to be addressed first based on business value and feasibility scoring. Then, it will identify the solution concept to meet high-level requirements and the project roles required to deliver the solution.

In the final prioritization and planning stage the organization will produce an 18- to 24-month road map to sequence the activities that are required to establish and deploy their data and analytic use cases. This includes crafting the resource plan for the acquisition of the right personnel and technology. This final step includes designing a deployment and adoption model to assist in the organizational change management necessary to drive the successful deployment of the plan into the organization.

Key success factors

It’s important for an organization to connect its data analytics use cases to business outcomes; this connects the dots between the technology and the business and helps further define and illustrate the value and impact of analytics. Measuring what matters and establishing targets and timeframes are critical to driving business goals. This is all part of making the data strategy CLEAR.

Clarity means the data strategy effort needs to align with the expectation created within the organization as to what the data strategy is. The data strategy must be positioned as a key corporate priority and so requires sound leadership in the data organization to drive and support any revision to an existing corporate strategy. Execution of a data strategy is critical, so it is important to consider that the proposed strategy is well-understood, actionable, aligned to company culture, and has support across the organization. A data strategy is not a straightforward, linear process, so agility in the organization and data team are required to allow the strategy to be refined and revisited as more knowledge and inputs are captured. Finally, the data strategy team must ensure the strategy remains relevant to the ongoing existence and success of the organization.