Ask a child what they think of when they hear the word “big,” and they might mention elephants and whales, or the sun and the ocean.
Ask a group of people who follow technology the same question, and they may all have the same answer: data.
The amount of data being generated every day is staggering, and growing each year. By 2020, it’s estimated that for every single person on earth, 1.7 MB of data will be created every second. It’s a number so large that it’s incomprehensible.
This explosion of data is both a challenge and an opportunity for private equity firms and the companies they buy or acquire. There are actionable insights buried within this mountain of data that will allow companies to make better business decisions – and harnessing this information requires investing in big data systems and technologies.
There are four important reasons a private equity firm needs big data for its portfolio of managed investments:
1 – Big data provides a 360-degree view of a business.
Big data allows a company to combine historical and current data through system consolidation, then build algorithms on top. Firms then can use trends and predictions to identify how to optimize assets, provide a better customer experience, and streamline operations within its investment – because more data analysis means better answers.
2 – Big data is necessary to revive a failing brand.
Private equity firms often acquire companies in distress, and because the company is in trouble, it likely has not bothered upgrading its IT infrastructure or implementing any advanced techniques to improve its technological back end.
Private equity firms can start with a technology audit to review what kind of infrastructure exists and what type of data is being generated. Once the technical due diligence has been performed, the firm can begin collecting all of an investment’s data, refining it, consolidating it and analyzing it to derive value from an asset that likely was not being used to its full potential before.
3 – Big data is critical during M&A.
When two large or midsize companies merge, or when one company acquires another, each company come with its own systems and operational processes. Big data can help bring the disparate processes together, creating a consistent view of what’s happening over the two companies.
For example, both companies will have their own groups of customers, and there likely will be some overlap. Big data allows both companies to merge these lists, see who the common customers are, and create one master customer list – and this applies to all areas within a company, including products, employees and so on. While both companies can maintain their own data within their legacy systems, the big data system reveals the commonalities, ensuring insights can be extracted based on all available information.
4 – You can’t get to digital transformation without big data.
Traditional data types were structured, fitting neatly in a relational database. Now, data comes in new, unstructured or semi-structured data types, such as text, audio and video, which require additional preprocessing to develop meaning and support metadata. Big data helps organizations combine structured historical data with unstructured data (e.g., social media posts) and make it all actionable with predictive analytics that shape the company’s future – bringing a company closer to true digitization.
Big data sets will also provide the foundation upon which machine learning platforms can be built. Using big data to fuel a machine learning program will yield predictive analytics that self-update in real time as data changes, revealing to a firm what’s happening in a company right now as well as what should be happening in the future.
Big data is not intended to replace existing systems – rather, it augments them – and there’s no single software, solution, technology or answer when it comes to big data. Technology becomes obsolete quickly these days, and firms need to continually reinvest in their companies’ technology solutions in order to stay up to date and keep moving forward in the long march to digital transformation.
