As organizations look to reap the rewards of deploying a data analytics strategy, data quality is a top priority for many leaders. Prioritization, however, does not always ensure that the data your team works with is of the highest standard and accuracy. For leaders who want to create real adoption of data, this presents a major challenge, as inaccurate answers can erode trust and stall adoption in its tracks.
So whether you are dealing with customer data or marketing analytics, making sure that your data is reliable can greatly improve decision-making processes and help reduce risks, while encouraging users to utilize data at greater scale. That means ensuring data quality metrics should be a top KPI for data analysts, scientists, and engineers. Without question, having an understanding of the most important data quality metrics can be hugely beneficial in ensuring that your team's work keeps up with the latest industry standards, while helping you create a data-driven organization. In this post, we'll discuss 8 essential metrics any organization should be aware of when assessing their data quality.
In analytics, there’s always a right answer. How much revenue did we generate last quarter has a singular, correct response. Accuracy is a critical aspect of data quality, as it plays a major role in data-driven decision making processes. When the data is inaccurate, decisions based on that data are likely to be wrong or flawed. Inaccurate data can have serious implications for businesses, ranging from lost revenues and wasted resources to customer dissatisfaction and negative publicity. This is especially true when business users leverage self-service BI; inaccurate or wrong data can impact multiple decisions on the frontlines, while hindering adoption for data projects at large.
To ensure accuracy in their data, organizations should employ several methods. These include data validation and verification. Data validation involves checking whether all incoming information meets certain criteria before being entered into the system. For example, if an organization requires that only numbers between 0 and 1,000 be inputted into its database, then any values outside this range would be rejected by the validation process. Verification ensures that previously recorded values match up with their sources. This is done by comparing entries to sources and checking for discrepancies between them.
In addition, organizations should also implement data monitoring systems that provide real-time alerts when inaccurate values are detected. This allows them to address any potential issues before they become serious problems. Finally, organizations should have a backup system in place so that if data loss or corruption occurs, it can be recovered quickly and accurately.
Completeness is another important aspect of data quality. It refers to whether or not the data includes all relevant information that is necessary for making decisions. If data is incomplete, it can lead to mistaken conclusions and a wrong course of action.
Examples of incomplete data may include missing values in customer records, incorrect or outdated contact information, or a lack of information about certain products or services. Such cases can cause businesses to miss out on potential opportunities and suffer from higher costs due to manual processes such as filling in the blanks manually.
To ensure completeness in their data, organizations should use several methods including data profiling and data enrichment. Data profiling involves examining existing datasets for patterns and anomalies so that any missing information can be identified. Data enrichment is the process of supplementing datasets with additional information from external sources to fill in any gaps. Often, data completeness can also be advanced by adopting a cloud data platform, which makes it easy to bring various data sources together to create a single source of truth, capturing all the relevant data in one central place. Each of these methods can help organizations identify incomplete data and take steps to ensure its completeness.
Consistency is another important factor of data quality. It refers to the uniformity and accuracy of data across different systems, applications, or databases. When data is consistent, it allows organizations to have a unified view of their information which in turn enables them to make better decisions.
However, inconsistent data can cause significant problems for businesses. This can include discrepancies between two datasets that prevent companies from getting an accurate picture of their operations, leading to wrong interpretations and faulty conclusions about their performance. Furthermore, inconsistency can also lead to customer dissatisfaction due to incorrect orders or delayed deliveries caused by mismatched information.
Organizations should employ several methods such as data cleansing and integration to help drive consistency in their data. Data cleansing involves scrubbing datasets of any unnecessary or inaccurate information, while data integration involves combining multiple datasets from different sources into a single unified dataset. Here, again, having the right platform as the foundation for your modern data stack can help organizations to ensure that their databases are up-to-date and accurate, allowing them to make more informed decisions.
Timeliness is another important component of data quality. It refers to how quickly and accurately data is collected and made available for use. Whether dealing with supply chains or pushing for inventory optimization, the world around us is constantly changing, and if your data doesn’t change in parallel, it's easy to make erroneous decisions based on an out of date picture of your business. When data is timely, it allows organizations to make decisions more efficiently, as they can access the most up-to-date information without any delays.
On the other hand, untimely data can lead to missed opportunities due to inaccurate or outdated information. It can also cause errors in decision-making processes as businesses may be operating on incorrect assumptions based on old information.
