Digital innovation is sweeping across the financial services industry and creating opportunities for banks and credit unions to leverage data as a source of competitive advantage.

Until recently, most credit unions were delegating data management and analytics to the IT department, which in turn created data silos that inhibited the enterprise use of data.

Has your credit union made the business case for creating an analytics team to spearhead important data initiatives? If so, you now need to hire or train the right talent that can turn data into value and deliver on your organization’s data strategy

Chances are you have numerous questions whirling around about how to define the key data roles and responsibilities.  When venturing outside the credit union to evaluate data leadership, this list of tips breaks down key roles and how they should align with your needs.

Chief Data Officer

The CDO is a senior executive who bears responsibility for the credit union’s enterprise data and analytics strategy, data governance, data management, and data utilization.  The CDO’s role will combine accountability and responsibility for information protection and privacy, information governance, data quality and data life cycle management, along with using member data to create business value.

This last point is arguably the most crucial.  If your analytics team is not delivering business value then you’re not achieving the team’s full potential.  The CDO should focus on measurable outcomes for specific use cases to provide the necessary cultural and change management sparks to garner enterprise-wide buy-in.

Data Scientist

A data scientist masters a whole range of skills and tasks from being able to handle the raw data and analyzing that data with the help of statistical techniques, to delivering actionable recommendations based on the underlying data.

The title “Data Scientist” has become a bit of a buzzword as of late.  If your “Data Scientist” can query a database, but the extent of the statistical knowledge is mean, median, and mode…they aren’t a data scientist.

Real” data scientists have deep knowledge of statistical and probabilistic models and know how to leverage those models for specific analytic applications.

The Data Analyst

The data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions.  The insights that data analysts bring to the credit union can be valuable in identifying and even helping to predict the needs of the credit union’s members.  They help develop effective ways to collect the data and compile key findings into reports to share with other teams within the credit union.

Think of the data analyst as the individual who translates between the technical world and the business world.  This individual needs to have basic competencies from a technical perspective, but, most importantly, they need to be able to interpret technical knowledge into practical business terms and vice-a-versa.

A good data analyst doesn’t just produce charts, graphs, and other fancy visualizations.  They produce clearly articulated meaning to describe what the visualizations mean to the business.

ETL Developer/Data Engineer

The ETL Developer/Data Engineer is a critical member of the data analytics team as they are dedicated to the fundamental process of capturing, storing and processing your data.  If your CU leverages a data warehouse as your analytics platform, then the “ETL Developer” most aptly describes the job title.  If your organization is leveraging a data lake or hybrid platform, “Data Engineer” is a more appropriate title.

In the end, this role boils down to ingesting new data sources into the platform.  This may come from non-core third-party applications (i.e. consumer LOS, real estate LOS, online banking, etc.) to external data sources (i.e. demographic data, economic indicators, social media interactions, etc.).

Report/Visualization Developer

To effectively deploy self-service reporting and analytics through your BI portal (i.e. Tableau, Power BI, Information Builders, etc.), someone must be tasked with creating these reports and dashboards. This is the critical role of the Report/Visualization Developer.

If your credit union embraces a more decentralized approach to data analytics, then these resources may reside in the business areas instead of centrally managed.  Regardless of where they reside within the organization, this is an essential function for providing a front-end to your analytics platform.

The Right Role for Your Credit Union?

As credit unions grow and look to remain competitive, there’s an obvious need to hire the right data talent who are highly skilled in analytics, who can interpret data, and insight and tangible business value. Demand for data expertise is growing every day. Be sure to understand which roles are specifically needed by your organization.  Most credit unions don’t have the necessary budget to hire each of the resources discussed.  Determine where the greatest internal need exists and identify strategic partners who can assist with the rest of the functions.

The bottom-line, all organizations have the power to become data-driven by accessing data skills – and on almost any budget.  Ready to formulate a winning data analytics strategy?  Contact The Knowlton Group to get started.

Sources:

  1. Gartner Chief Data Officer Survey

 

If you have reviewed the some of the important steps and hurdles to overcome for credit unions to improve their analytics maturity outlined in our first article on Why the Lag, then you are ready for some more steps in the process.

Challenge:

 Maintaining data quality is a hurdle for many credit unions, but it is a critical component to becoming data-driven. To achieve consistent and reliable member data, credit unions must constantly manage data quality at the source so that they can trust and use the data to enable quicker and more knowledgeable decision-making.  As the saying goes, “garbage in, garbage out” so the importance of clean data can’t be understated.

Solution:

Step one is to know what data you’re collecting, why you’re collecting it and where it comes from.  Make sure that every component is coming from a trusted and knowledgeable source. Validate data as it is entered by automatically flagging missing, incorrect, and/or inconsistent information. Whenever possible, eliminate the opportunity for free-text fields and opt for drop-downs instead.

If you discover problems with incoming data, go all the way back to the original source to make corrections.  The data warehouse or analytics platform is not the place to make those data quality corrections.  Otherwise, you will constantly be correcting for inaccuracies.  Use the data warehouse to identify issues, and then make the corrections at the source.

Challenge:

Lack of leadership buy-in is another challenge we see for those credit unions failing to successfully implement a data strategy. For any new initiative to work well, all departments within they credit union need to communicate, work together and see the payoff of becoming data-driven.  Buy-in will require fortitude and integration into the strategic plan, culture and budget.  This is where analytics becomes as much of a change management problem as it is a technical one.

 Solution:

 To gain support and financial approval for your data analytics initiatives, you need to give senior managers a snapshot of how these efforts can pay-off.  Be sure to provide the “why” the credit union should invest in data analytics and the multitude of ways the data will improve efficiency, member engagement, marketing effectiveness and more. Be transparent and encourage team members to want to be a part of this transformation with concrete examples of how it will improve the “whys” for your credit union (time savings, member service increase, cost reduction, etc.)  Show evidence and examples of how the competition is using data to grow and increase market share.

 Challenge:

 A lack of analytics talent is a major obstacle faced by credit unions desiring to be data-driven. Hiring, training and managing highly skilled, knowledgeable, data-savvy personnel is costly. Given the explosive growth on the job posting sites for those with analytics expertise and the intensifying competition to fill more jobs than there are qualified people, it is difficult to attract and retain the right talent.

Solution:

An effective data analytics talent effort should consider not just compensation but also cultural fit. Striking this balance is critical to set both the data scientist/ data analytics hire and the credit union up for long-term success. Also, consider if anyone in house has the foundational skills necessary to build upon.  Your “Excel gurus” could very well be trained to become your organization’s modern analytics expert.

Millennials, particularly, find it appealing to work with organizations with a strong social and community conscience.  Credit unions inherent operating model – from their community focus to their charitable presence – are well-positioned to offer job applicants the right cultural fit.

Still not sure if your internal team has the right skills?  Consider working with outsourced firms that can augment your internal data efforts.

Is your credit union making the most of member data? If not, what is holding you back?  At The Knowlton Group, we believe that every organization – no matter their size – can become data-driven. The best data and analytics program starts with a great strategy and clearly defined roadmap and implementation plan. Our personalized approach to each engagement ensures that the specific needs and goals of your credit union are captured for maximum results. Want to know how you can further improve your members’ experiences? Let’s talk. Contact me today at brewster@knowlton-group.com or call 860-593-7842.