Guest Post by the Observatory of P101
The transformational potential of big data is huge. The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, virtual-reality applications, and billions of mobile phones. Data-storage capacity has increased, while its cost has plummeted. In Italy alone, the market for big data reached a value of 183 million Euros in 2016, that is a 44% growth over the previous year.
Data scientists now have unprecedented computing power at their disposal, and they are devising algorithms that are ever more sophisticated. The areas where we expect the greatest impact are geolocation-based services, healthcare and retail banking. Indeed, by 2030 mobility services, such as ride sharing and car sharing, could account for more than 15% to 20% of total passenger vehicle miles globally. Personalized medicine could reduce health-care costs while allowing people to enjoy longer, healthier, and more productive lives. The total impact could range from $2 trillion to $10 trillion in the US alone. In the retail banking industry, experts estimate a potential economic impact of $110 billion to $170 billion in developed markets.
But that is not all. As highlighted by a new report from the McKinsey Global Institute The age of analytics: Competing in a data-driven world, in the last 6 years the range of applications and opportunities has grown and will continue to expand. Given rapid technological advances, the question for companies now is how to integrate new capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries.
It is no coincidence that the leading global “unicorns” tend to be companies with business models predicated on data and analytics, such as Uber, Airbnb, Snapchat, BlaBlaCar, and Spotify. These companies differentiate themselves through their data and analytics assets, processes, and strategies. However, leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models. The leading firms have remarkably deep analytical talent taking on various problems—and they are actively looking for ways to enter other industries. These companies can take advantage of their scale and data insights to add new business lines, and those expansions are increasingly blurring traditional sector boundaries: for instance, Apple and Alibaba have introduced financial products and services, while Google is developing autonomous cars.
Adapting to an era of data-driven decision making is not always a simple proposition. The first challenge is incorporating data and analytics into a core strategic vision (we have discussed this here). The next step is developing the right business processes and building capabilities, including both data infrastructure and talent. It is not enough simply to layer powerful technology systems on top of existing business operations. A total rethinking of business models and strategies is needed, one that comes from management and permeates all of the company’s actions and operations.
Also, the “war” to attract the best talents is not to be underestimated – as the experts at P101 underline. Across the board, companies report that finding the right professional is the biggest hurdle they face in trying to integrate data and analytics into their existing operations. The McKinsey survey highlights that approximately half of executives across geographies and industries reported greater difficulty recruiting analytical talent than filling any other kind of role. 40% say retention is also an issue. Data scientists, in particular, are in high demand. Even though US universities are adding data and analytics programs and the number of graduates could increase by a robust 7 percent per year, demand is likely to grow by 12% annually, which would lead to a shortfall of some 250,000 data scientists. In Italy, too, the hunt for data scientists has begun: in 2016, 30% of Italian companies recruited a data scientists and 7% codified the role in an official job title (4% in 2015). However, knowing how to read data is not enough: another equally vital role is that of the business translator who serves as the link between analytical talent and practical applications to business questions. McKinsey estimates there could be demand for approximately 2 million to 4 million business translators in the United States alone over the next decade.
These numbers speak for themselves: access to big data and their intelligent use is already revolutionizing the way we do business. So much so that some companies, when confronted with the complexity of analysing huge blocks of information, have decided to acquire big data start-ups. Just think of Microsoft, that in 2015 acquired the analytics start-up Metanautix, or enterprise software provider SAP, which bought Altiscale, a start-up for cloud-based storage of big data. In short, if you think that your company cannot independently manage the data, this could be the right way to go. Because, if used well, the inexhaustible information contained in big data will help companies to no longer go on gut instinct: they can use data and analytics to make faster decisions and more accurate forecasts supported by a mountain of evidence. It’s time for truth.
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This article has been written by the Observatory of P101.
P101 is a venture capital firm focused on investing in digital and technology driven companies. Founded in 2013 by Andrea Di Camillo, the firm is managing a €65m fund, which has 25 companies in portfolio including including ContactLab, Cortilia, Tannico, Musement and MusixMatch. The vehicle, which partners with Italian private accelerators such as HFarm, Nana Bianca, Boox and Club Italia Investimenti to source investment opportunities, is backed by Azimut, Fondo Italiano di Investimento and European Investment Fund.