Quality Data Drives Good Outcomes

What is data quality?

In the CRM community, data quality is measured and maintained using seven steps, including:

• Data profiling
• Deduplication
• Standardization
• Verification
• Enrichment
• Automation
• Monitoring and Maintenance

Each of these steps must be applied to ensure high-quality data. When your business collects customer data, it may seem like you’re on the right track because you’re gaining information that can help with overall business operations. However, if you’re not maintaining a high level of data quality and keeping it clean, your organization may be wasting valuable resources and time with poor-quality data that’s leading you astray.

The problem with poor data quality

Bad outcomes rooted in poor decision-making

As businesses utilize data more and more to influence their day-to-day decisions, processes and strategies, data quality has become a core signifier for future success — specifically, whether it happens.

Data processing is only as useful as customer data integrity. If you have poor data quality, your company is likely to lose more than just money. Other potential impacts include:

• Loss of operational efficiency
• Reduced campaign effectiveness
• Competitive disadvantage
• Poor long-term strategy choices
• Tarnished brand reputation
• Disrupted customer journey
• Employee churn

Lack of awareness of opportunities and threats 

If your data doesn’t serve you, it’s essential that you implement methods to fix your data quality.

Inefficiency

The components of quality data

By asking yourself how your data performs in each of the data quality dimensions, you can streamline your data quality management and pinpoint where to make changes to improve.

Accuracy

Accuracy is arguably the most straightforward component of quality data. It determines exactly how precise or error-free your gathered information is to let you know how much trust to put into it.

For most companies, data is collected from many sources (depending on the industry, think web-to-lead and web-to-case forms, point-of-sale systems, online reservation sites, etc.) and stored across multiple systems instead of being streamlined into one cohesive platform.

The result of this is the higher potential for inaccurate and inconsistent data, for example, unreliable data.

A solution to this is to implement a data management system that can verify key components of customer data like email addresses and standardize data points used to fuel reports. Both are easy to implement, can be automated quickly after implementation and will improve data quality and accuracy substantially.

Completeness

When gathering data, it’s critical that your business considers exactly what information your company needs to ensure the completeness of your customers’ data.

In other words, does your data include every piece of information that your teams need to properly apply and use the data? If not, then you’re working with incomplete data, which will negatively impact your data quality.

Here is a simple example of incomplete data: You’re building out a mailing list, but the customer doesn’t include their address. Now, you’ve only collected partial information, which makes the entire submission essentially useless.

Some methods to combat this are:

• Make a checklist of every point of data needed
• Enable required fields for customer-submitted data
• Use AI technology to sort through data weak points
• Run exception reports to surface records with missing data points

Reliability

Good quality data builds trust — and reliability will help to ensure you can trust your data and build confidence across your organization.

In terms of data quality components, reliability means that a piece of information doesn’t contradict the same or another piece of information existing in a different source or system.

An example is if you track your customer’s birthdate in two unconnected databases. In one system, your customer’s birthday is listed as 12/9/1982 while in the other system it says 12/9/83. These contradictory values for the same datapoint render your data unreliable.

If your data contradicts its own past data or another source’s data, it can signify inaccuracy and hurt your business’ ability to trust and use the collected information.

Relevance

Similar to accuracy, relevance can make or break a data set’s usability to your team. Expansive and thorough data doesn’t mean anything if the actual content isn’t needed or useful to your company.

An easy way to figure out if your data is relevant is to ask yourself and your team members: why are we collecting this information? What will it be used for?

If the answers don’t fit the needs and strategies of your business, then you might be gathering irrelevant information, resulting in low-quality data. Data for the sake of data is a waste of time, money and resources for companies. It creates noise and drowns out the value of your relevant data points. To ensure good quality data, your data has to be relevant to your current and future goals.

Timeliness

Was your data collected recently? Was data entered into the system in the right time frame to not delay workflows dependent on that information to function accurately? If the answer is no, then your business’ data may be struggling with timeliness, which indicates how up-to-date your information is.

It may seem like once you collect your data, you now have the information you require to take action and make decisions. However, data needs to be collected in a timely manner to make sure that it’s not out-of-date.

In many cases, data from a year ago — or even a month ago — won’t accurately reflect the information you need for today. Proper data quality management needs to be aware of how often data needs to be collected to ensure you have the most current data.

Validity

When discussing data quality, validity indicates if your information is conforming to a recognized data format.

An easy way to understand this concept is to consider how dates, like birthdays, are collected. Depending on your country, industry or business standards, dates may be ordered in a specific way (think month/day/year or day/month/year) or collected in precise format (numbers vs. words).

Either way, if the information is entered without following the specific business rules, it’s going to appear invalid.

What are the benefits of good quality data?

If data meets every component of good quality data dimensions, businesses benefit across the board from positive impacts like:

• More informed decision-making
• Better audience targeting
• More effective content and marketing campaigns
• Improved relationships with customers
• Easier implementation of data
• Competitive advantage
• Increased profitability

Since your team leaders and members can trust and rely on good quality data, they’re more likely to use it effectively, which will accelerate your growth and profitability.

Data quality management stakeholders

Below, we will discuss some data management stakeholders that also play a role in data quality maintenance.

Top management

If there are any data pain points that need to be addressed, detailed reports should go to top management for review.

Direct management

Data quality management can make or break your business in the long term, so it’s essential that you understand the points of leadership that can ensure proper data quality standards. If you can pinpoint who can identify problems and who can find solutions, you’ll be able to better standardize the quality of your data.

IT department

Governing and industry standards bodies

Building good data management into your organization

Here are some best practices to improve your data management:

Outline your company’s goals

Define and share good data quality components

Ensure your data is secure

Make data accessible to your employees

In addition to these practices, an effective means of data management is to implement a secure data management platform that can both clean and maintain CRM data to help improve your business operations.

By opting for a standardized data management platform, your business can make sure that all collected data meets the following criteria:

• Data standards
  • Ambiguous data standards will result in low-quality data as each piece of information won’t be held to the same levels of scrutiny or quality control. That’s why it’s essential that each business outlines quality data components and shares its standards to ensure good quality data.
• Metadata management standards
  • Locating and accessing data is at the heart of good data management systems. By creating a metadata repository that uniformly names and defines each data point, your business won’t struggle with lost or misplaced data. In the CRM space, this can also be captured in the field-level setup.
• Data validation rules
  • By setting specific data validation rules, data that does not meet your business’ preferred format won’t be put into the system. Instead, an error will be flagged, so you can either fix, replace or remove the mistaken data.