How To Ensure Data Quality in Financial Services

The financial world involves a high volume of transaction data. Financial services firms and their clients need a constant stream of high-quality data to make the best decisions, create accurate reports, and stay compliant to succeed. 

The key word here is “quality.” Not all financial data is equally valuable, and if you’re not careful, you could make bad decisions or submit inaccurate reports based on flawed data.

If you don’t properly maintain data quality in financial services, you risk issues ranging from customer churn to legal action. Customers must be able to trust institutions to handle their money. 

How can you ensure that your financial service only uses the best quality data? 

Let’s take a look:

Why does data quality matter in financial services?

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In a sector where precision and accuracy are everything, poor-quality data can cause massive problems. If your data is inaccurate, outdated, incomplete, or otherwise dirty, you risk making bad decisions for your customers and your business.

Four ​​key benefits of high-quality data in financial decision-making

With high-quality data, on the other hand, you can:

1. Accurately manage risk

Good quality data will accurately reflect things like market volatility, credit risk, and so on. Proper risk management allows you to accurately calculate the risk of any given investment, loan, or course of action.

2. Keep customers happy

When people use a financial services provider, they expect high accuracy and reliability. They want their bills to be accurate, their investments to pay off, and their recommendations to be on point. High-quality data helps make this possible.

3. Stay compliant

Regulators worldwide insist on a high degree of accuracy in things like financial reporting. Keeping your reports and activities compliant is easy if your data is high quality. But if your data has inadequate quality, it’s equally easy to fall out of compliance without realizing it, which can, in turn, result in fines and even prosecution in some cases.

4. Operate efficiently

With good-quality data, you don’t have to waste time running extra checks or combing through your calculations to find which faulty data point has caused an error. Instead, you can operate in a fast, accurate, and efficient manner that gets results.

How to maintain data quality in financial services

It’s clear that data quality is essential in financial services. But how can you sustain that data quality?

Here are some guidelines:

Set a data governance framework

Maintaining data quality is much easier when you know what high- and low-quality data looks like and have protocols to measure your data quality.

A data governance framework will help you to identify quality controls, set policies and standards for data collection, and give you frameworks to follow when things go wrong.

You must first set your data governance strategy before building a data governance framework. Work out what you hope to achieve with your data governance framework. Identify challenges standing in your way and common problems you must solve. Then, look for ways to overcome these problems and build your framework.

There is no “one size fits all” framework for data governance. The framework you build will be tailored to the specific needs of your business. If you’re struggling with data governance, you can find advice here.

Carry out regular data audits

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Regular audits are crucial to data quality control. Use your data audits to check that all your data is relevant, timely, and accurate and to clean your databases of poor-quality data.

There are usually four distinct steps to a data audit:

1. Gather process information. Understand the current state of your data processes by talking to the relevant department managers and data teams.

2. Locate your data. The data you use may be stored in several different databases or platforms. You must know where every relevant piece of data lives. It is crucial to have a centralized repository like the Alation Data Intelligence Platform to find the data easily.

3. Identify the most valuable data. Determine which data is most relevant and helpful to your company and focus on ensuring that this data is impeccable, understood, and easy to access.

4. Check data quality. Assess the data you’ve identified for accuracy, relevance, timeliness, completeness, etc. 

If all of this sounds laborious, don’t worry. The right tech can make it easy.

Invest in data quality and observability tools

Data quality and observability tools are game changers in improving data quality. No matter what kind of data you’re dealing with or which department uses that data, there’s a platform that can make organizing, auditing, and analyzing that data very easy.

When determining the right tools for your organization, it is vital to understand that different tools specialize in data quality by industry, region, and business type. Due diligence must include a proof-of-concept to ensure the data-quality product will meet your organization’s needs.  

Integration is another critical factor for success. Take, for example, Alation's Open Data Quality Framework's flexible approach, which integrates the best-of-breed data quality vendors. Insights, including quality, reliability, and performance, are seamlessly displayed in the Alation Data Intelligence Platform.

Implement data quality training programs

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It’s much easier to maintain data quality when everyone is involved. So, bring data quality training programs to your business.

Ensure data quality is at the forefront of every team member’s mind. Data quality starts with data being entered into applications. Everyone in the organization must understand this. Train your people in the importance of data quality, protocols, spotting poor-quality data, and safe data handling techniques.

For the best results, start data training as soon as possible. For example, you could build data quality training into your onboarding process so your new hires are instructed about data quality from day one.

Remember, data and data quality processes change relatively quickly, so it’s a good idea to run refresher training sessions frequently. 

Create data stewardship roles in your organization

Data stewards take ownership of and responsibility for your organization’s data. By creating data stewardship roles, you clarify who is responsible for what and to whom people should turn if they have data-related questions.

Carving out specific data stewardship duties also means that data quality control will be more reliable and efficient. Rather than assigning data quality tasks as and when they arise, you will have dedicated people constantly monitoring your data for quality issues. 

While this doesn’t mean that the rest of your team should let up their data management responsibilities, it does mean there’s far less scope for problems to slip through the cracks. And, if they do, your data stewards should have valuable insight into how, when, and why the problem occurred.

Evaluate third-party vendors for quality standards

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If you’re working with third parties, ensuring their quality standards won’t compromise your own is an excellent idea. Everyone you work with must take data quality seriously and be dedicated to protecting customer data at all costs.

How can you do this? First, just ask. When deciding which vendor or solution provider to work with, send questions, conduct interviews, ask to see demos, etc. If what you learn doesn’t leave you confident that the company can fulfill your lofty quality standards, keep looking.

Data quality in financial services is so vital that you can’t risk compromising it with third parties who may not take data quality as seriously as you. Be diligent, and do your homework before working with any third-party vendor.

Prioritize data quality in financial services!

The better your data quality, the better your results will be. Good quality data helps you provide the best service. With high-quality data, you can accurately judge risk, make data-driven decisions, run an efficient service, and keep your customers happy.

Data doesn’t maintain itself, however. Even the best quality data degrades over time. Gathering data properly and managing, organizing, analyzing, cleaning, and auditing your data regularly is essential. Similarly, training your team members in data quality protocols tightens up your quality control so no poor quality data can slip through the cracks.

The bottom line is that maintaining data quality is extremely important if you want to provide sound financial services. By following the tips in this article, you can ensure that your data and the services you provide are always top quality.