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Getting Started with your Data Analytics Journey

For the organizations who seek to become data-driven and take the right initial steps- this panel discussion of 2017 Credit Union Analytics Summits moderated by Brewster Knowlton answers the following questions -

  1. How to get started with the Data Journey?
  2. What needs to be done to gain momentum within the organization for analytics?
  3. How to identify quick wins in Data Analytics?
  4. What can be done to overcome the resistance of teams and cultural transformations?

The speakers in the panel include

  • Ben Morales, CEO at Q-Cash Financial and CTO at Washington State Employees’ Credit Union
  • Naveen Jain, President & Founder, CULytics.
  • Matt Duke, Senior Director, GTM Strategy & Operations - Cloud Infrastructure, Oracle
  • Harsh Tiwari, Managing Partner at TVG Group LLC
  • Clay Yearsley, SVP Data Analytics at Texas Trust Credit Union

Getting Started with Analytics.

The question that should be asked before initiating the session, as proposed by the moderator - How many analysts are actually doing analytics and not just moderating data?

Raw data in on its own does not hold any value unless it is processed into information. A data warehouse is worth it only when it can be used to gain useful insights. The key is to make the data warehouse worth it and being realistic about your goals.

For most data warehouses, implementation of technology is perhaps the easiest solution. If we look into the TOGS reference ( Technology, operations, governance, sponsorship) the latter are the ones requiring more effort.

According to Ben Morales, his data analytics journey started because of digital transformation, when he realized that because the digital transformation of consumers is here to stay and in the midst of it, there is a need to create new revenue streams, margins etc. Data was the way to adapt to this change. The CEO created the burning platform which pulled the organization together and the teams rallied around that.

Naveen Jain defines his journey in three phases

  1. Cost and operation efficiency.  Emphasis not only on how efficiently you can generate a report but whether you can use the data provided to drive the business?
  2. Deciding what to do with the insights? Taking action on data. Adopting methods like deploying market automation.
  3. Building data warehouse and maturing data governance process.

He mentions that incapability to use data is the loss of an opportunity to get close to members. The two necessary elements for a successful data analytics journey are executive commitment and a deep passion and understanding in the team.

The driving question for Matt Duke and his team in his data analytics journey was- How do we enable better decision making in the organization in order to become a trusted source of data?

He emphasized on designing a journey around a) Effortless Customer experience b) Data-driven culture and decision making ( facilitating rapid experimentation and value of learning, empowering people to come up with their own hypothesis and then testing that hypothesis and then taking back lessons from it) .

His journey started with:

  • Pricing initiative - How do we optimize our pricing and extract more value and at the same time,  look for opportunities to simplify the portfolio.
  • Hiring people -  from credit analysis and investment banking sector.

Key action steps for the same included -

  1. Finding a business problem to solve.
  2. True data within the organization. ( Trusted independent of view)
  3. Business insights across the organization.
  4. How to get into the leading indicators of business, for example - user experience, and creating data around it.

Harsh Tiwari discussed de-averaging the metrics, taking one data insight and measuring its influence and reaching that 10%  of relevant data. The context of why should be clear to as far as the last mile.

“Understanding where the problem is and what pieces of data can help me solve that Where might they exist and how do I get to it most efficiently. These questions if worked upon can cut down your cycle time massively.”

If we see that customer satisfaction is high. We should not stop there. Some questions that should be asked and answered are - Is the data same for every customer? Is it transferring into profitability? Are the most satisfied customers also the most profitable customers? Are we not correlating different pieces of information? This concept is called deaveraging the metrics and helps to work on the good and worse parameters separately.

Some tips that Clay Yearsley leaves us with include -

  1. Commit, to big to be someone’s part-time job.
  2. Data gap analysis- figuring where the gaps are, prioritizing them and addressing them.
  3. Start with a question.
  4. Align with strategy.
  5. Set a goal to impact the entire organization.
  6. Start using the data you already have. ( ACH data is a gold mine.)
  7. Look for talent within the union to be your ad hoc team.
  8. You are not alone. Leverage the credit union network.
  9. Start challenging conventional wisdom.
  10. Data analytics does not always need massive budgets.

  Creating the momentum, identifying quick wins and addressing cultural transformations.

How do we build the momentum for data analytics within our organizations and overcome skeptical executives or peers?  Some ideas and practices which are discussed in the video are as follows -

  1. Process improvements - Data analytics and the process of back office and create efficiency.
  2. Monetization - Example - As Ben mentioned, short-term credit lending platform which was commercialized and continued to build on member analytics and generate revenue. It acts as a self- motivating factor.
  3. Sales funnel - The stickiness factor. Using it to show leads inflow, cross-selling, up-selling across multiple portfolios.
  4. Focusing on the foundation for a long-term flywheel and overcoming concerns of data quality and governance.
  5. Adopting automation, eliminating manual work and reducing operating costs.
  6. Focusing on the future rather than criticizing the past.
  7. Sending out reports to the entire organization rather than sticking to the executive staff.
  8. The simple analysis of membership and reporting information that is connected to the portfolios.
  9. Quick wins to build momentum.
  10. Start making long-term investments.

Matt Duke suggested, showcasing quick wins implies identifying key challenges across the business which can be influenced and their value can be shown. Good should be targeted each quarter.

Quick wins are not always small dollars.  Realization of the benefit of investment in data analytics is increasingly lacking, and it is essential to realize the same.

It is more important to be actionable than being predictable.  And for this to happen, it is essential to take insights from quick wins.

Some questions asked by the audience and addressed by the panel include

  1.  What are the key attributes that you use to determine that a member is going to leave the credit union?
    • Listing members that have savings in just one product. Identifying the nearest maturity on the product, and that is the member who is going to leave.
    • Deposits activity goes on decreasing.
  2.  How to overcome data quality issues, address and manage them?
    • Continuous improvement. As data evolves, it gets better over time. Help customers understand what problem they are trying to solve.
    • Scoping the problem.
    • Data quality often gets confused with data governance. People talk about the same thing but measure it differently. So this difference needs to be identified.
    • Never make the same mistake twice.  Find your errors first.
  3. How to prepare for a cashless society?
    • Being specific in language.
    • Scaling the data. Buying data from third-party and mixing it with internal data.
    • Personalization - have to grow beyond data proprietary sets.
    • Save all the data.
    • The quicker we can understand iterate the loop of understanding the changes, testing hypothesis and learning from it, the more we can iterate our learning cycle.

The panel addresses the expectations and challenges while initiating and during a successful data analytics journey. The amazing individuals elaborate their views while demonstrating through their personal experiences which adds value to the discussion.

WATCH FULL PANEL DISCUSSION HERE

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