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 Outdated, siloed, unformatted, duplicated and otherwise bad data can be the culprit that’s ruining your customer experience.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?
What Makes Data Bad?
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).
Related Article: How to Prepare Data for Ingestion and Integration
What’s the Problem With Outdated 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.
George Schoenstein, senior vice president of marketing and corporate communications at Fusion Connect, told CMSWire that his solution is to use a multi-pronged approach to data integrity and cleansing. “Our account representatives augment client contact information as part of their daily interactions with the client base.”
Additionally, Schoenstein said that his clients can update their own information within their systems. They also use third-party services to identify changes to client information, such as when someone’s switched jobs.
Is a Customer Data Platform the Solution?
Assad Jarrahian, chief product officer at Unanet, told CMSWire that metrics overload often distracts brands from focusing on what really moves the needle.
"New technology tools are available to capture data and filter out the anomalous, incomplete information,” said Jarrahian. “Automated analytics capabilities provide insight into data that is easy to view and manipulate.”
Obtaining actionable insights is one of the primary goals of utilizing good data, and fortunately, technology enables brands to wade through the mire to obtain them.
“To tune out the noise and hone in on the numbers that truly drive your business, companies should look for tools to help them easily glean actionable insights. These insights can reveal important trends and anomalies,” said Jarrahian, who added that high-level metrics should be simple, measurable and, most importantly, relevant to organizational objectives.
Customer data platforms (CDPs) are often used to tame the data beast. Steve Zisk, senior product marketing manager at Redpoint Global, told CMSWire that according to IBM, the annual cost of poor-quality data is over $3.1 trillion in the US alone. Brands have to run many different campaigns across a number of different digital and traditional channels, Zisk said, and fragmentation occurs because sifting through all that data without assistance can be difficult, and key information is often missed.
"That’s why marketers, especially those who need easy access to large data streams, may need to rethink their current data practices,” said Zisk. “Achieving high-quality data takes a dynamic customer data management platform that will maintain the quality needed for today’s real-time world.”
Zisk emphasized that a CDP should be enterprise-class, able to ingest data from internal and external sources in batches and in real-time, provide quality assurance (e.g., merging, matching and identity resolution), create an identity graph of each customer along with all profile and transactional history and provide automations and workflows that enable brands to work on strategy while the system handles personalized interactions at scale.
“Data pulled from a ‘no-frills’ CDP or stagnant data lake (aka, data swamp) may be fine for a small business, but it creates poor customer experiences for large organizations,” explained Zisk. “Data streamed from a comprehensive, dynamic CDP enables marketers to engage customers instead of exasperating them.”
Related Article: Understanding the Key Components of a Customer Data Platform
Bad Data Could Require Multiple Tools to Fix
Josh Perlstein, CEO at Response Media, told CMSWire that the solution to the problem of disparate or outdated data does not rely upon a single solution but requires a combination of solutions, both technological and human.
Perlstein said brands should budget for and integrate data from multiple sources through a combination of tech (platforms, CDPs, application programming interfaces (APIs), web services) as well as people. “This requires an ongoing process of writing business rules, testing, QA (quality assurance) and validation. Keep in mind — these processes aren’t just for setting up data sources but are ongoing as data from various sources can change.”
Perlstein believes that brands must consider two primary things when looking for both the freshest and most actionable insights. The first is to automate incoming data integration from the freshest sources.
“For our digital marketing clients,” he said, “this means we’re pulling in behavioral data from owned digital assets (websites, ecommerce platforms, email, etc.), registration data (zero-party) from those websites and social properties as well as data from paid media (think search, social, display, video, etc.).”
Through automation, Perlstein said brands can integrate this data in almost real-time, which in turn leads to the second primary focus. “Make the data actionable by integrating CRM (customer relationship management) and marketing automation platforms, CMS systems and the like to enable immediate segmentation, personalization and action (such as sending an email, delivering relevant content, etc.).”
What Are the Challenges of Bad Data?
According to Goodman, bad data isn’t the only problem that brands face — data confidence is equally important to data quality.
“The problem is that many business leaders have become conditioned to bad data and have developed an inherent resistance to using data. This can be an even bigger problem when analysis of a set of data challenges the established norm,” Goodman explained.
The new information may be disregarded by leaders, who then might miss out on new insights into their business or their customers. “Given that confidence must be inspired, organizations must take a ‘technology meets humanity’ approach,” said Goodman, whose employer, NTT DATA, uses CRMs and encourages their clients to do so as well.
According to recent NTT DATA research, most respondents have made or are making significant investments in foundational technologies, such as enterprise resource planning (ERP), CRM and cloud, to drive performance. Goodman said that of those who did so, nearly 75% reported higher financial performance, growth and ROI as a result — and nearly 75% reported gains in customer satisfaction.
Dr. Alvin Glay, Response Media VP of growth and analytics, told CMSWire that good data is the backbone of any data-led organization, and as such, it's pertinent to have proper data hygiene and data governance protocols in place to maintain data integrity. "The implication of bad data on business outcomes cannot be understated. Unfortunately, too many firms fall into this trap."
According to Dr. Glay, proper data governance is vital to maintaining data quality and cleansing.
“For instance, the two most important data you can acquire are declared data — data you explicitly receive (signups, transactions, etc.) and observed data (behavioral data from media, website logs, email, etc.),” he said.
To obtain this data, brands need the technology to validate and cleanse the data in real-time. “Developing a detailed data architecture map and implementation plan allows you to maximize the benefits of good, clean and quality data to inform business outcomes,” said Dr. Glay.
Brands have a vast amount of data available from all of their various channels — both digital and analog. As such, it’s extremely important that all of the information is cleansed, structured and fresh to be usable by the technology that can provide actionable insights.
By using artificial intelligence and machine learning in conjunction with CDPs and CRMs, brands can tame the data monster and provide their customers with an exceptional customer experience.