How to Fix Bad Data — and Use It to Feed CX
Bad data can be the bain of a brand’s existence. Learn how to transform this bad data and use it to promote great CX.
Poor data quality is often the cause of negative experiences for customers, leads and employees. Outdated, siloed, unformatted, duplicated and otherwise bad data can be the culprit that’s ruining your customer experience.
Misspelled names, inappropriate product suggestions, undeliverable messages, duplicate communications, inaccurate transactions and customer service histories — all these issues stem from bad data and lead to customer frustration, annoyance and overall negative emotional experiences.
What can brands do about bad data, and what causes it to occur?
Consumers produce vast amounts of data every day through their interactions with brands’ websites, apps, service centers and chat servers. According to a 2020 LinkedIn Pulse report, every single person creates 1.7MB of data every second, and humanity produces 2.5 quintillion bytes of data every day.
With so much information being produced, how can brands ensure they’re not collecting bad data?
Keep in mind: bad data is not just a problem for brands interested in improving their customer experience — it also affects return on investment (ROI). In 2017, Gartner estimated that inferior data costs brands $9.7 million per year.
Data is considered to be “bad” if it is unstructured, inaccurate, inconsistent, incomplete or contains duplicate information. All of the data brands collect comes from a variety of channels, many of which are siloed, and much of the data comes in different formats or from different databases. Other data is more random and is not formatted, with no consistency, and must be aggregated in a structured, consistent way for it to be useful.
Michael Goodman, vice president of data, intelligence and automation at NTT DATA Services, told CMSWire that as humans, we store our ideas of the world and everyone we meet as feelings, memories and impressions. For businesses, this worldview exists through data, much of which has yet to be cleansed.
“The only way companies can store that same view of the world and everybody they meet, in this case customers, partners, etc., is as data and the insights and intelligence derived from it,” said Goodman. “The challenge is that raw data is often a fleeting resource and can be very messy.”
To correct bad data and turn it into good data, it must be “cleansed.” Data cleansing is described as the process of fixing unstructured, incomplete, incorrect, duplicate or otherwise erroneous data in a data set and involves identifying errors and updating, fixing or removing them, improving the quality of the data (i.e., making it “good” data).
Although it may seem negligible, data that is old or outdated is often worse than bad data. Brands that try to use outdated data to inform their decisions will be doing themselves and their customers a disservice.
Consider consumers in 2018 and how they approached brands and shopping, both online and in brick-and-mortar stores. Flash-forward to 2022, and the landscape (especially post-COVID-19) looks quite a bit different.
For instance, many shoppers today purchase their products online and then drive to a store to pick them up curbside — or have them delivered directly to their door.
Additionally, customer demographics change over relatively short amounts of time: people change their name, address, age, marry, have children, switch jobs, get promotions, adjust their income level, education level and, as noted above, change their shopping and spending habits.
On an individual level, using outdated historical customer purchase history may be deceiving or outright incorrect, especially when used to obtain actionable insights.
