Quality data is key to making accurate, informed decisions. And while all data has some level of “quality,” a variety of characteristics and factors determines the degree of data quality (high-quality versus low-quality). Furthermore, different data quality characteristics will likely be more important to various stakeholders across the organization.
Data quality characteristics and dimensions include:
Data quality challenges include:
Privacy and protection laws
The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which gives people the right to access their personal data, are substantially increasing public demand for accurate customer records. Organizations must be able to locate the totality of an individual’s information almost instantly and without missing even a fraction of the collected data because of inaccurate or inconsistent data.
Data governance practices
Data governance is a data management system that adheres to an internal set of standards and policies for the collection, storage, and sharing of information. By ensuring that all data is consistent, trustworthy, and free from misuse within every company department, managers can guarantee compliance with important regulations and reduce the risk of the business being fined.
Artificial Intelligence (AI) and Machine Learning (ML)
As more companies implement Artificial Intelligence and Machine Learning applications to their business intelligence strategies, data users may find it increasingly difficult to keep up with new surges of Big Data. Because these real-time data streaming platforms channel vast quantities of new information continuously, there are now even more opportunities for mistakes and data quality inaccuracies.
Completeness is defined as a measure of the percentage of data that is missing within a dataset. For products or services, the completeness of data is crucial in helping potential customers compare, contrast, and choose between different sales items. For instance, if a product description does not include an estimated delivery date (when all the other product descriptions do), then that “data” is incomplete.
Consistency of data is most often associated with analytics. It ensures that the source of the information collection is capturing the correct data based on the unique objectives of the department or company.
Timeliness measures how up-to-date or antiquated the data is at any given moment. For example, if you have information on your customers from 2008, and it is now 2021, then there would be an issue with the timeliness as well as the completeness of the data.
Uniqueness is a data quality characteristic most often associated with customer profiles. A single record can be all that separates your company from winning an e-commerce sale and beating the competition.
Greater accuracy in compiling unique customer information, including each customer’s associated performance analytics related to individual company products and marketing campaigns, is often the cornerstone of long-term profitability and success.
Integrity of data refers to the level at which the information is reliable and trustworthy. Is the data true and factual? For example, if your database has an email address assigned to a specific customer, and it turns out that the customer actually deleted that account years ago, then there would be an issue with data integrity as well as timeliness.
The data you collect should also be useful for the campaigns and initiatives you plan to use it for. Even if the information you collect has all the other characteristics of quality data, if it’s not relevant to your goals, it’s not useful to you. It’s important to set goals for your data collection so that you know what kind of data to collect.