“If you always do what you always did, you’ll always get what you’ve always got.” – Henry Ford
Deloitte provides a clear and simple answer to this question in their “Analytics for Manufacturers” guide. Their bottom line? Data-driven insights are a strategic necessity.
In recent years, there is no business discipline that has moved to the forefront of the business community’s collective consciousness more than business intelligence.
The following chart illustrates Google search interest in “business intelligence” topics over the past 5 years.
With consistently increasing deal multiples across markets, the opportunity to acquire bolt-on companies in pursuit of multiple-arbitrage has made buy-and-build strategies more popular than ever.
In their 2019 Global Private Equity Report, Bain confirms “Buy-and-build can offer a clear path to value at a time when deal multiples are at record levels and GPs are under heavy pressure to find strategies that don’t rely on traditional tailwinds like falling interest rates and stable GDP growth.”
Business intelligence is one of the hottest topics among industry leaders. Private equity firms are no exception. However, through our exposure to hundreds of companies, precious few have shown up having successfully rationalized their data assets into a functioning toolset capable of accelerating growth.
Even the smallest companies produce a significant amount of data that represents untapped value. So why don’t more companies exploit the resource?
If you believe better data is one of the top drivers of growth, you are not alone.
According to a 2019 survey, "Fortune 1000 companies are now recognizing that they must become more adept at leveraging their data assets if they are to compete successfully against highly agile data-driven competitors." - MIT Sloan Management Review
In the quest for differentiation among buy-and-build PE firms, everyone seems to be pointing to data as the next business alchemy.
The terms Big Data, Machine Learning, and Artificial Intelligence are bandied about at workshops and industry events as though they’re well-understood and commonly applied.
In the race to increase portfolio company value through add-ons, improved gross margins, and operational efficiency, private equity managers and their operating team counterparts often find themselves too busy chopping wood to stop and sharpen the axe.
Successful companies that are attractive to future investors need actionable data and clear KPIs to provide transparency and focus.
Management teams are selected for the expertise needed to usher their companies to a liquidity event. Without clear visibility into performance, deal teams can't responsibly give their management teams the latitude they need to apply their efforts and talent to their highest and best use.
"If you want to motivate employees, stop following your instincts and adopt a data-driven approach." - Harvard Business Review
Data is trending, and while private equity deal teams increasingly give lip service to the importance of data-driven operations, their tactics often miss the mark. Of the hundreds of companies we've consulted for, nearly all default to Excel spreadsheets, PowerPoints, and PDFs to promote data intelligence.
"Transparency is not just a buzzword; it may be a necessity for business survival in the 21st century." - Glassdoor
Successful execution is what matters most, even if the model has been around for decades. Airbnb wasn’t the first clearinghouse for C2C home rentals, but its execution was unprecedented. Excite, Yahoo, and Ask Jeeves came first, but none delivered as successfully as Google.
“If you want to improve the quality of performance in any area, improve or increase the frequency of the feedback.” — Charles Coonradt, The Game of Work
For many private equity firms, managing a portfolio of companies can feel more like controlled chaos than strategic execution. Rather than spending time on key partnerships, hires, and acquisitions, deal teams are often consumed chasing down performance data in an effort to understand what's happening on the ground. Why? Because manually harnessing data is often a herculean task that interferes with daily operations.