Data is Eating the World

Data and analytic technologies are shaking up the world and the effects are poised to reshuffle the landscape in multiple industries. In a lecture at the Google user’s meeting in 2006, Steve Jobs, Founder and CEO of Apple, used this phrase to emphasize the tectonic change in consumer’s habits:“if your user is not the one paying, then he is not the customer but rather the product being sold”. These words reflecting on the huge value of data, seem more relevant now than ever before: users’ data is becoming the core value being sold.

(Part of this writing is Sourced from Mckinsey & Co. Dec. 2016)

Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. The convergence of these trends is fueling rapid technology advances and business disruptions.

Data is now a critical corporate asset. It comes from the web, billions of phones, sensors, payment systems, cameras, and a huge array of other sources—and its value is tied to its ultimate use. While data itself will become increasingly commoditized, value is likely to accrue to the owners of scarce data, to players that aggregate data in unique ways, and especially to providers of valuable analytics.


Recent advances in machine learning can be used to solve a tremendous variety of problems—and deep learning is pushing the boundaries even further. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories. The value potential is everywhere, even in industries that have been slow to digitize. These technologies could generate productivity gains and an improved quality of life—along with job losses and other disruptions.


Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass. An even bigger wave of change is looming on the horizon as deep learning reaches maturity, giving machines unprecedented capabilities to think, problem-solve, and understand language. Organizations that areable to harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.

Analytics leaders are changing the nature of competition and consolidating big advantages. There are now major disparities in performance between a small group of technology leaders and the average company—in some cases creating winner-take-most dynamics. Leaders such as Apple, Alphabet/Google, Amazon, Facebook, Microsoft, GE, and Alibaba Group have established themselves as some of the most valuable companies in the world. The same trend can be seen among privately held companies. The leading global “unicorns” tend to be companies with business models predicated on data and analytics, such as:Uber, Lyft, Didi Chuxing, Palantir, Flipkart, Airbnb, DJI, Snapchat, Pinterest, BlaBlaCar, and Spotify. These companies differentiate themselves through their data and analytics assets, processes, and strategies.

The relative value of various assets has shifted. Where previous titans of industry poured billions into factories and equipment, the new leaders invest heavily in digital platforms, data, and analytical talent. New digital native players can circumvent traditional barriers to entry, such as the need to build traditional fixed assets, which enables them to enter markets with surprising speed. Amazon challenged the rest of the retail sector without building stores (though it does have a highly digitized physical distribution network), “fintechs” are providing financial services without physical bank branches, Netflix is changing the media landscape without connecting cables to customers’ homes, and Airbnb has introduced a radical new model in the hospitality sector without building hotels.

According to Forbes magazine May2016, the Banking, Financial services & Insurance sectors accounted for 25% of the Data driven transformation.

The value of data and analytics has upended the traditional relationship between consumers and producers. In the past, companies sold products to their customers in return for money and negligible data. Today, transactions—and indeed every interaction with a consumer— produce valuable information. Sometimes the data itself is so valuable that companies such as Facebook, LinkedIn, Pinterest, Twitter, and many others are willing to offer free services in order to obtain it. In some cases, the “customer” is actually a user who barters his or her data in exchange for free use of the product or service. fintechIn others, the actual customers may be marketers that pay for targeted advertising based on user-generated data. To maintain an edge in consumer data, user acquisition and user interaction are both critical. Venture capitalists have long understood the importance of building a customer base. Many internet startups from Quora to Jet have focused as much attention on capturing users who can provide valuable data as on capturing paying customers.


The data driven technologies are allowing new players to challenge incumbents with surprising speed since they circumvent the need to build traditional fixed assets. Amazon, Netflix, Uber, Airbnb, and a host of new “fintech” financial firms have moved into industries where incumbents were heavily invested in certain types of physical assets. These disrupters used their digital, data, and analytics assets to create value without owning physical shops, cable connections to viewers’ homes, car fleets, hotels, or bank branches, respectively.

