In the business world of today, one of the most popular topics is data analytics. With its adoption widening among companies, it now sees use in transforming several areas such as business development, HR, operations, and sales and marketing. And this is applicable not just in one particular industry but in fact across a wide spectrum.
However, when it comes to the work of private equity professionals, the transformation has been much slower. Certain analysts rank something as old as Microsoft Excel the last big innovation in the field!
In recent years, there have been some changes in this status, with some of the best private equity consulting firms looking to use data analytics. Key points in this regard include the following:
- It can help to understand and project the revenue of a portfolio company. This includes:
- Transactional due diligence: Understanding business from the bottom up
- Creating value post-investment: Driving operational improvements in a portfolio company after properly understanding how it performs
- Technology is an important enabler. Large-scale cloud-based engineering helps to handle very large data sets in very short timeframes. In-depth methods allow viewing of the happenings in business as they happen i.e. on a minute-to-minute, day-to-day basis.
- Accurate prediction of the growth of a portfolio company is hard, and data analytics makes it easier. Presently, most associates at a private equity firm follow the crowd rather than taking a more informed decision. Data offers a much more granular perspective by counting every customer and transaction.
- Data can be understood by combining multiple standardized analyses and statistical machine learning (ML) models, which help figure out how a company is doing and whether there are opportunities to change things.
- Speed is a major advantage. In commercial diligence, the time available to make a go/no-go decision on a deal is typically no more than 4-6 weeks. Data analytics allows private equity professionals to take in large data volumes and quickly understand the likelihood of returns from a target company.
Conventional wisdom often falls at the altar of data analytics. For example, in the case of a business operating in multiple regions, conventional wisdom might look at a particular critical region with many competitors and judge that it is mature, with no likelihood of much growth. However, the best private equity consulting firms may choose to examine data more deeply and realize the market was in fact at the beginning of a growth trajectory that would persist, judging by the pace and acceleration with which customers were coming into the system.
For those considering careers in private equity, it would be erroneous to ignore data analytics, due to the following reasons:
- Many investment firms rely on CRM systems such as Salesforce, which do not do much other than logging companies and data without integrating data analytics and data layers on top.
- A management team that resists data analytics should raise questions in the mind of a private equity investor, given that it could mean the company is trying to get the highest valuation by not keeping its data fully transparent.
Private equity firms are beginning to gravitate toward portfolio companies with an ability or capacity to support data analytics.
What do the best private equity consulting firms need to have to adopt data analytics faster? Having a strong team could be the biggest differentiator for an analytics-savvy private equity firm. It is useful to have skills such as private equity domain expertise and consulting expertise coming together with data scientists and engineers backed by PhD-level research, the latter typically being seen at Internet behemoths like Facebook and Google. These bring a level of capability thus far not seen in the industry.
In the past two decades, there has not been much change in the pace at which data analytics is being adopted by those working in private equity jobs. Key factors to consider include:
- Low-velocity business: It is not uncommon for private equity firms to do just 2-3 deals in a year. Hence, even if sophisticated algorithms are in place, seeing the results of their assessments could take some time.
- Low availability of tools and data: A cohort analysis (grouping customers by similarities to understand their behaviour and growth) was always conducted, but data analytics is now making its place with more data and tools becoming available.
- A shortage of talent: Successful application of data analytics requires that people looking for careers in private equity should also bring in skills in data analysis, and should be able to use the available tools.
- Resistance to change: There is always the factor of human inertia. The industry needs to sit up and agree to embrace something new and useful.
Adoption of data analytics ultimately will become about culture rather than tools. The best-positioned private equity firms will be those who successfully integrate data analytics into their thinking.
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