What is Data Quality?
When you think about the quality of your data, what things come to mind? For me, it goes way beyond simply having accurate information. It’s much more all-encompassing & includes completeness, timeliness and relevance of the data. Think about it this way – how does it make you feel when you receive an email that starts off with “Hello <First_Name>”. Does that fill you with confidence that the rest of the message will be tailored to meet your needs or provide you with information or an offer that is exactly what you are looking for in that exact moment?
Compare that to an email that starts off with “Hello Eric” For the sake of this article, assume your name is Eric. Or every time you see “Eric”, substitute that with your actual first name. That invokes a much different feeling, doesn’t it? For me, I feel like the person (or company) who is emailing me, knows who I am, understands what I’m looking for & is prepared to have a good conversation with me as we build a great business relationship together.
Why is Data Quality Important?
Bad data wastes time & resources. It clogs up systems like email & Salesforce. It costs companies real money. According to Gartner’s 2021 “How to Improve Your Data Quality” study, “Every year, poor data quality costs organizations an average of $12.9 million.” That’s a lot of money! This cost comes in many forms: lost revenue, increased operational costs, penalties for compliance violations in regulated industries, and possibly the worst category – damage to a company’s reputation.
Overall, the cost of poor data quality can be significant, both in real, financial losses and also other negative ways. Investing in data quality management and implementing best practices can help reduce the risks of bad data as well as mitigate tangible costs and ensure that your business is basing decisions on reliable, high-quality data.
How to Transform Bad Data into Quality Data
A few years ago, when I thought about bad data, my mind immediately went to duplicate records. Or “almost duplicates” – where one or two pieces of data about a person or company were different, and all other information was identical. But that’s just one piece of the quality puzzle. You don’t have quality data if you’re missing key pieces of information or if the data you have is no longer valid.
What is a key piece of information? Anything that’s important to your company’s decision-making process. Anything that helps you understand who your customer is & how they make purchasing decisions. It could be something as simple as an email address, or even what time zone the individual resides in. As you think about your own data quality journey, be sure to think beyond the obvious as you look towards the future.
The real challenge is how to maintain quality data in real time, as new data is being input or existing data is updated in Salesforce. The DQE Name module does real-time validation of all your customer contact details, names, postal addresses, emails, phones and company information with global coverage for more than 240 countries. This is extremely important for e-commerce businesses to ensure customers will receive their orders in a timely manner. It’s also crucial for call centers as they strive to provide the absolute best customer service experience.
All this ties nicely to the idea of having a single, unified customer record. Imagine this: You return to a website to browse their latest sales and a pop up appears, “Welcome back, Eric!” And you are presented with items matching your last search, items that coordinate with your latest purchase and a discount offer for your next purchase. Nice. They know who you are and value you as a customer.
Long Term Approaches to Quality Data
You may think that once you have cleaned up your data, you’re good to go. Nothing could be further from the truth. Now it’s time to develop a strategy that will keep your data quality at the highest level. Build the processes, get the right tools in place – do it now, proactively. But slow down, just a bit. Use a bit of discretion and move forward cautiously. The quality of your isn’t something to take lightly. Be sure to look towards the future and realize that change is inevitable, but having quality data should be a constant.