Author: Deepak Dhirasaria
Data has become the lifeblood of many industries as they unlock the immense potential to make smarter decisions. From retail and insurance to manufacturing and healthcare, companies are leveraging the power of big data and analytics to personalize and scale their products and services while unearthing new market opportunities. However, it has been proven that when the volume of data is high, and the touchpoints are unsynchronized, it becomes difficult to transform raw information into insightful business intelligence. Through this blog series, we will take an in-depth look at why data analytics continues to be an elusive growth strategy for Private Equity firms and how this can be changed.
For starters, Private Equity (PE) firms have to work twice as hard to make sense of their data before turning them into actionable insights. This is because their client portfolios are often diverse, as is the data – spread across different industries and geographies, which limits the reusability of frameworks and processes. Furthermore, each client may have its own unique reporting format, which leads to information overflow.
Other data analytics-related challenges that PE firms have to overcome include:
– No reliable sources and poor understanding of non-traditional data
– Archaic and ineffective data management strategy
– Inability to make optimal use of various data assets
– Absence of analytics-focused functions, resources, and tools
These challenges offer a clear indication of why the adoption of data analytics in the private equity industry has been low – compared to others. According to a recent study conducted by KPMG, only a few Private Equity firms are currently exploring big data and analytics as a viable strategy, with “70% of surveyed firms still in the awareness-raising stage.”
Why Private Equity firms need to incubate a data-first mindset
So, considering these herculean challenges, why is a data analytics strategy the need of the hour for Private Equity firms? After all, Gartner has said that by 2025, “over 75% of VC and early-stage investor executive reviews will be informed using AI and data analytics.”
First, it’s important to understand that as technology continues to skyrocket, a tremendous amount of relevant data is generated and gathered around the clock. And without leveraging data to unearth correlations and trends, they can only rely on half-truths and gut instincts. For instance, such outdated strategies can mislead firms regarding where their portfolio companies can reduce operating costs. Hence, the lack of a data analytics strategy means they can no longer remain competitive in today’s dynamic investment world.
Plus, stakeholders expect more transparency and visibility into the valuation processes. So, Private Equity firms are already under pressure to break down innovation barriers and enable seamless access and utilization of their data assets to build a decision-making culture based on actionable insights. They can also proactively identify good investment opportunities, which can significantly help grow revenue while optimizing the bandwidth of their teams by focusing on the right opportunities.
Some of the other benefits for Private Equity firms are:
• Enriched company valuation models
• Enhanced portfolio monitoring
• Reduced dependency on financial data
• Pipeline monitoring and timely access for key event triggers
• Stronger due diligence processes
The emergence of data analytics as a game-changer for Private Equity firms has caused some to adopt piecemeal solutions – hoping that it could reap low-hanging fruits. However, this could prove to be hugely ineffective because it would further decentralize the availability of data, which has been this industry’s biggest problem in the first place.
In reality, the key is for Private Equity firms to rethink how they collect data and what they can do with it – from the ground up. There’s no doubt that only by building a data-led master strategy can they make a difference in how they make key investment decisions and successfully navigate a hyper-competitive landscape.
We hope that we helped you understand the current data challenges Private Equity firms face while adopting a data analytics strategy and why it’s still a competitive differentiator. Stay tuned for the next blog in the series, in which we will shed light on how Private Equity firms can overcome these challenges.Tags: AI in financial services AI in private equity