By Roy Urrico
On a daily basis, credit unions compete in a data-driven banking world, processing valuable information. To stay competitive and relevant in today’s world, it is crucial for credit unions to understand data on member behavior, preferences, and patterns. Analytics provider Trellance, which offers data management to credit unions, believes effective data management allows decision-makers to deliver the right information on a timely basis.
Market and consumer data firm Statista expects the volume of data created, captured, copied, and consumed worldwide to reach 97 zettabytes in 2022 (that is 97,000,000,000,000,000,000,000) and 181 zettabytes by 2025. Not all of the information belongs to the financial services industry, but much of it does. Banking as an industry creates data at every level and grows information at the rate of 700% each second, according to Sigma Computing.
Trellance defines areas of improvement to ensure credit unions reach a successful and sustainable data management practice. The Tampa, Fla.-based company provides a customized, roadmap outlining key actions within seven key data management disciplines:
· Data Governance — A collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.
· Data Architecture — The infrastructure for the storage, integration, and use of data throughout the organization.
· Metadata — Provides key information about data attributes.
· Data Quality — Delivers optimal and organizational confidence when using that data.
· Data Lifecycle — Ensures the correct process and flow so that information maintains its integrity throughout the data supply chain.
· Analytics — Transforms data into valuable insights and decision-making capabilities for organizational teams.
· Data Privacy — Supports the organization’s ability to manage data shared internally and with third parties.
Promoting Data Discipline
Merrill Albert, the data services delivery director for Trellance, helps credit unions with data management, quality, and governance. “I'm a data person through and through. It's kind of been my whole career,” Albert said. “I'm responsible for creating and delivering our data management services. That includes delivering all of our data management consulting projects to our clients.”
Albert works with credit unions to define and execute a structured program. “When we talk about data management at Trellance, we say that it's comprised of seven data disciplines, and we can provide services in all of those areas,” explained Albert. “This is more about working with (organizations), asking them questions, finding out what's going on with their data, and developing it that way.”
Trellance suggested one of the most valuable actions credit unions can take is to empower employees with sound ways to use raw data, which in turn leads to better business decisions and promotes future growth. One proven way to accomplish this is to apply analytics and knowledge to gain meaningful insights.
Analytics help credit unions assess trends and improve key performance indicators (KPIs) in a variety of programs and business domains, proposed Trellance, including consumer, mortgage and commercial loan origination; mortgage loan servicing; general ledger; payments; digital banking; and credit risk/collections.
Why Data Governance Matters
Sometimes confused with “information governance” — which provides an organizational outline to improve business results, manage risks and comply with regulations — “data governance,” usually an IT responsibility, is a collection of processes, roles, policies, standards, and metrics that establishes the processes and responsibilities that ensure an organization’s data quality and security.
Albert pointed out, “There's a little bit of a fine line between information governance and data governance, but I think it is close enough that you can use some of the same principles. They're common enough.”
Data governance accounts for all structured and unstructured data as it relates to storage and transfer. Aspects involved include data security, lineage, service levels, management and loss prevention.
“Data governance is really the starting point,” said Albert. “It's not so much analytics, it's who are your decision makers, who are your doers, who in the organization (will) manage your data?”
Albert conceded that sometimes a credit union becomes attracted to a new shiny information concept, such as “big data” or “analytics,” not realizing there is more complexity involved. “If you do not have that foundation right, then you are only going to be so successful with your tools and your people.”
Albert pointed out a significant benefit of data governance is the consistency it brings to participants. A structured and repeatable methodology ensures that roles, processes, metrics, and standard documentation is available. Without data governance, credit union leaders may use incomplete or inaccurate data to make decisions. The lack of data comprehensibility can also lead to costly oversights, inefficiency, and non-compliance.
Said Albert, “It's not about hiring a whole bunch of people, removing people from their jobs and saying, ‘Okay, now you're going to be in this other group.’ You really need to make use of the people that you've got and develop a data culture.”
Before data governance helped clarify data management, many times people thought IT was responsible for the organization’s information stored in a database. “The problem is IT can be very good for the technical stuff, but they don't always know the business rules.” Albert cautioned. “To be fair, sometimes the business has not clearly explained those rules to the technology side either. By bringing people together in a data governance organization, now we can get the perspective of IT, marketing, lending; because not everyone sees things the same way.”
But first data governance needs to be part of the data management process, Albert said. When Trellance performs its data maturity and gap assessment with credit unions, she added, “A lot of times we find that data governance is missing.”
Data Privacy and Quality Starts with Discipline
Data management also depends on other disciplines such as data quality and data privacy.
Data quality is more about supplying the right data within the organization. Trellance does not just report on things like 10% of the addresses do not have zip codes, Albert explained. Trellance instead asks questions like “Are you checking the quality of your data? Are you putting checks in place when you are receiving the data? Are you re regularly monitoring the data? Are you producing metrics on the quality of the data?”
Albert noted what often happens at financial institutions is the assumption its data is good enough. “They're not really checking a lot about the quality of the data. It is really a bit more superficial. So, just trying to get data quality to be more of the discipline is a big thing.”
Data privacy extends beyond the credit union. “It is definitely one of the things that we do talk about because of the way some of the states have written, their policy (on privacy),” said Albert.
As personal data privacy protection has become a priority for individuals, governments at all levels have enacted a variety of privacy rights laws to control how organizations collect, store and process personal information, such as names, addresses, financial records, and credit information.
While there is no single national law, three states (California, Virginia and Colorado) have passed data privacy laws, and every other state has either introduced or advanced to committee an information privacy regulation.
Albert made it clear when it comes to data privacy organizations also have to consider what happens with personal data, especially if the credit union delivers it outside the organization. “And if you are getting data from different vendors, those vendors may have contracts that state you are only allowed to do certain things with the data.”
Data Worries
A concern Trellance comes across is financial institutions comprehending data value either incorrectly by deeming it good enough or not even thinking about it at all. “(Organizations) think all they need to do is buy a tool and put the tool over the data and everything's going to be magically right,” suggested Albert.
Apprehension can also arise from skewed information. “Showing bad or inconsistent data to members is going to impact how the members feel about you. You also don't want to make decisions based on biased data.”
Then there is the question of how long to keep the information. Sometimes people destroy data prematurely. Data does need storage for a period of time, explained Albert. But on the other hand, organizations do have to destroy it at a certain point. “You can keep it beyond its useful life. Lawyers will tell you that's an important thing to think about.”