Retail banking is ripe for massive data integration to change the nature of competition

Retail banking has always been data-rich, with stores of information on customers’ transactions, financial status, and demographics. But few institutions have fully taken advantage of this data due to internal barriers that limit access across the organization, the variable quality of the data, and sometimes even an inability to see the value that data could generate.Surmounting these barriers is becoming critical now that a plethora of new data sources can be added to existing transaction records. These include social media posts, call center discussions, video footage from branches, and data acquired from external sources and partners. In addition, retail banks can partner with mobile phone operators and retailers to complement their view of each customer.Adding analytics on top of all the data can enhance the customer experience, enabling banks to retain existing customers and attract new ones. Customers increasingly expect a personalized experience across all channels.

The banks also want banking services to be available on their preferred platforms; a growing number are making payments directly via messaging apps, for instance. Integration tools such as data lakes help to provide a holistic view of each customer by combining different sociodemographic and behavioral data. One of the important applications for banks is the ability to improve risk and credit assessments by drawing on data from multiple sources for more accurate predictions.

Massive data integration repositories and the analytics they enable can also optimize banking operations and increase revenue (See Exhibit below). This is critical in an era when low interest rates are putting pressure on margins, regulatory and reporting requirements are growing more complex, and new digital native startups are introducing innovative business models into financial services.

Retail banks have opportunities to break their data silos, combining traditional and new data sources in data lakes


Widespread adoption of analytics on top of massive data integration architecture has significant potential to create economic impact within the retail banking industry. Among the main mechanisms will be improved cross-selling and the development of personalized products. Analytics could have an especially large impact in reducing risk via better credit underwriting, credit monitoring, and improved collections. Retail banking is still undergoing digitizing many operation, and as this trend combines with a deeper use of data and analytics, new efficiencies can be realized in areas such as automating business support, targeting marketing more effectively, and improving customer engagement. The process of digitization itself will enable banks to generate more customer data while simultaneously taking advantage of lower-cost digital channels. Together these shifts could generate impact of some $400 billion to $600  billion annually, with roughly two-thirds of the impact realized in developed economies and the remainder in developing economies.

As the retail banking industry becomes more data-driven, it is likely to take on an entirely new look. Talent capable of crunching the numbers, developing the algorithms, and combining those skills with an understanding of financial services and the regulatory landscape will be critical. As more banking services become digital, physical branches could be phased out, leading to substantial cost savings.

Three main types of ecosystem players could emerge.


First are the solution and analytics innovators. This category includes many of the fintech players. These digital natives have deep capabilities, and they tend to focus on a particular market niche, with no intention of expanding into full retail banking offerings. This frees them from the constraints of being fully regulated banks. Betterment, for example, offers wealth management by robo-advisers, while ZestFinance combines vast amounts of data and machine learning to improve credit scoring.


Second are incumbent institutions that are the natural owners of significant amounts of data describing the financial position and behavior of their customers. This proprietary data gives them a significant advantage, but they could lose it if they lose the source of the data (that is, their customers) or if other players come up with ways to create equivalent or superior information by integrating from different sources.


Third, companies in other sectors can become part of the banking ecosystem if they bring in orthogonal data—such as non-financial data that provides a more comprehensive and granular view of the customer. These players may have large customer bases and advanced analytics capabilities created for their core businesses, and they can use these advantages to make rapid moves across sector boundaries, adding financial services to their business lines. Alibaba’s creation of Alipay and Apple’s unveiling of Apple Pay are prime examples of this trend.

The move toward massive data integration and analytics may create room for new players and partnerships. Acquisitions and collaborations may be necessary for traditional retail banks to acquire the talent and capabilities they need to stay relevant. Santander UK, for example, has launched a partnership with Kabbage, a small business loan provider that uses machine learning to automate credit decisions. Data-sharing partnerships between banks and telecom companies have helped both sides develop new insights (especially around fraud, customer creditworthiness, and microsegments of customers) that otherwise would not have been possible.Analytics-driven business models are emerging, such as Mint, an aggregator that utilizes data to create personal finance recommendations. Another new model involves peer-to-peer platforms. SoFi, for example, uses robust risk scoring through creative data sources and analytics to connect borrowers and investors directly; the company reports that it already has more than $6 billion in funded loans.