Which Is More Mature, Wine Grapes or Your Data?

One of the most overlooked areas in data is enterprise maturity. Many organizations think that data maturity will organically occur during the pursuit of data connection and ingestion. That is a fallacy. Data is an incredibly robust asset and requires love and attention, just like grapes being grown for fine wine.

What is data maturity, and why do credit unions want it?

At its simplest, enterprise data maturity is the extent to which a credit union utilizes the data it produces. The more the credit union uses the data and develops a data competency, the more the credit union increases its maturity. Generally, there are four stages of enterprise data maturity. Let’s think about them as four stages of growing wine grapes.

Stage 1: Nascent/Budding

In this stage, the data is in its earliest form. It is raw, ideally clean and living in silos. The credit union is just beginning to understand the power of connected data, similar to how buds form and grow on grapevines. The credit union recognizes that it has data, but it has no formal data strategy and substantial effort is needed to produce data reports.

Stage 2: Functional/Blooming

Like maturing grapes reacting to their environment, in this stage the credit union moves into a reactive position with its data. Spreadsheets are used as a primary means of reporting. Reporting is limited to tasks that are critical for business operations. There are no formal business intelligence and analytics tools or standard. There is no data governance, creating a low level of confidence in data. Descriptive analytics are employed.

Stage 3: Exploration/Veraison

As the organization begins to mature, it resembles veraison, or the onset of the grapes’ final ripening on the vine. The grapes are getting plump and changing color. In terms of the credit union’s data maturity, credit union talent is increasing its confidence in some capabilities and exploring beyond earlier boundaries to increase their knowledge. The credit union takes a proactive position concerning its data. Data is being used to create what-if scenarios in financial reporting. Standard sets of reports are being produced regularly with ad-hoc capabilities being made available. The credit union is beginning to track key performance indicators. Exploration has already moved into statistical analysis and data management standards are beginning to take shape. BI and analytics are in their early stages of implementation and used to report on activity.

Stage 4: Enterprise Adoption/Harvest (We’ve Got This!)

Like the final phase of wine grape maturity, called harvest, here the credit union has matured its data – data management is being practiced and governed across the organization with effective policies and procedures. Data is pulled in real-time and used to predict outcomes. It is time to prescribe solutions and improve upstream processes.

How is data maturity measured?

Most data maturity is measured by applying a data maturity model. Organizations will implement a data maturity model to benchmark their capabilities, identify strengths and gaps, and leverage their data assets to improve business performance. Data maturity models generally cover the following six categories:

1. Data strategy

2. Data governance

3. Data quality

4. Data operations

5. Data architecture

6. Data culture

What is data governance, and why should a credit union formally “govern” its data?

As the data matures, a formal process is needed to audit and document it.

Data is one of the most robust assets that a credit union owns (yes, it has more potential value than loans do), so it requires a governance process similar to what the credit union currently has in place for its loans. A credit union defines, prioritizes, audits, and creates policies and procedures for its loans, and a very similar process is needed for its data.

The mission of a data governance program is to give data a formal structure. It also encompasses the management of data access, quality and security throughout its life cycle. Data governance is part of organizational data maturity efforts and works best when aligned to the data strategy. It is a discipline unto itself. Data governance is a never-ending loop of defining, prioritizing, auditing, setting policies and procedures, and working to maintain effectiveness and efficiency.

The data governance program is managed by the data governance council, which is accountable for setting strategy and direction. The key roles are:

• Chief data officer (drives the long-term data strategy and road map, and mobilizes program funding);
• Data governance lead;
• Data quality lead;
• Metadata and prioritization lead; and
• Data PMO.

The following are three common pitfalls that occur when creating a data governance program:

Not having a formal program. Many credit unions feel that their data governance program will grow organically. This is not the case. The best starting point is to understand the enterprise data vision and have a clear understanding of the member-centric use cases.

Lack of leadership. Credit unions treat data governance as an IT initiative and do not assign credit union leadership to the effort. A chief data officer, who can be the credit union’s CEO, COO, CMO or CFO, takes the project’s reins to provide a global lens for the data.

Thinking that it’s only data. Many data governance efforts fail because the focus is only on data quality and tool implementation. Data governance is much broader than that. It is an iterative, continuous process that helps the organization achieve its goals.

While a formal data governance and maturity program may not feel like a priority, it very much is, as data governance has already appeared in credit union NCUA audits.

Anne Legg is the Founder of THRIVE Strategic Services, a San Diego, Calif.-based company that assists credit unions with data transformation, and author of “Big Data/Big Climb,” a credit union playbook for data transformation.