High-quality data leads to effective strategic decisions
It’s easy for banking leaders to see huge opportunities for their businesses in the data analytics and artificial intelligence technologies that are flooding the current marketplace.
At the same time, for some, it’s difficult to stomach the reality that they need to better position themselves to take advantage of these technologies. Data analytics platforms can’t produce results without high-quality data — and you can all but forget about AI if your data isn’t mature.
Grant Thornton Principal, Technology Modernization Will Whatton likes to talk about data quality in terms of technical debt, which accrues interest just like an unpaid loan does. And if you don’t work to develop data quality, that interest is going to keep accruing.
“To be a truly data-driven organization, you have to put in a data quality program and start moving the needle there.”
“To be a truly data-driven organization, you have to put in a data quality program and start moving the needle there,” Whatton said.
On a basic level, the AICPA’s Statements on Standards for Attestation Engagements establish a useful set of criteria for suitable data. According to the AICPA standards, data should be:
- Relevant to the subject matter.
- Objective and free from bias.
- Complete, without omitting relevant factors.
Developing data that’s relevant to your needs is an overarching concern. It takes a huge amount of clean data to perform some of the highest-level tasks that banks are using technology to accomplish in the current environment. While banking leaders prepare data for their current uses, they also should be looking one, three and five years down the road to the more advanced tasks that they wish to perform with their data.
Putting processes in place now to capture the data you need later will prevent you from having to play catch-up in the future.
Culture can be a driver
Developing a culture of quality control and quality assurance can help organizations get the most out of their data.
The board’s role in data maturity
In its oversight of risk management and strategic opportunities, the board has a key role to play in data maturity.
“Board members should focus on the value they’re getting out of data maturity efforts, whether the right items are being prioritized and how management is handling this task that’s difficult to accomplish across the entire organization,” said Grant Thornton Principal, Technology Modernization Will Whatton
Grant Thornton Managing Director, Technology Modernization Mike Pilch said that when looking at data from source systems, third parties, or other sources, leaders need to understand:
- Where does the data come from?
- How does the data come together?
- What steps are needed to produce the quality of data needed to produce the desired outcomes?
“There needs to be someone who can rationalize the ultimate goals of the organization, knowing that certain deliverables are anticipated, who is able to understand how that data comes together,” Pilch said.
With the right people in place, a culture can be developed that drives relevance, objectivity, measurability and completeness in data in such a way that it can be used for numerous purposes with a limited amount of transformation or re-engineering. Processes and systems can be created and aligned with the organization’s data needs, and people can be trained to collect and preserve the data in a way that’s useful to everyone.
Policies and standards can transform a disorganized data environment into a cohesive machine that delivers all the inputs the organization needs with minimal effort. Many banks have in-house teams that oversee these processes, but they may need outside help to discover gaps that may form as they look to mature their offerings.
“There are folks that have worked with multiple banks who can provide more expertise and recommendations,” said Grant Thornton Senior Manager, Technology Modernization Supreet Singh. “It’s always good to have a second pair of eyes to review their strategic requirements and how they’re looking to mature their data strategies.”
Use data to save costs and generate revenue
Key steps in data maturity
- Create a management-level data governance committee to establish a charter for data governance. The committee will identify and prioritize domains and establish data owners and enterprise definitions of data.
- Establish a system-agnostic enterprise reporting chart of accounts within the data governance model.
- Centrally define each data element and manage mapping between systems.
- Centralize and certify enterprise hierarchies to be used in reporting.
- Align supplier/customer/item master data across the enterprise.
- Expand data governance across all master data domains, and then beyond all master data.
- Once governance is established, the organization can take the next step to reap the benefits of improved reporting, big data analytics and ultimately machine learning and artificial intelligence.
In banking, avoiding unnecessary risks is the first reason to drive data maturity through your systems.
Regulatory risks are a major driver. For example, a bank that doesn’t have a mature, data-rich reporting mechanism can suffer significant regulatory harm. Some smaller banks handle loan applications at the branch level and may not be able to perform aggregated fair lending analyses or regressions.
These banks may face regulatory jeopardy if the Consumer Financial Protection Bureau finds that these immature data practices are putting certain classes of people at a disadvantage in loan approval or interest rate decisions.
“It’s important to develop data quality to perform aggregated reporting,” said Grant Thornton Manager, Risk Advisory, Richard Neal. “And then you build the apparatus to report consistently and later you figure out how to make strategic decisions off that data.”
Once banks get comfortable with a data-driven approach to strategy, they often take another step and use data to enhance their customer relationships. Here’s where faulty data can lead to reputational risks.
Customers may not be patient with banks that inadvertently present them with additional opportunities that are based on flawed or inaccurate data.
“They’re going to call you out right there to say, ‘You have no idea who I am. The data you have on me is wrong.’ And that’s a way to lose trust and capability instantly,” Whatton said.
But if data quality is high, new customer opportunities such as pre-qualified offers based on mature data can result in significant additional business for the bank.
Choose technology carefully
Developing data maturity across an entire organization is such a demanding process that it’s easy to mistakenly think there’s a technology solution that you can just plug in to make it all work right. But it doesn’t work that way.
In general, mature data is a prerequisite for — not a result of — effective technology use.
“You need to first figure out the objectives of the business and then determine what technology you need to get to your goal.”
“You need to first figure out the objectives of the business and then determine what technology you need to get to your goal,” Pilch said.
Pilch said it’s also important for data to be digestible and formatted in a way that it can be interpreted. This can be difficult for organizations that have multiple disparate systems in place.
“Some organizations haven’t aligned data across all their different solutions,” Pilch said. “They need a thought process or strategy in place to align data across all the different solutions. You want to make sure that your ultimate deliverable is aligned all the way back to the source systems that are supported from a data element perspective.”
There are enterprise applications or solutions that may help solve the problems that exist when a bank is using multiple, disparate systems. But even enterprise systems are most effective in a culture that prizes data quality and relevant, objective, measurable and complete data.
The encouraging thing for banks is that when they get the data and technology right, there are significant opportunities to drive revenue. Some of the clients that Singh has worked with are skillfully driving profitability through technology that identifies emerging opportunities with specific customers based on their data.
“And it really goes back to the data that you’ve collected,” Singh said. “You can invest in geographies and other market segmentation, appropriately based on and backed by data that allows you to make those informed decisions across the organization.”
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