By treating data as a product, banks can maintain its quality, utility and relevance.
By Young Pham
Customer data can help banks eliminate manual processes, sell more personalized financial products and improve customer experience. But for many banking leaders, managing their data takes a lot of work.
In marketing, for example, nearly half of leaders need help to collect and organize customer data, not to mention handling analytics, reporting and tracking progress. As a result, data is often hard to use, impacting activities like customer targeting and behavioral analysis.
However, with the right approach, bank leaders can help teams manage their data more efficiently. Here, I’ll offer four steps that can help.
1. Identify the data-driven outcomes you want
Many banks approach customer data with a “more is better” mindset. They are racing to collect as much information as possible from every available source, from transaction records to social media.
But suppose you are collecting data just for data’s sake. In that case, your bank likely must align its data needs with a specific business goal (such as improving credit risk models). And if you wind up with too much data, ingesting it can overburden teams and waste resources. The impact: data that’s costly to work with and perhaps unusable.
Instead, tailor your data ingestion process to your desired business outcomes. Ask yourself:
• What result do I want to achieve?
• What data-driven strategies will help me get there?
• What kind of data—and how much of it—do I need to succeed?
For example, let’s say your bank is struggling with fraud detection. You can start by focusing on a single desired outcome (say, reducing fraud in the underwriting process for new credit card accounts). Then you can identify the right analytical strategy.
If you want real-time analysis, you will need an efficient way to ingest multiple financial, geographic, and demographic data sources. But compared with a sampling-based strategy, you may be able to limit the quantity of data and data sources. As a result, you will only ingest enough data to achieve your desired outcome. That means higher-impact data collection without draining employees and resources.
The bottom line: By letting your desired outcomes guide your data ingestion, you can better optimize your data foundation.
2. Treat your data like a product
Even banks with clear data goals often ignore a crucial success factor: data quality. Maybe a customer dataset includes duplicate addresses, outdated household information, or missing digits in their phone numbers. This low-quality data isn’t just unwieldy. It can lead to inaccurate insights that impact your bank and customers.
That is why thinking about data like a product is essential. Like smartphones or packaged goods, banking data needs regular quality checks before it’s ready for employee and customer applications.
To adopt a product mindset, I suggest regularly checking three aspects of your data:
Accuracy: Check whether each data point is correctly formatted for its purpose (ten digits for every phone number, etc.) and avoid duplicates in the same dataset.
Timeliness: Keep all data up to date. Ensure there are processes to remind customers to update information that can frequently change (like their primary residence or phone number).
Consistency: Each data source should regularly provide quality data. If not, look for ways to ensure quality at the entry point (such as auto-flagging form entries with missing information before submission).
By treating data as a product, banks can maintain its quality, utility and relevance.
3. Weigh the benefits of cloud, on-premises or hybrid data storage
Viewing data storage in black and white is easy: Go all-in on the latest cloud technology or stick with a legacy, on-premises system.
But effective data management requires a more nuanced approach. Many cloud storage providers offer scalable storage capacity and enable advanced predictive analytics. These services, though, can be expensive and excessively feature-rich.
On the other hand, on-premises systems give banks more server control and maximize data security, keys for sensitive information (such as social security numbers). But these systems are hard to scale and access off-site.
To choose the suitable data storage model for your bank, I recommend considering three variables:
Need: What are my bank’s essential storage needs? Which storage capabilities do I need to achieve my business goals?
Cost: How expensive is this storage solution, and how flexible are the prices?
Outcomes: What do I want to use this stored data for (basic reporting and analytics versus more advanced predictive modeling and app-building, etc.)?
Going 100 percent to cloud for data storage may not work for your organization. But you can still reap the benefits of cloud storage with a hybrid model that uses an on-premises system to secure the most sensitive information.
4. Establish a data governance strategy
Regarding data, regulatory compliance is a top priority for banks. But there’s also a trust factor to consider. More than 40 percent of customers say trust is a huge loyalty driver. But just 37 percent place a high level of trust in their banks’ data handling.
As banks expand teams’ data access, it’s crucial to establish a clear data governance strategy with protocols that ensure ethical and compliant handling.
To flesh out your strategy, I suggest considering:
How teams are leveraging data. Are they building apps or compiling reports? And is data use limited to sales and marketing or is it applied throughout the organization?
Which employees need access. A sales representative may require different data access than a business analyst.
Your data’s traceability. You should be able to trace all data to its origin (a credit database or third-party data stream, etc.), tracking every change and stopping point.
Your specific regulatory constraints. Community and regional banks have different regulatory restrictions than national or global ones.
With a robust data governance strategy, you can consistently leverage data in a trust-centered and compliant way.
To win the data arms race, focus on data management
At most banks, data goals do not exist in a vacuum. Leaders are keenly aware of the rapid digitalization of traditional competitors. Not to mention new data-first challengers among neo-banks and fintech companies. However, winning depends on your data management strategy. In 10 years, the banks on top will have a strong data foundation that can adapt to changing customer, employee and regulatory needs. But the ones without one will be left behind.
Young Pham is chief strategy officer at CI&T, a global digital specialist.
