6 disruptive models that can create the data-centric enterprise
In the digital age, companies are striving for radical reinvention in order to create new, significant and sustainable sources of revenue. Imperfect market conditions such as inefficient matching, information asymmetries or human biases and errors open the door to disruption.
Data is the secret weapon to change the dynamics of competition and spur digital growth. Digital-savvy organizations are conquering markets at a rapid pace by employing data-centric strategies to outpace the incumbents.
For best-in-class organizations, data has meanwhile become a critical corporate asset—similar to land, labor or capital—not just to improve their operations but to launch entirely new business models.
The advent of artificial intelligence, data analytics and machine learning enable organizations to solve an unprecedented array of business problems—and the emergence of technology is continuously pushing the boundaries even further.
To jumpstart from center span to front line, McKinsey has identified the following six distinctively different patterns of how organizations can apply data-centric models to turn strategic insights into a competitive advantage, as published in their "The Age of Analytics Report":
Leveraging orthogonal data can be a game-changer
Across most verticals, incumbents are used to relying on a standardized set of certain data. Bringing new data all of a sudden to the table to enrich the data already employed can change the dynamics of competition. New entrants utilizing privileged access to these “orthogonal” data sets can cause a disruption in their respective field of business. Rather than replacing existing data silos, orthogonal data typically complements the data in use to enable correlation as well as taping into new territory to gain additional insights.
Matching supply and demand in real-time through digital platforms
Digital platforms are matchmakers that connect sellers and buyers for products or services. They typically provide a transaction-based framework and act as an intermediate to facilitate the sales process. Thanks to data and analytics, platform operators can now do this in real-time and on an unparalleled order of magnitude in markets where matching supply and demand has been inefficient.
Personal transportation is one example where platforms such as Uber, Lyft and Chinese ride-sharing giant Didi Chuxing have expanded rapidly by allowing third parties to put their underutilized assets to work, rather than owning large fleets themselves. By 2030, shared mobility services could account for more than 15 to 20 percent of total passenger vehicle miles globally. This growth—and the resulting disruption to the taxi industry—may be only a hint of what is to come.
Data and analytics allow “radical personalization”
Data and analytics can discover more granular levels of distinctions, with micro-segmenting a population based on the characteristics of individuals being a powerful use case. Using the resulting insights to radically personalize products and services is changing the fundamentals of competition across many industries, including advertising, education, travel and leisure, media and retail.
This capability could also heavily affect the way health care is provided when incorporating the behavioral, genetic, and molecular data connected with many individual patients. The advent of proteomics, the declining costs of genome sequencing and the growth of real-time monitoring technologies allow generating this kind of new, ultra-fine data. Experts estimate the economic impact could range from $2 trillion to $10 trillion.
Massive data integration capabilities can break down organizational silos
Combining and integrating large-scale data sets from a variety of sources, and breaking silos within an organization to correlate data, has enormous potential to gain insights. However, many organizations are struggling with creating the right structure for that synthesis to take place.
Retail banking, for instance, is an industry possessing lots of data on customers’ transactions, financial status and demographics. Massive data integration could enable better cross-selling, the development of personalized products, yield management, better risk assessment and more effective marketing campaigns—and ultimately help the institutions become more competitive. In fact, McKinsey estimates the total impact of $110 billion to $170 billion in the retail banking industry in developed markets and approximately $60 billion to $90 billion in emerging markets.
Data and analytics can fuel discovery and innovation
Innovation can serve as a booster to differentiate and leapfrog competition. Throughout human kind, people have exploring new ideas in an effort to strive for progression. However, with the emergence of artificial intelligence, data mining and machine learning human ingenuity is now being supported, enhanced or even replaced in some instances.
For example, data and analytics are helping organizations determine how to set up teams, resources and workflows to optimize their outcome. High-performing teams can be many times more productive than low-performing teams. Understanding this variance and how to accomplish more effective collaboration presents a huge opportunity for organizations. Data and analytics can also test hypotheses and find new patterns that may not have even been recognized otherwise. In product innovation, data and analytics can transform research and development in areas such as materials science, synthetic biology and life sciences.
Algorithms can support and enhance human decision-making
Human decision-making is often muddy, biased and limited. Analytics can help overcome this by taking far more data points into account across multiple sources, breaking down information asymmetries, and adding automated algorithms to make the process instantaneous.
As the sources of data grow in complexity and diversity, there are many ways to use the resulting insights to make decisions faster, more accurate, more consistent, and more transparent. Besides medical decision support systems to preclude human error when it comes to treatments, smart cities are one of the other prevailing settings for applying the ability of machines and algorithms to scrutinize huge data sets in a blink of an eye. Utilizing sensors to smoothly route traffic flows and IoT-enabled utilities to reduce waste and keep infrastructure systems working at top efficiency are just two of the many smart city scenarios.