Many organizations in Canada, including private equity firms, pension funds and institutional investors, claim to be employing data analytics strategies for their investments. But many, if not most of these companies have mission statements at best, conflating the fact that any analysis of digital information is considered deep data analytics, which is not the case. At its core, big data is about the digitization of the sheer volume of information, both private and public, that is being produced globally. Many business leaders only understand the end results of this—that is, the visualizations and analysis—when in fact, data analytics is also about the sophisticated management of information.
Furthermore, taking data analytics to the next level is not just about producing more data or faster data—it’s about developing multidimensional information that allows us to examine several different points of view in unison. We need to move from “small data” to “big data” to uncover unknown correlations and market trends in both structured and unstructured data.
In PwC’s Deals practice, we tend to think of data analytics as a digital “toolbox” that allows firms to see investment opportunities from an entirely new perspective. And, on a number of recent transactions, we’ve helped our clients understand that looking at the right data can illuminate the risks and opportunities—from initial strategy development through to acquisition, post-deal and through to exit.
In most M&A deals, investors tend to focus on financial KPIs such as revenues, income and operating costs, but lack the tools or time to really dig into the underlying drivers of a business. What internal and external market factors actually influence these KPIs? What risks are on the horizon? Using information provided by counterparties on a deal, combined with public and proprietary external data sources, organizations can leverage predictive analytics using machine learning to more truly understand a business’ growth potential.
We recently helped a client in the automotive industry backtest historical relationships of an acquisition target's revenue streams with external market factors using statistical modelling. This showed how the company might perform given sensitivities to factors such as currency changes, commodity prices and even environmental risk. To supplement this, we also employed probability testing to understand ROI under different income and operating scenarios. Leveraged properly, data and analytics was able to help us recognize risk to add another layer of objectivity to evaluate the transaction and take due diligence to the next level.
Public market investors have relied on published financial statements for as long as they have existed. But today, in addition to public company disclosures, there is a mountain of publicly available data that can help savvy investors target investment opportunities based on risk, industry-type and holding period. Investors are increasingly bolstering their analysis by analyzing market variables such as trade volumes, price ratios and momentum against other unstructured variables, like social media presence and political volatility.
We recently worked with a major retailer on a national expansion plan by focusing on consumer and locational data. By overlaying a number of disparate but related data variables such as census information, mobile data patterns and pedestrian volumes, we were able to develop a model forecasting potential revenue by geographic location. This was then incorporated into the organization’s long term growth and merchandise mix strategies.
Another good investment strategy that employs data analytics might include demographics: follow what’s going on with the people—what’s happening to wages, consumer spending and population growth. By leveraging large quantities of data, you can better identify patterns to augment human judgement and set smarter growth priorities.
While the previous examples highlight the power of data analytics for more “offensive” pursuits, analytics can also help firms add more sophistication to their internal operations and management. Most enterprise systems today are built around financial reporting and regulatory processes—most of the data remains in raw, unstructured formats. The big data and analytics revolution conversely is pushing firms to dissect, relate and cross-compare information in new ways.
Some fund managers, for example, are using visualization techniques to better understand their portfolio against benchmarks and market competitors. What’s more, software can be trained to get increasingly smarter with these data applications, helping to refine the robustness of models, identify better investment targets and better manage data in real-time.
Canada is a mixed bag. Some businesses and investors have invested in analytics in a big way, building extensive skills and capabilities, while others are just beginning. As the global economy continues to rely on advanced analytics, data analytics will no longer be a value-add, but a necessity. In order to stay ahead of the curve, we need to put pressure on ourselves now to innovate and invest (both financially and culturally) in technology that will allow us to augment traditional investment decision-making processes.
You might look at your investment decisions differently if you looked at them through the lens of big data. If you’re not sure if you’re getting all that you can out of your data—or even looking at the right data to begin with—we’re here to help.
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