Data Accuracy vs. Data Consistency: Why You Need Both

We are in an era of heightened customer expectations. Customers expect companies to know who they are. And they want this knowledge reflected in personalized experiences tailored to their needs and preferences. 

Knowing a customer requires organizations to connect the data dots across a myriad of disparate touchpoints and interactions. They must capture and connect everything from contact details and transactional data to social media activity, CRM data, web analytics and more — bringing it all into a single, Customer 360 view that can power the personalization customers need and expect. 

Companies can use Customer 360 views to deliver exceptional experiences that drive customer loyalty, retention, and upsell opportunities. But when data lacks integrity because it is inaccurate or inconsistent, the experiences delivered can suffer or, worse, backfire. That’s why prioritizing the accuracy and consistency of customer data is critical.

Accuracy vs. Consistency of Data: What’s the Difference? 

Data accuracy and data consistency are distinct data quality dimensions that contribute to overall data integrity, making both factors critical for effective decision-making. However, much of the data that businesses rely on is bad. But what is the difference between accurate data and consistent data?

What is Data Accuracy?

Data accuracy refers to whether or not the data is error-free. Measuring data accuracy requires organizations to look at a number of data quality dimensions including:

• Correctness: Does the data contain errors?
• Precision: Is the data clear – or does it leave room for interpretation?
• Reliability: Can a user count on the data?
• Timeliness: Is the data up-to-date?

Accurate data is a critical factor when it comes to decision-making and effective operations. After all, if the data is faulty, so, too, are the insights, decisions, and actions that result from it. 

What is Data Consistency?

Data consistency, on the other hand, ensures data remains uniform, which makes it easier to integrate across systems and sources. Data consistency often includes a set of rules or standards for systems to follow across key measures such as:

• Standardization: Is the data captured, stored, and processed in uniform formats?
• Naming conventions: Does the data follow a standard way of naming across systems?
• Data type: Is each data type (text, numeric, date, etc) captured and stored consistently? 
• Updates: When updates occur, are they applied consistently across data sets?

Data consistency also considers how data is maintained over time, including the historical state of data at a given point in time. 

Consistent data plays an important role in data integration. When data is consistent, it’s easier to consolidate into a golden record that spans systems and sources. When it’s not, integration fails, resulting in duplicate or incomplete records. 

While separate data quality dimensions, both data accuracy and data consistency contribute to overall data quality and data integrity, making them equally important when it comes to improving trust. 

The Business Implications of Inaccurate and Inconsistent Data

Having accurate, consistent data is crucial for businesses to succeed. But the reality for many companies is that their data is poor quality. And poor-quality data is bad for business. In fact, Gartner estimates that poor data quality costs organizations nearly $13 million annually. Ouch. 

From fat-fingered data entry and inconsistencies in the way departments capture data to incomplete records and outdated information, bad data causes many challenges for modern businesses, ranging from bad decisions to sub-standard customer service to reputational harm. And these challenges are keeping executives up at night. In fact, Harvard Business Review cites a study by KPMG that indicates a whopping 67% of CEOs prefer to use intuition and experience over insights generated through data analytics to make decisions.

When customer data is inaccurate and inconsistent, companies suffer from:

• Disjointed customer experiences: The lack of a holistic view of the customer leads to fragmented, disconnected experiences.
• Wasted marketing budget: When customer data is missing values, companies target the wrong customers.
• Inaccurate personalization: If companies don’t know their customers, they risk delivering experiences that aren’t relevant or applicable – rather than those that are properly personalized and drive engagement.
• Compliance concerns: Failure to handle and protect customer data appropriately not only causes risks of non-compliance, but could also result in hefty fines.
• Missed revenue opportunities: Without a holistic view of a customer, sales teams may fail to spot upsell and cross-sell opportunities within their customer base.

The likelihood that an organization’s data is inaccurate, inconsistent, or both is high. However, the news isn’t all doom and gloom. By fixing poor data quality and improving overall data integrity, companies can overcome these challenges and deliver the exceptional experiences customers expect. 

Methods for Ensuring Data Accuracy and Consistency

Keeping data accurate and consistent isn’t easy, but it’s a critical step to realizing the benefits of true and dependable Customer 360 views. Doing so requires companies to employ a number of tactics to improve data quality and preserve it as data and sources change. 

Common methods that companies include as part of their master data management (MDM) strategy include:

• Automating data cleansing: Many companies today employ data quality tools to help them spot and fix inaccuracies and inconsistencies as well as standardize and validate data to ensure it’s trustworthy.  
• Conducting data audits: Reviewing data is a common data management practice that helps companies to spot inaccuracies and incomplete fields.
• Implementing MDM data governance: By establishing clear guidelines on ownership, accountability, and the processes for managing and updating data, companies can help to ensure it remains complete and error-free.
• Creating golden records: With the goal of creating the most accurate, most consistent version of a customer, golden records aim to unify data from multiple sources and create a single, authoritative source.

As data management evolves, many companies are realizing that these tactics alone are no longer enough. And traditional MDM solutions, once groundbreaking, are struggling to get the job done. That’s why many data-savvy organizations are investing in AI-native MDM as a way to accelerate their ability to deliver accurate and consistent data and realize the true promise of Customer 360. Using AI-native MDM, businesses can finally produce trustworthy golden records and provide the clean, quality, customer data needed to propel the business forward.