Bad data creates bad decisions, and that costs time and money.
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 said, it’s hard to prioritize fixing a problem you can’t see. While Gartner estimates that poor data quality costs organizations an average of US$12.9 million per year, nearly 60% of organizations don’t even measure the cost of poor-quality data.
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.
What is data democratization? How to make data accessible to business users more easilyData self-service means enabling business users to access, analyze and manipulate data. 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.
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:
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.
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.
Data self-service allows business users to work autonomously. By transforming and analyzing these data sets, 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.
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.
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.
But in order to achieve this, you need to use the appropriate tools. Without a data platform that supports effective self-service, you’ll struggle to reap the benefits.
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. Its new Wrangler feature also enables non-technical users to work with data autonomously using a simple visual interface. With the right tools in place, you can create a company culture that promotes data education.
Book a demo today to learn more about how the CloverDX platform can increase your data quality by enabling data self-service.