Executives at a European financial services firm had a vision. They would create a data analytics application to collect information about all their customers’ behaviors and preferences in all the markets served. That would enable the company to present just the right offering to just the right customer at just the right time.
Related event: TDWI BI Symposium London 2015, 7-9 September
Taking full advantage of a firm’s information resources requires good data management practices, and that requires corporate leadership dedicated to two principles.
Rising revenues, of course, would follow.
Partway through building the application, however, as project costs rose, the firm decided to focus on just one market and launch the application there. However, it had neglected to establish frameworks for defining and categorizing the data assets it was collecting well enough to understand how the data points related to each other, nor had it documented its work clearly. This meant the data the firm had collected could neither be used nor reused. Not surprisingly, the rollout in the pilot market did not yield the results the firm had envisioned, and revenues did not rise.
This sad story underscores the necessity for proper data management when pursuing the promise of big data analytics. The European financial services firm had to scrap everything and start from scratch. The firm essentially lost four or five years of opportunity to leverage the data collected.
As the firm struggled, its competitors moved ahead.
The Data Management Imperative
There are many companies with lots of internal data and access to external data sources. Combining the two can generate all sorts of valuable questions. How does our business stack up competitively? What customer signals could inform our strategy for the future? What preferences do our customers demonstrate and what is likely to entice them to be loyal and increase their consumption of our products and services?
Taking full advantage of a firm’s information resources requires good data management practices, and that requires corporate leadership dedicated to two principles:
Principle #1: Consistent data management
Corporate leaders must be committed to taking advantage of data resources and the insights they offer. This enables business and information technology (IT) leaders to align their efforts with corporate strategy. It empowers a commitment to a master data management approachthat establishes definitions for data assets and business processes for maintaining these resources in good order as they accumulate over time.
When a company changes strategies, IT leaders, or staff, the organization retains an understanding of the data assets it holds, including:
— How each data asset relates to others
— How each data asset relates to new, incoming sources of data
— How the assembled data assets can deliver business value
Principle #2: Commitment to deploying data analytics as a competitive lever
Leaders need to commit to the idea that implementing analytics is required in order to understand existing customers and connect with new ones. This concept is essential to success and necessary to stay abreast with competitors using data-enabled strategies to enter the financial services industry.
It’s hard to escape the news that technology firms are entering the financial services arena:
— Apple Pay, launched in 2014, allows purchase of goods at the point of sale via a user’s iPhone or iPad.
— Google Compare, a service in the United Kingdom started in 2013, enables consumers to search for car insurance price quotes among 123 providers, promising savings.
— CurrencyFair and FairFX have entered the currency exchange market with data-driven approaches.
— PayPal is running a pilot to allow customers to pay for goods in Bitcoin, adding to PayPal’s services.
Established players should consider these moves as the start of an onslaught from new entrants that are data rich, analytics competent, and masters of data management
Discipline and Humility
It’s not hard to follow these two principals; it just takes discipline and a little humility. For example, instead of going for a big bang by launching in all its markets, the European financial services company could have run pilots in one or two countries. By following master data management principles, the company could have established quality controls for the data it collected and analyzed and then formulated business processes to manage that data.
This would have allowed the firm to demonstrate the worth of these practices with early and relatively small wins. Then the company could have used these processes as prototypes for rollouts in other markets, accumulating business value along the way as the firm moved toward a full, companywide adoption of the system.
This approach would have placed strong data management at its center and made analytics core to the company’s growth strategy just as new market entrants do. It likely also would have saved the firm the years (and money) it wasted.
What Success Looks Like
An insurance company found it was able to reduce risk by analyzing data about accident claims for its transportation clients. It shared its analysis with its clients, and the transportation companies rerouted some of their trucks to avoid roads where more accidents generally occurred. This resulted in the insurance company (which offered incentives for its clients to use the alternate routes) reducing its payouts.
The insurance company leveraged data it possessed: accident claims and insured vehicles. It used external sources to analyze traffic and highway data. It used predictive analytics to model what would happen if vehicles drove different routes to reach their destinations. It learned which routes met business requirements while reducing the probability of accidents and claims.
This happy case highlights the benefits of good data management practices. The insurance firm’s top executives demonstrated their belief in the value of data analytics (Principle #2); they worked with IT colleagues to align the analytics program with business goals (Principle #1). Combining the two led to supporting the investments needed to develop the applications, and because the program was properly implemented, it led to a high return on investment.
Your Three-Point Checklist
High-quality information drives decisions. Executives leading firms need to be able to analyze data.
To assess your capability, ask yourself these questions:
- Are we taking the right steps to put analytics at the center of what we do? In other words, are you treating analytics as an asset with both strategic and tactical applications? Your plans for analytics should have clear business imperatives, and the results from analytics applications should drive business decisions.
- Are we taking the right delivery approach for our analytics? Simply put, too little doesn’t get you anywhere, and too much in a short time frame can lead to failure. Avoid big-bang projects. Prove the worth of analytics with small victories. Build on them to achieve a defined destination. Embrace change management to ensure analytics is adopted in your organization.
- Are we leveraging different business model options to increase speed to market? There are many technology and service providers that can help you procure your analytics outputs on a less-costly, as-needed basis.
A Final Thought
Significant value can be realized from analytics-led initiatives. The factors outlined in this article underpin success and can position your organization to remain competitive and defend it from the inevitable: technology companies entering and disrupting your market.
For the last 15 years, David has been working with clients to achieve high-performance growth and profit management. David advises companies about delivering results, in particular by leveraging their data and applying decision science to their problem solving. He helps clients embrace analytics and digital platforms across their value chain, especially their sales and marketing functions, and is passionate about engagement and execution. David founded Distinct Intelligence in 2005 based on a core belief that: “Companies can derive competitive advantage leveraging decision science and data analytics in their pursuit of improved profits, happier customers and a better cost profile.” You can contact the author at [email protected].
All articles are © 2015 by the authors.
Related event: TDWI BI Symposium London 2015, 7-9 September