To ensure timeliness of their data, organizations should employ several methods such as real-time data integration and synchronization, which often requires building robust data pipelines. Real-time data integration involves combining datasets from different sources into a single unified dataset, while data synchronization ensures that all datasets are updated in a timely manner. Both of these methods can help organizations ensure that their databases are up-to-date and accurate, allowing them to make more informed decisions.
Validity is an important consideration in determining the quality of data. It refers to whether or not the data collected reflects reality, and if it can be used to draw accurate conclusions. When data is valid, it allows organizations to confidently use their data for decision-making processes.
Invalid data however can cause businesses to make assumptions that, while true based on the data, do not in fact reflect the real world. This can cause several issues for businesses, such as wrong interpretations due to inaccurate information, customer dissatisfaction due to incorrect orders, or failed deliveries caused by bad data.
Valid data is non negotiable for businesses, but they do have several methods such as data profiling and cleansing that can help them deliver valid, useful data. Data profiling involves analyzing datasets for any errors, inconsistencies, or anomalies that may affect its accuracy, while data cleansing involves scrubbing datasets of any unnecessary and inaccurate information. Both of these methods can help organizations to ensure that their data is valid, allowing them to make more informed decisions.
Relevance refers to the ability of data to be useful in decision-making processes, and how applicable it is to a given context. When data is relevant, it allows organizations to make decisions quickly and accurately, helping them achieve better outcomes for their business.
Irrelevant data on the other hand can lead to missed opportunities or incorrect assumptions because decisions are made on superfluous data that doesn’t directly matter in regards to the decision at hand. This can result in wrong decisions being made and resources being wasted, leading to losses for the company overall.
By using data mapping and robust data modeling, organizations can ensure their data is relevant. Data mapping involves creating a visual representation of how different datasets are related, while data modeling involves analyzing datasets for patterns and trends to gain insights into how the data can be used. Both of these methods can help organizations to ensure that their data is relevant, allowing them to make more informed decisions.
While data may be an objective source of truth, it is always in the context of people. Consensus is an important consideration in determining the quality of data because it demonstrates whether or not all the stakeholders involved in a decision-making process, such as employees and customers, agree on the same information before any decisions are made. When consensus is present, it allows organizations to make decisions with confidence knowing that everyone has access to the same accurate information. Getting this right, by bringing stakeholders in early into the development process, is critical for any data initiatives.
When this is done late, or after data teams have spent hours building dashboards, disagreements over data however can lead to miscommunications and wrong interpretations, and frustration. It’s why traditional dead dashboards continually fail to deliver transformation business results.
To ensure consensus among stakeholders, organizations should employ several methods such as data governance and stewardship. Data governance involves creating policies and procedures regarding how data is to be managed and used, while data stewardship involves assigning someone the responsibility of overseeing and administering the organization’s data. Both of these methods can help organizations to ensure that all their stakeholders agree with each other, allowing them to make more informed decisions.
Accessibility is an important, but often overlooked, component of data quality. It refers to how easy it is to access the data, and how quickly organizations can gain insights from it. When data is accessible to everyone, it allows organizations to make decisions faster and with greater accuracy, helping them achieve better outcomes for their business.
Inaccessible data on the other hand can lead to missed opportunities or incorrect assumptions due to delay in obtaining the necessary information. This can result in wrong decisions being made and resources being wasted, leading to losses for the company overall.
To ensure accessibility of their data, organizations should employ several methods such as data cataloging and security. Data cataloging involves creating a centralized database of all available datasets within an organization, while data security involves ensuring that the data remains safe and confidential. Both of these methods can help organizations to ensure that their data is readily available, allowing them to make more informed decisions. This is even more powerful when paired with self-service analytics, so business users not only know what data is accessible, but can access it through simple, natural language.
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Data can be a powerful tool for businesses of any size and across any industry, allowing them to improve customer satisfaction, boost employee productivity, and increase profits. It's essential that businesses fully understand the eight key metrics for data quality to make informed decisions and capitalize on their data collection and data management. Doing so, however, requires not just establishing data quality, but making this quality data available to business decision makers at scale. Investing in a quality Business Intelligence platform such as ThoughtSpot could be just what your business needs- it blends incredible power, usability, and flexibility into one product that fits the needs of any company. Its modern artificial intelligence enables users to ask questions in plain English- no more complicated queries! Start investing in your data today by signing up for a ThoughtSpot free trial to see firsthand how great decisions start with good data quality.