Data Quality

What is Data Quality?

Data quality describes a data set’s fitness to serve an intended purpose or use case. The six characteristics that measure data quality are accuracy, completeness, timeliness, uniqueness, consistency, and validity.

Why is Data Quality Important?

Business-to-business (B2B) organizations are prioritizing becoming digital-first. To succeed at this, they must rely on data to inform their sales and marketing strategies. Getting the data right is a crucial first step to see digital-first initiatives succeed.

Additionally, data quality improves customer experience and supports customer responsiveness. Service reps and salespeople on the front line fear being unable to be customer-responsive. However, this challenge can be offset by improved data quality and eliminating time-consuming, non-value-added activities caused by incorrect data.

High-quality data can lend businesses a competitive edge by reducing risk and increasing efficiency and productivity, which leads to better-informed decision-making, more effective campaigns, better audience targeting, and better customer experiences. In addition, companies that commit to data quality are better able to use their knowledge of customers, prospects, and vendors to influence top- and bottom-line results.

On the other hand, poor-quality data undermines digital initiatives. It can weaken an organization’s competitive standing and create customer distrust.  

Why Quality Data is a Business Asset

Second only to its people, data may be an organization’s most valuable asset. But data will only be valuable and useful if it’s high quality. Bad data gets in the way and can be a liability.

Setting a standard for high-quality data and improving your data’s quality should be business priorities. These efforts can make a difference in sales and marketing performance, especially during challenging times. Better data leads to better decision-making, which leads to better results. 

Gartner found that poor data quality may cost organizations an average of $12.9 million per year due to operational inefficiencies or costly mistakes resulting from the use of inaccurate or out-of-date data.  

How Data Quality Supports Sales and Marketing Efforts

Customer or account intelligence is indispensable to marketing and sales success. Thus, data quality is inextricably linked with sales and marketing success. Marketing automation driven by quality data has raised customers’ expectations for relevant and timely information and redefined how marketing and sales can work together to increase revenue and reduce cost. 

Data-driven marketers play a strategic role in maximizing the marketing spend’s return on investment (ROI) – profiling and targeting customers, nurturing, and handing “buy ready” leads to the right salespeople. In addition, a 360-degree view of a customer across divisions, geographies, and corporate family tree relationships allows sales to more effectively manage their accounts and realize their full potential to upsell, cross-sell, and coordinate appropriate service levels.  

What Does Bad Data Quality Cost?

Bad data quality can have significant business consequences. A 2016 IBM study found that poor-quality data costs the U.S. economy $3.1 trillion annually because of lower productivity and the higher cost of maintaining that data.  
 
Poor-quality data can lead to bad decision-making, inaccurate analytics, and misguided business strategies. The impact of poor-quality data might include incurring additional expenses, lost sales opportunities, fines for improper compliance, and loss of customer trust. The cost associated with bad data only worsens as tech platforms and their associated data proliferate. 

Consultant Thomas Redman wrote that the cost of bad data is an astonishing 15%-–25% of revenue for most companies. (MIT Sloan Management Review, 2017.) “These costs come as people accommodate bad data by correcting errors, seeking confirmation in other sources, and dealing with the inevitable mistakes that follow,” he wrote.  

Benefits of Data Quality and Data Quality Management

A strategic investment in data quality pays off in multiple use cases across the enterprise. First, good-quality data can be used, processed, and analysed more easily, offering insights to help an organization make better decisions. Second, good-quality data means organizations can extract more value from their data. Third, good-quality data is trusted data, and trusted data is used more frequently by the teams that are tasked with using it.

Data quality management strives to improve organizational efficiency and productivity while reducing the risks and costs associated with poor-quality data. Data quality management lets data management teams focus on more productive tasks rather than spending their time cleaning up data sets. They can spend their valuable time helping business users and data analysts take advantage of the organization’s data assets in practice while proactively promoting data quality best practices to minimize data errors in the first place.  

What is Considered Good Quality Data?

Although definitions vary from organization to organization and use case to use case, generally speaking, good- or high-quality data is data deemed fit for its intended purpose(s), whether in operations, decision-making, or planning. Conversely, incomplete, duplicate, out-of-date, and incorrect data is considered low-quality data. 

Good or bad, data makes an impact. Therefore, you need to trust your data and be confident that the decisions you make based on that data are the right decisions. 

What is Data Hygiene, and Why is it Important?

Data hygiene is the process of cleansing data to ensure accuracy and organization — error-free data that is consistent and accurate. In addition, data cleansing prevents the issues caused by dirty data, such as duplicate or outdated information and incomplete records, that businesses struggle with. Therefore, data cleansing is an important part of data quality in order to use the data to its fullest capacity.

Who is Responsible for Data Quality?

Ultimately, data quality is ultimately the responsibility of everyone in the organization who works with data. 

Data quality is a business issue, not an IT issue. Companies that are leaders in data quality place the responsibility of data quality sponsorship at the C-level. The senior champion and a cross-functional group of line and IT experts create the most effective team to sell, frame, and drive a data quality initiative. Customer-facing line functions guarantee that data and process decisions support business delivery. Finally, IT, and often Operations, plays a consultative role in recommending options to help the implementation team meet its data quality goals. Broad representation ensures that improvements address the biggest ROI opportunities across the organization, including credit management, vendor management, sales, marketing, and business development.

Instituting a Data Quality Framework

Using a data quality framework will help you understand how your data should perform and identify opportunities for data transformation. 

Through the continuous cycle depicted above, we can develop new data policies, new data standards, and new data guidelines.

Data policies are directional statements and requirements that aim to protect corporate values, assets, and intelligence. Data policies are the foundation for the related standards, processes, procedures, and guidelines.

Data standards are practices and benchmarks used to comply with the requirements outlined in the policies. A standard should always be a policy derivation, as it is the second step in an organization’s policy propagation process.

Data guidelines are hints, tips, and best practices derived from policies and standards. Guidelines are optional, but they typically document well-known parameters, processes, and procedures under which policies and standards are successfully implemented.  

Data Governance Versus Data Cleansing

Data governance helps build consensus for agreed-upon definitions and standards of data quality. Because opinions of data quality can differ widely within an organization, based on roles and use cases, a data governance strategy establishes and maintains data quality, most critically at the point of entry. Data cleansing is the practice of meeting that standardization to ensure data quality.