MIT SMR: what finance execs think about data as asset

Deriving more value from analytics and emerging technologies like artificial intelligence starts with trust, simply because data collected for analytics must be trusted. On behalf of SAS, MIT SMR (Sloan Management Review) conducted a survey of more than 2,400 business leaders and managers to provide insight into organizations’ activities in each or these key areas and identify where recognized best practices are becoming more mainstream and where they may still be exceptional.

It found that respondents who have advanced their analytics practices to incorporate AI-based technologies such as machine learning and natural language processing work in organizations that do the most to foster data quality, safeguard data assets, and develop cultures of data literacy and innovation.

Barclays US

Barclays US appointed an analytics leader for each business unit and installed cross-functional teams made up of analytics and business experts in a data science lab.

Vishal Morde, vice president of data science and advanced analytics at Barclays US, says the company’s data science lab includes business experts who work with analytics experts to set up projects to solve business problems. “Both sides need to come together; both sides need to actually make sure that they understand each other’s perspective,” Morde says. “They understand each of the challenges and come up with a more kind of unified approach, rather than working in silos and saying that, ‘Oh, somebody else needs to do this job for me.’”

While many corporate leaders agree that data is an important asset, those who back up that view with committed organizational resources may be faster to gain advantage from AI and advanced analytics, and at Barclays US, a set of management decisions to treat data as an asset underlies the success of such efforts, according to Morde.

For example, his business unit’s chief data officer reports directly to the CEO. And the bank established a data management council to catalog all data assets, each asset’s owner, and policies for who gets to use the data. This governance structure creates internal understanding about the importance of sound data management and ensures trust in the company’s data resources, Morde says.

The approach is crucial for the consumer bank’s Data Science Center of Excellence, where the focus is on reaching customers with compelling offers while maintaining a business relationship built on trust. Morde’s team has used advanced techniques such as machine learning, deep learning, natural language processing algorithms, topic modeling, and sentiment analysis to examine customer complaints.

After finding patterns in this text-based data, the bank changed some of its policies. “The natural language processing allowed us to uncover those deep, hidden insights. We were able to think about it very comprehensively from the customer’s point of view and actually made some tangible impact on the customer experience,” Morde says. After these changes, complaint rates dipped to their lowest point in four years. And in the 2018 J.D. Power Credit Card Satisfaction Study, Barclays US moved from seventh position to third position.

And by tapping the consumer insight and some of the anecdotal hypotheses that business experts have, and testing them out with advanced data science methods, Barclays US was able to improve prediction power significantly — in some cases by 50%. Wins like that don’t come from just data, methods, or analytics tools, Morde says: “It was that we were able to transfer some of the domain knowledge from the marketing folks into our models. You’re actually incorporating years and years of expert knowledge that people gathered about consumer behavior and consumer needs and wants.”

Sun Life Financial 

Toronto-based Sun Life Financial generally begins with the premise that no data is trustworthy, regardless of source, because all data has quality issues. Still, how data will be used also factors into how trustworthy it needs to be, says Eric Monteiro, senior vice president of client solutions at the global financial services company.

“We do hold different bars for different types of use cases. For example, for general client segmentation or even business cases where we are sizing an opportunity and making a strategic decision, the bar for quality is lower because in general, the errors will even out up and down, and you’re OK in the end,” he says.

However, if data is used to drive individual communications with clients — sending an email based on the assumption of a certain life event, for example — there must be less tolerance for error. “If we go to you and say, ‘Congratulations on retirement,’ and it turns out even though you’re 64, you are not at all thinking about retirement, that’s pretty embarrassing and a pretty bad client experience,” Monteiro points out.

At Sun Life Financial, data management leaders view GDPR (General Data Protection Regulation) as a reinforcement of their existing practices, he added. The financial services company has a chief privacy officer, data breach notification protocols, and a privacy impact assessment — all elements required under GDPR.

The company also has embarked on a “plain and simple language initiative” that in the past year has reviewed 500 standard letters customers receive with an eye toward improving their clarity and minimizing legalese. The prime reason for these activities — maintaining customer confidence — predated the new rules.

Monteiro says Sun Life follows a policy of providing value for whatever data clients share. The company’s digital benefits assistant, called Ella, is an example. Ella uses a set of predictive models to “nudge” clients to take actions that are in their best interest, such as maximizing retirement contributions (if financial data shows the client is not) and using ancillary health care services (like a chiropractor or wellness program) that can improve the person’s quality of life based on, for example, the stress inherent in the person’s job. The firm’s satisfaction scores increase significantly — 27 points in a Net Promoter Score scale — when Ella engages proactively with these clients, he says.

Read the full report

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