Why data quality is important and how to track & measure results
Any company engaging in data-driven business practices should prioritize achieving and maintaining high-quality data. High-quality data offers an accurate and complete picture of key consumer and operational metrics and reduces your risk of a costly data security breach.
Learn the importance of high-quality data and how you can track and measure your data to understand your customers and avoid security risks.
What is Data Quality?
Data quality is a term used in data analytics to describe the state of a dataset. Data quality looks at objective elements like consistency and accuracy. It also looks at subjective measures like how well a data set suits its intended purpose.
A company engaging in data-driven marketing can leverage data quality software to inform its spending decisions, streamline its operations, and strategize for future growth. The key is ensuring the data you collect is accurate and aligned with your company’s specific goals.
What are the 4 Categories of Data Quality?
High-quality data has four characteristics: accuracy, completeness, consistency, and relevance. A dataset is likely of high quality if it meets the criteria of all four data quality dimensions.
- Accuracy: the data is exact and error-free, with no outdated information, typos, or redundancies.
- Completeness: all data entries are complete, with no fields containing missing or incomplete values.
- Consistency: there are no contradictions in your data. Consistency looks at metrics like range, standard deviation, and variance to verify consistency across datasets.
- Relevance: you are collecting the data for a practical reason. Gathering irrelevant information is a waste of time and money.
Why is Data Quality Important?
Making business decisions using low-quality data can be costly, as incorrect or incomplete data can give you a misleading picture of your company’s performance. Without high-quality data, you are more likely to make poor marketing investments and employ misguided growth strategies, which can ultimately sink your business.
Without a strategy informed by high-quality data, you may also miss opportunities to expand your customer base or lose trust with your current customers. When working with inconsistent, low-quality data, it’s far more challenging to identify what your customers want and need because the data you’ve collected about them may not match their preferences.
On the other hand, working with high-quality data offers valuable insights into consumer behavior which you can capitalize on with a tailored market strategy.
How is Data Quality Measured?
Companies use a range of metrics to measure and track the quality of their data. These metrics help a company understand the quality of the data they are currently gathering and inform future data-collection methods. Many companies leverage multiple metrics to assess their data quality management strategy holistically.
The following are commonly-used metrics examples and questions they can help you answer:
- Data-to-Errors Ratio
Do you have a high number of errors relative to the overall size of your dataset? Datasets with missing, redundant, or incomplete entries can yield highly inaccurate information, but you can use your data-to-errors ratio to gain insights into your data quality. If you see the same or lower number of errors for datasets of the same or greater size, your data quality is increasing.
- Data Transformation Error Rate
How many errors occur when you convert your information to a different format? If you have issues with data transformation, it’s often a sign that your data is not high quality. You can measure data quality by identifying the number of failed conversions and track the effectiveness of any interventions by seeing if they reduce the error rate. Your data is likely flawed if you continue seeing a high data transformation error rate.
- Cost of Data Storage
Does it cost more to store the same amount of data? Many companies pay to store their data within the networking infrastructure of a data storage provider, such as iCloud, DropBox, or OneDrive. If you start seeing higher costs to store the same amount of data, you may need to prioritize increasing the quality of your data.
- Dark Data
Dark data, data that does not offer any insights into decision-making, is unusable and can lead to compliance and security risks in your data quality framework unless addressed. If you measure a significant amount of dark data in your dataset, that data quality is probably low.
Certain metrics may be more helpful depending on how you plan to use your data. For example, if you are working with a data storage provider, you can look at your data storage cost metric to determine if it’s more cost-efficient to build a separate marketing data warehouse (MDW).
How Do You Maintain Data Quality?
You can maintain data quality by using data quality tools such as data cleansing, data auditing, and data migration.
- Data Cleansing
Data cleansing involves removing or fixing incorrect, corrupted, duplicated, or incomplete data from a dataset. On average, data scientists spend about 60% of their time cleaning and organizing collected data. Clean data will meet the four characteristics of data quality: accurate, complete, consistent, and relevant.
- Data Auditing
A company can audit a dataset to identify systemic data issues and security gaps. Internal data audits can uncover several data security and compliance issues, including:
- Incomplete or inaccurate data sets
- Barriers to accessing data
- Insufficient depth or breadth of a dataset
- Security gaps
- Siloed data
One simple task of a data quality audit is to provide visibility into a company’s data usage, location, and security. This visibility helps organizations adhere to regulations set by the government, industry, and other corporate entities.
The United States has a mix of laws governing data privacy and security, known by acronyms like HIPAA, FCRA, FERPA, and GLBA, among others. Companies that fail to achieve regulatory compliance can face high fines. The minimum penalty for willfully violating HIPAA rules is $50,000; more significant violations lead to higher fees and possible jail time.
Many companies store their data in secure; protected environments called Data Clean Rooms to reduce data security risks.
- Data Migration
Data migration involves moving your data from one location, format, or application to another. Organizations generally migrate data when introducing a new location or system for the data or when converting an 0n-premise data infrastructure to a cloud-based storage system.
Though data migration can ultimately result in more secure, compliant, high-quality data, the migration process can put your data quality at risk. These risks include:
- Loss of data during migration
- Semantics risk (data migrates into different columns or fields)
- Data corruption or crashes
- Orchestration risk (data migrates in incorrect order)
- Application stability risk
Before any data migration, a business should have a thorough action plan that includes strategies for mitigating risk during migration, like thorough data migration testing.
Ensure Quality Data with Measured
Media and marketing optimization are the most exciting applications of high-quality data. Measured’s award-winning media optimization platform uses incrementality measurement to reveal the tangible contribution of every marketing dollar a company spends on promoting its products and brand.
Using Data Driven Incrementality (DDI) Measured offers its clients accurate, full-portfolio measurements within two weeks. Book a demo today to learn more about how our integrations can help ensure your company achieves and capitalize on high-quality data.