Data is becoming a core component of every business. As a result, the quality of the data being gathered, stored, and consumed is becoming increasingly important. We have all seen the impact that poor data quality can have on a business and are familiar with the common effects of bad data, but what does it really cost? Gartner’s recent research indicates that organizations believe that poor data quality is responsible for an average of $15 million in losses each year. While those of us who deal with data daily know that bad data is costly, this number is an eye-opener.
Download the 2020 Data Assessment Framework to understand core aspects of an effective data management strategy, assess current state against emerging best practices, and identify areas of opportunity to improve your data strategy.
The reason poor data quality is so costly to organizations is twofold. Poor data quality impacts both employee productivity and can impact a company’s reputation.
Because decision-makers, managers, and workers accommodate poor data quality in their daily work, this is a common drag on productivity. According to Forrester, nearly one-third of business analysts spend over 40 percent of their time vetting and validating their analytics data before it is analyzed and used for strategic decision making.
Poor data isn’t just impacting your analyst’s productivity. It is affecting your Sales and Marketing teams as well. Zoominfo reports that 94% of businesses suspect that their customer data is inaccurate. In B2B sales, that poor data quality is responsible for wasting over 27% of a rep’s time. That is 546 hours per year, per rep wasted due to poor data quality.
If you think that is bad news, poor data quality can also negatively impact a company’s reputation. Organizations make (often incorrect) assumptions about the quality and state of their data. As a result, data quality in their business continues to go unmanaged. With this mismanagement of data quality, they continue to experience operational inefficiencies, excessive costs, compliance, and customer satisfaction issues as a result.
The impact on customer satisfaction is what undermines an organization’s reputation. Frustrated customers share their negative experiences, and even employees can question the underlying data when inconsistencies are not addressed. Employees may ask a customer to validate a product, service, or customer data during an interaction. This results in increased customer support times and eroding trust.
What Is Data Quality?
High-quality data is the foundation of the wisdom hierarchy. When that data is in context, you get information. When that information is actionable, you get knowledge. When you can apply that knowledge, you gain wisdom and drive positive business outcomes. When there is a breakdown in any of these elements, risky business outcomes are the result.
Data is considered high quality when it is fit for the intended purpose of use, and correctly represents the construct that the data describes. When this is the case, the data-to-information-to-knowledge-to-wisdom transformation can occur.
What Does Bad Data Look Like?
Before you can resolve data quality problems for your organization, you need to assess your existing data. To access your data thoroughly, you will need to communicate cross-functionally and across seniority levels to understand and eliminate information silos. There are five key areas that need to be reviewed when you are assessing your organization’s data quality:
- Data Accuracy: How well does the data reflect the real-world person or thing that is identified by it?
- Data Consistency: How well does the data align with a preconceived pattern?
- Data Completeness: Is the percentage of critical data items completed and within the benchmark range?
- Data Uniqueness: Are there cases of duplicate data records?
- Timeliness: How much of an impact do date and time have on the data?
How Do You Improve Data Quality?
The most important step you can take to improve your data quality is to get started. Business data sets will consistently be growing as more prospects come on board, and new markets are discovered. This means there will never be a great time to tackle this critical issue.
When you begin the data quality improvement process, it may feel daunting. However, there are 7 core components of an effective data strategy.
The 7 Core Components of a Data Quality Strategy:
- Develop and maintain a documented data strategy that is in alignment with the segment strategies and enables business objectives
- Develop and maintain a documented data acquisition process, an approved list of vendors and a systematic way to purchase and update data feeds
- Develop and maintain a systematic enrichment strategy that is frequent, utilizes effective tools and advanced analytical techniques
- Develop and maintain a documented governance process that supports and enables record completeness, data accuracy, data usability, regulation compliance, and data deduplication
- Develop and maintain a procedure for supplying business teams with actionable, targeted and high-quality data
- Ability to measure and track data quality and usage metrics and disseminate throughout the organization
- Enable through technology and utilization of effective tools that enable critical operations processes, data quality, data warehousing, and usability, including the use of predictive and other advanced analytical practices
To begin your data quality improvement journey, and start driving better business outcomes today, start by assessing yourself against these core components. You can start by downloading our 2020 Data Assessment Framework and identify the key areas you need to improve. Then, take the SBI Revenue Growth Diagnostic to assess the rest of your organization and see how other functions may be contributing to the problem. By understanding these components, you will be set for success and better business outcomes.