For all financial institutions, Data Analysis needs to be a centralized, full-time function. In the face of disruptions such as COVID-19, we should be able to expose, understand, and harness the data to enable and take action.
In this article, we discuss the keys to building a strong analytics Practice. The insights are taken from the presentation by Martin Walker, Vice President- Digital Experience and Innovation, Sound Credit Union, at the 5th Annual CULytics Summit.
To build a strong and efficient data analytics practice, the following players need to be kept in mind-
- The CEO – It starts at the top. If the CEO does not support the Endeavour, analytics will not be driven as a practice within the organization. Data support at the top, to use data as a factor behind decision making is crucial.
- The Advocate – The person who is exposing the data for good. It is crucial to overcome the fear that data will impersonalize the relationship with the member. Data enables a stronger personal relationship with members.
- The Driver- The person who decides with data, and knows how to leverage data to achieve business outcomes. Such a person is important because without action data has no impact.
- The Interpreter – The person who speaks data and business. The interpreter helps to understand the outcome business is looking to achieve and understands how the data team can help.
- The Champions- These are the early adopters, eager, and willing to work with data.
Recipe for Success
- Culture – Build a culture that supports data and enables data analysis as a practice.
- Transparency- To build a culture as described above, it is extremely crucial to be transparent in showcasing data and show how it is being used.
- Desire to be better- You should be able to feel the need to attack the disruption and improve consistently.
- Quick Wins- Irrespective of how small or big the quick wins are, it is important, for encouragement, and as a driving force, to have them under your belt.
- Find Champions- Get people on board who can adapt, learn willingly, and with excitement. They will attract more champions.
- Tell your story – You need to have someone who can tell the story and connect the dots between data and the positive outcomes.
Some philosophical changes-
- Move from not questioning to questioning everything.
- From trying to not fail, move towards failing fast.
- Always believe that data enables relationships.
- Move towards an ideology of, ‘I want everyone to know’.
USE CASE EXAMPLE OF SOUND CREDIT UNION
OBJECTIVE – DATA ENHANCES RELATIONSHIPS
Sound CU had a 2-2-2 program for onboarding members. (Reaching out to members within 2 days, 2 weeks, and 2 months with particular touchpoints.) There was however difficulty in follow-through and administration.
An early adopter branch-manager created a report to alert each employee to complete their next reach out.
The first-year churn for new accounts decreased from 6.06% in 2018 to 4.30% in 2019, a 29% improvement.
OBJECTIVE – DATA HELPS US BE NIMBLE, RESPONSIVE
In late 2018, the government shutdown negatively impacted some members.
The business intelligence team delivered a list of negatively impacted members to business teams to reach out with a 90-day short-term loan to help.
24 loans were issued for a total of $114,200, allowing them to help members in a stressful time.
OBJECTIVE – DATA IMPROVES EFFICIENCY
Transaction totals that were reported by operations did not match reporting totals from other sources.
The business intelligence team exposed data and worked with teams to define data and terminology, resulting in accurate reporting and common understanding.
Branch staff allocation and planning were adjusted based on new reports.
OBJECTIVE – DATA PUTS THE MEMBER FIRST
Several business members were using courtesy pay to bridge an unpredictable gap between payables and receivables.
The business intelligence team provided a list to the lending team, enabling them to extend lines of credit to members, resulting in a lower-cost, more reliable solution.
For 600 business members who used Courtesy Pay, Sound CU was able to offer a solution that reduced the users’ cost by 80%.
Learning and conclusion
Everything we do creates data. Adoption of data analytics is not easy and many encumbrances can occur. Adoption is also critical. Ensure data quality and continue to connect data sources. It is significant to achieve some quick wins and learn from data as you go.