These days, data is its own currency. It can help you better understand your members, forecast upcoming trends, and make better business decisions. And those better decisions can translate directly into better profits. According to PwC, companies who fully exploit the potential of data they already have can reduce annual costs by 1/3 and increase incremental revenue by over 30%.
Raw data alone won’t get the job done, though. How can credit unions use the data they gather every day from their members to gain meaningful insights? That’s where data management comes in. The data management framework takes seven interrelated disciplines used by companies to analyze — and maximize — the value of their data.
Read on for a quick debrief on each of these seven disciplines, and access the definitive guide to data management for credit unions for more.
Managing Data through Data Governance
Data governance is the organized management of people, processes, and technology supporting an institution’s data. It reinforces all data touchpoints and allows you to build the foundational data upon which you can base your analytics and insights.
The data governance team is a cross-functional team that provides timely and accurate information to support business decisions. They are responsible for ensuring that each employee within the credit union, or external organizations, has access to the appropriate data to make informed and timely decisions. The processes involved in data governance involve decision making around data and are imperative to be clear and followed by those responsible. Finally, technologies can be data lineage tools, data analytics platforms, or databases.
Overseeing data management, developing the right processes, and managing data appropriately are essential to making data usable for your financial institution.
Organizing Data with Data Architecture
Data architecture explains the context of how a financial institution stores, integrates, and uses data. Often, a diagram illustrates how data flows through financial institution systems. A strong data architecture makes it easier to understand where a report’s data originated from and how it has been used.
A properly designed architecture has a single source of truth (also known as an authoritative data source). The philosophy behind this is that there should only be one place to find the data, instead of having to look in multiple, potentially conflicting locations.
Some of the ways to strengthen your data architecture are by organizing your data properly — eliminating data held individually by departments or applications, and instead storing and sharing data centrally. Eliminating duplicate data also helps ensure you have one single source of truth. And by managing data as it’s received, you’ll establish a strong foundation.
Defining Data with Metadata
Metadata is commonly referred to as “data about data,” and the job of metadata is to describe, organize, or manage other data. There are three important components of metadata: definitions, code values, and data lineage.
As part of managing metadata, data elements or fields get defined. Without these definitions, terms can be understood or used in ways other than intended. Defining terms gets everyone on the same page. Code values can be letters, numbers, words, or alphanumeric strings that are consistently applied to defined data fields and denote specific groupings or meanings. Finally, data lineage details the journey data takes during its lifecycle.
Proper management of metadata reduces the likelihood that a financial institution will become overwhelmed by its data. Tools can help you manage your metadata, and track things like your data lineage.
Ensuring Data Quality
Data quality is the measure of data’s condition for its intended use in operations, decision making, and planning. It’s measured on the dimensions of accuracy, completeness, conformity, consistency, integrity, timeliness, and uniqueness.
The benefits of high-quality data include the ability to provide necessary information to handle services effectively, promote accountability, and prioritize and utilize resources properly. It can also help your financial institution save time, energy, and money.
Because it can impact public-facing materials, data quality can be highly visible to your members — and the stakes are always high when dealing with financial assets and personal information. Small errors can have big consequences, so a commitment to high-quality data helps your financial institution maintain its professionalism and enhances the member experience.
Managing the Data Lifecycle
Data lifecycle is the process of checking data quality throughout the series of stages data takes in its life. There are typically six data lifecycle stages: data capture, data processing, data transmission, data usage, data retention, and data disposal.
Proper data lifecycle management can increase the quality of your data, and helps you to use the data more efficiently. So how can you manage this lifecycle most effectively?
First, maintain documentation about the data lifecycle. This can provide context, and keep everyone on the same page about proper processes. You should also correct problems at their origin. By understanding where data originated in a lifecycle, you can ensure it is correct from that point on. Finally, promote data lifecycle education via onboarding and training that is consistent across departments.
Maintaining Data Privacy
Data privacy is the protection and continual confidentiality of data that is collected, shared, and used. For financial institutions, this includes personal information like a member name, personal ID, contact information, personal characteristics (like photos or fingerprints), and other personal information like date of birth or employment information. Particularly for financial institutions, cybersecurity is a top concern, and data privacy relates directly to that.
Privacy laws have been enacted in many states and countries, so keeping up with data privacy is not only the right thing to do for your members, but is also a legal requirement. By ensuring your financial institution’s compliance with these laws, creating a data privacy plan, understanding how to safely dispose of data, and creating a culture of privacy at your financial institution, you can keep your members’ personal information private and secure.
Gaining Insights Through Analytics
Analytics is a discipline that is focused on helping organizations make better business decisions; it takes data that has already been prepared and puts it into action. It involves applying business knowledge to data in order to generate insights that will go on to influence decision-making.
There are five different analytics categories, descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics. Respectively, these tell you what happened previously, why something happened previously, what may happen in the future, what to do in the future, and how to achieve future outcomes.
Analytics platforms can also be used to gain a better understanding of trends and opportunities relating to members, branches, operations, dealers, and more. Still, analytics are only as strong as the data they rely on, which is why every aspect of the data management framework is essential to successful insights and outcomes.
Your financial institution holds the data keys to understand members, operate efficiently, and stay competitive. By implementing a data management framework, you will set your financial institution up for success.
If you want a more detailed guide on how to adopt the data management framework, click this link to access Trellance’s white paper, The Definitive Guide to Data Management for Credit Unions.