Federated data governance turns data sharing into a win-win

Data enrichment programs are winning over financial services industry decision-makers around the world, and for good reason. These programs have the potential to be win-win strategies for everyone involved.

However, inconsistencies between datasets can lead to errors that impact consumer trust, regulatory reporting, and financial decision-making. That’s why high-performing teams emphasize data quality, leveraging autonomous AI data monitoring tools that identify anomalies as soon as they reach your tables.

And as we enter the age of Self-Driving Data, AI data agents make it possible to continuously improve data trust and fully democratize monitoring. Monitoring is becoming more powerful and easier to use, so financial services companies get more bang for their data governance buck.

Data enrichment is quickly becoming table stakes

As with all advances in business strategy, word’s gotten around about data enrichment. This process of augmenting first-party datasets with information from third-party or shared datasets provides new insights for decision-making activities. Particularly compelling is the fact that data sharing and enrichment can benefit several core functions in financial services firms.

Perhaps you’re interested in data enrichment because it can help you offer more appropriate products and services to your customers. Knowing about a customer’s recent life changes or purchase history, for example, can help you determine whether to offer that customer a new type of financial product.

Or you might have been drawn to enrichment with the goal of developing a more effective Anti–Money Laundering (AML) strategy. A customer’s transaction may seem above-board in isolation, but more context could expose a different story.

Some companies also engage in data sharing or enrichment to support stronger internal business decisions. After all, an understanding of the overall market is crucial for determining the best course of action in finance or management.

Whatever your expected benefit, the cat’s out of the bag about data enrichment. Most FSI firms now understand the upsides, and many have ongoing data enrichment programs already in place. But not all of those firms understand the critical importance of ensuring data is trustworthy.

Untrustworthy data puts businesses between a rock and a hard place

Untrustworthy data endangers business decisions and corrupts customer trust. Imagine a third-party know-your-customer dataset you’re using to augment your first-party customer records. Inconsistencies in ID numbers between those two datasets could result in duplicate records, possibly fracturing customer information into multiple siloes. You may not notice until it’s time to submit a Suspicious Activity Report (SAR), at which point unravelling the mess is extremely costly.

To add to the problem, third-party datasets are often opaque, whether by design or by happenstance. This makes spotting faulty data that much harder. The decision becomes whether to use data that might be untrustworthy or miss out on the potential benefits of data enrichment.

Autonomous AI data monitoring lets you have the best of both worlds. You can take advantage of the increased information set that comes with data enrichment, while also maintaining customer trust.

Autonomous AI data monitoring democratizes data governance

The right autonomous AI data monitoring tool enables federated data governance and eliminates the burden of manual rule-writing, enable non-technical subject-matter experts (SMEs) to participate in data governance efforts, and let you leverage autonomous AI agents that surface valuable insights around the clock. When monitoring friction is reduced and data governance is a cross-team endeavor, data trust comes naturally.

Legacy data governance techniques primarily revolve around manual rule-writing. As a solo tactic, this doesn’t scale to enterprise datasets. A modern autonomous AI data monitoring system eliminates the need for manual rules, instead using expertly tuned automated checks to cover most tables’ data quality needs. This includes coverage for unknown unknowns, cross-column issues, missing data, and data freshness.

For tables with specific niche concerns, top-tier autonomous AI data monitoring systems will also allow for frictionless custom checks. You may want a check, for example, that confirms a match rate between a specific first-party dataset and an augmented third-party dataset. A useful monitoring platform will make it easy to build and deploy that specialized check.

An autonomous AI data monitoring system should also democratize data monitoring for non-technical SMEs. It should have low-code and no-code check building options, straightforward UI tools, and plain-language visualizations. These tools allow non-technical team members to share in vital data governance efforts. After all, subject matter experts (SMEs) often have important insights into data quality, like memories of past issues with the table or specific concerns about finicky sources.

The most forward-looking data monitoring systems now also offer autonomous AI agents, which work around the clock to surface insights into your data’s health. These proactive insights help data governance team members react to data quality issues more quickly and with less friction. When triaging, a team member can chat with their data and ask about possible root causes for the issue. Agents also handle the “nice to haves” like documentation, for times when non-urgent tasks have to take the backseat.

And the utility of agents isn’t limited to times of crisis. Even when there aren’t any immediate data issues, team members can chat with agents to independently validate shared data, ask data analysis questions, see check result history, and more.

Choose the right data monitoring solution for you

We’ve covered the basic feature sets that autonomous AI data monitoring platforms should have, but each platform is built differently and it can be confusing to wade through the specifics.

When deciding on the right data monitoring platform for you, start by soliciting opinions from a range of stakeholders. Ask your data engineering team, data governance team, and SMEs about their pain points. What issues have come up recently? What issues are they worried about for the future? The autonomous AI data monitoring vendor you choose should be able to articulate specific solutions.

The vendor’s history, experience, and strategy may also affect your decision. Ask your vendor about:

• Their history of supporting enterprise FSI firms
  Not all data monitoring learnings track from one industry or scale to another. A monitoring solution not informed by both industry knowledge and enterprise data experience won’t have all of the necessary context to detect high-impact FSI data quality issues.
• Their level of experience with onboarding non-technical users
  Although some of your SMEs likely have technical experience in SQL, APIs, or data governance best practices, one of the most powerful “wins” in data monitoring is getting non-technical users engaged in data trust. The most successful vendors will be able to show a clear and proven path to onboarding all end users.
• Their strategy for agentic AI data monitoring
  This strategy should be consistent with real-world use. Anyone can say they’ve baked AI into their product, but fewer vendors will be able to describe the real-world benefits agentic AI brings to FSI data trust.

One of the autonomous AI data monitoring platforms you may discover in your search is Anomalo. As a longstanding innovator in the enterprise data monitoring space, Anomalo has the background and expertise to execute on a clear, powerful vision for the future of automated AI data monitoring. And we back that up with day-to-day features that bring in SMEs and non-technical users while empowering highly technical data governance teams.

How Equifax democratized data quality

Equifax maintains information on more than 3.6 billion U.S. tradelines, which means a staggering amount of data needing to be monitored. This tradeline data is vital for Equifax’s business, so data trust is critical for operational resilience, customer satisfaction, and regulatory compliance.

As part of the market-wide shift toward data sharing and data enrichment, data flows were becoming more and more interconnected. Equifax had to prepare for (and proactively prevent) potential cross-team data issues.

When Equifax implemented Anomalo, they brought in a diverse group of stakeholders from the start. This included business owners, compliance experts, and data engineers. These stakeholders made up a cross-functional Data Quality Working Group, which formalized Equifax’s commitment to data trustworthiness.

With the help of Anomalo’s autonomous AI data monitoring system, Equifax was able to standardize their data governance operations, increase operational efficiency, and create a cultural shift toward the democratization of data quality.

What’s next?

As data enrichment programs become table stakes for financial services companies, data trust is more important than ever. Anomalies like corrupted data and typos between datasets erode consumer trust and influence internal decision-making.

Anomalo’s revolutionary agentic AI capabilities, stacked on top of a proven, enterprise-ready autonomous data monitoring platform, have helped financial services companies around the world democratize data governance. Our FSI solutions make it easy to trust your data and your decisions.