How to increase your overall data quality by enabling data self-service for business users

Bad data creates bad decisions, and that costs time and money.

It's hard to prioritize fixing a problem you can't see, and most organizations don't have great visibility into just how much poor data quality is costing them until it's already caused a problem.

Here's the thing, though. Improving data quality offers a significant opportunity for businesses. Maybe you can't see the impact of poor data quality today, but you'll definitely see better data results tomorrow.

In this article, we look at how you can get everyone engaged in improving data quality by enabling data self-service for business users.

Key takeaways

• Data self-service means enabling business users to access, analyze, and manipulate data themselves, reducing IT support requests while helping business users learn to identify bad data.

• The 1-10-100 rule shows why this matters financially: catching bad data at the source costs $1, fixing it after it's been transformed costs $10, and letting it reach a strategic decision costs $100.

• Nearly 60% of organizations don't measure the cost of poor-quality data, even though Gartner estimates it costs an average of $12.9 million a year.

• A 10% improvement in customer data quality is linked to a 5% improvement in customer responsiveness, according to Gartner.

• Self-service works best when IT retains control of source data, so non-technical employees work with pre-validated data catalogs rather than the raw source itself.

• Data self-service is one piece of a broader data quality strategy, alongside validation, error handling, and governance, not a replacement for any of them.

How data self-service boosts your data quality

As Melody Chien, Senior Director Analyst at Gartner, explains: “Data quality is directly linked to the quality of decision making. Good quality data provides better leads, a better understanding of customers, and better customer relationships. It is a competitive advantage that data and analysis (D&A) leaders need to improve upon continuously.”

That link between quality and decision-making has a real price tag attached to it. Gartner estimates that poor data quality costs organizations an average of $12.9 million per year, and nearly 60% of organizations don't even measure the cost of poor-quality data, which makes it hard to justify fixing a problem whose size nobody's measured.

Data self-service means enabling business users to access, analyze and manipulate data themselves. It means fewer IT support requests. And it encourages business users to engage with data, helping them to learn how to identify bad data.

Why is this important? The 1-10-100 rule states that detecting quality problems early in a process is less costly than catching them later. So, if you identify bad data at the source, it'll cost $1. If it's identified after a business user has spent time transforming this data into something actionable, it'll cost $10. But, if a business user takes bad data, transforms it, and then uses this to make a high-level strategic decision, it'll cost $100.

This is exactly the same principle behind catching sources of bad data as close to their origin as possible, self-service is one of the mechanisms that makes catching problems early happen in practice, since the people closest to the data are the ones most likely to notice something's wrong.

Implementing data self-service helps ensure that bad data never reaches the decision-making stage. Here are a few ways data self-service boosts your data quality:

1. It educates users on how to use data effectively

It can be challenging to judge data quality if you're not regularly engaging with it. Data self-service allows everyone at your business to engage with and analyze data on a daily basis. This helps to educate business users on the best practices for identifying data errors and gaps in your data collection processes.

However, it's important to use a platform that allows you to offer data self-service to your business users while IT retains control of source data. This way, non-technical employees are only working with pre-validated data catalogs. If they make a mistake, it won't impact your source data sets. If they identify an error, your IT team can follow up and verify whether it's an error with their particular catalog or an error in your source data that requires more investigation.

2. It supports open feedback between IT and the rest of the business

Although data self-service reduces interactions between business users and IT, it doesn't silo them. A self-service culture thrives when there's an open feedback loop between business users and your IT team. Encouraging feedback helps your IT team optimize source datasets, benefiting business users in turn.
If your IT team prepares data and your business users interpret it, your teams can improve their data flow. They can also speed up their "time to decision-making" cycle. And, when a problem arises, collaboratively investigate to determine what went wrong, the same kind of collaborative error management process we cover in more depth elsewhere.

3. It increases user engagement

Data self-service allows business users to work autonomously. By transforming and analyzing this data, they can identify actionable takeaways that inform decisions. Being part of the complete analysis and outcome loop, they can see where data errors may have contributed to poor outcomes. They begin to see why good-quality data is so important.

An incorrect decision that leads to further data analysis supports accountability. It also teaches data handling best practices. On the other hand, a correct decision that leads to business growth leads to year-end bonuses and higher commissions. Both outcomes encourage user engagement.

4. It streamlines your data processes

Data self-service reduces the number of support requests from business users to IT, allowing both teams to develop automated and autonomous working methods. Business users can compile and update their own reports using the latest data whenever they need. This frees up your IT team to prioritize more complex and valuable tasks rather than fulfilling routine requests. It also encourages business users to engage with data more frequently and at greater depth, without the delays that arise from needing technical support.

Data self-service with the CloverDX platform

According to Gartner, a 10% improvement in customer data quality is linked to a 5% improvement in customer responsiveness. This is because your teams can serve customers faster. They can provide more relevant answers using high-quality and trusted datasets.

Efficient data onboarding processes combined with data self-service can significantly increase your data quality. But in order to achieve this, you need to use the appropriate tools.

The CloverDX platform gives business users self-service access to the data they need to make smarter decisions, while allowing your IT team to retain full oversight.

Data Manager enables non-technical users to review, edit and approve data as part of an onboarding workflow, and Wrangler gives users a simple visual interface to create their own data transformations.

Self-service is one part of a broader picture. If you're building this into a wider data quality strategy rather than treating it in isolation, that's where governance, profiling, and monitoring come together with the people side covered here. And if the goal is catching bad data before it ever reaches a business user, this guide to data validation in data ingestion covers the technical mechanics that work alongside self-service, not instead of it.

Book a demo today to learn more about how the CloverDX platform can increase your data quality by enabling data self-service.

Final thoughts: The people closest to the data catch problems first

Automated validation and governance matter, but they can't be the whole strategy. The people who work with your data every day are often the first to notice something's wrong, and giving them the tools to act on that, rather than routing every fix through IT, is what turns data quality from a technical project into something the whole business owns.

Let's talk about how CloverDX can improve data self-service and data quality for your team.