Guest blogger David Cordero shares five golden rules that banks should follow to become a data-driven enterprise.

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Data has become the latest talk of the town with its potential to influence the decision-making framework in any firm. No matter which industry you are in, the new winners are the ones who can innovate and create value using a data-driven approach. Tech firms such as Google, Apple, Facebook and Amazon (GAFA) were early adopters, propelling their valuations to be 10x in comparison to banks. However, new banking challengers are quickly following in their footsteps. For example, Monzo is famous for finding ways to save customers money by looking at spending patterns. This strategy has helped them gain customers through word-of-mouth referrals while at the same time cross-selling financial products.

With the right foresight, banks can become the core of data-centric systems and be the major driver for cultural change.

A data-driven approach has brought a revolution and given rise to new trends, including:

• Becoming a silent player
  Rather than shouting from the rooftops, the trend is to use customer data insights to redesign systems and business models. Some cross-industry examples include:
  • Health and public service: Health website Iodine was founded to help improve health care by personalizing it for each individual. The company analyzes large health care datasets and puts that data together with information on an individual’s medical situation and background to provide individualized health guidance.
  • Oil and gas: Shell built an analytics platform based on software from several vendors to run predictive models to anticipate when more than 3,000 different oil drilling machine parts might fail.
  • Retail and consumer goods: Domino’s Pizza used Splunk to analyze consumer behavior to build data-driven business strategies and understand their customers’ needs, and cater to them more effectively by using Big Data.

The question for banks is how to collaborate with peers—or even competitors—to solve problems collectively.

• Embracing data minimization
  Data minimization is the concept where organizations strive toward only the data they need. Minimal viable data will be the new trend in product design, backed by algorithms for key business decisions. Data may start to be shared in data exchanges. Wibson, for example, is a blockchain-based, decentralized data marketplace that provides individuals a way to securely and anonymously sell validated private information in a trusted environment.
• The rise of phygital
  Studying the digital behavior of users is a powerful tool to understand what people want and value. The experience of seamlessly moving between digital and physical channels is evolving and paving the path toward a connected ecosystem of services and experiences. Recently, Google and Mastercard have signed a deal to enable Google to track retail sales using Mastercard transaction data. This shows that Google’s collaboration with financial services players is raising the bar for a new, innovative way of working. An understanding of the right data sources can drive new product design decisions.
• The dawn of artificial general intelligence (AGI)
  AGI is where the intelligence of a machine can successfully perform any intellectual task that a human being can. While not a reality yet, there have been some significant breakthroughs in medicine. For instance, Deep Mind (a company acquired by Google) is applying machine learning to radiotherapy planning for head and neck cancer. We are also starting to see robo-advisors emerging in wealth management. This technology can only improve with scale and experience.

What banks can do

We believe banks have a competitive advantage when it comes to data because of the quantity and quality of the data they hold. “Every second there are 6,900 tweets, 30,000 Facebook likes and 60,000 Google searches“ But the CICS application server, which runs on the IBM mainframe, processes 1.1 million transactions per second—that’s 100 billion transactions a day, as mentioned in an interview by IBM Hursley Lab Director Rob Lamb.

The key is to create value by using data to:

• Create smart interactions
  Banks need to understand the customer’s preference to create new experiences through data. This initiative enables banks to maximize customer retention and attract new customers by providing real-time services to drive engagement. For example, the Ally Bank’s “Splurge Alert” mobile app prevents customers from making certain expenditures by detecting through geolocation. This app helps consumers better manage their spending habits and control their personal finances.
• Develop digital-native products
  Banks are undergoing digital transformation to produce digital-friendly products. Digital-native products can be created by modernizing infrastructure and collaborating with third-party providers through open architecture models using cloud. This will eventually help to reduce the cost of products. For instance, Spanish bank BBVA has applied this mindset to create Valora, an end-to-end home-buying experience that reduces the cost for the end consumer and the bank.

The result is that banks can use data to evolve their operating model from a “trusted FS advisor” to one that “orchestrates an ecosystem” through collaboration with partners such as fintech and startup companies to augment their products. It also involves opening application programming interfaces (APIs) to third-party developers to create new services on bank platforms. Eventually, it’s possible that banks may one day evolve to an “ecosystem on GAFA” model where financial products and digital services are sold through GAFA and other retailer platforms directly to customers.

How can you get started?

We believe that in this era of emerging data ecosystems and hyper customer relevance, banks are the “sleeping giants” when it comes to data.

We propose five golden rules to become a data-driven bank:    

1. Think first about your use cases, then the required technology. Build your data-driven model around your business purpose.
2. Create a value realization unit to support your overall strategy. Identify use cases and prioritize a roadmap.
3. Be use-case-driven when building intelligent data platform capabilities, as they are required when using an agile approach. The time for building a completely new data architecture has passed.
4. Ensure the agile approach is aligned with overall strategy. From a technology perspective, the design authority will be required to ensure all architectural building blocks are created consistently with the bank’s future and target data technology vision.
5. Change your organization model around the use cases. Implement the use cases as the driver for internal transformation.

With the right foresight, banks can become the core of data-centric systems and be the major driver for cultural change.