Every leader knows that they need to build their business on data if they want to thrive in the decade of data. Simply having the right tech stack and analytics program in place, however, isn’t enough. You, and your entire organization, need to be able to trust the data you’re using to guide your company’s decisions.
There are many factors that shape this trust. Key among them is data reliability. Whether you’re an executive or frontline decision maker, if you’re using data to make a decision, you want to know you can trust the insight that drove that decision. And that means you need reliable data.
Creating data reliability is easier said than done. Read on to learn what data reliability is, how to ensure it, and how to create business impact with reliable data.
What is data reliability?
Data reliability, sometimes referred to as data observability, is the degree to which data, and the insights gleaned from it can be trusted and used for effective decision-making. Data reliability has two critical elements: accuracy and consistency. Accuracy refers to the degree to which the data reflects the reality of any given topic. This helps ensure data, and the decisions based on them, can impact the real world in the manner desired. Consistency refers to how similar measurements are taken in different circumstances. In doing so, this helps control for bias in data so that differences are due to real changes or anomalies, not variations in how, when, or what data was collected.
Data reliability is essential for creating insights from business analytics that lead to effective decisions and outcomes on a range of topics, from customer engagement strategies and product development cycles to marketing tactics and more. When reliable data is combined with the right kind of business intelligence, businesses can make informed decisions that result in improved operations and better customer experiences — leading to greater success overall. Without reliability, it’s impossible to know if the conclusions drawn from the analysis are valid or due to issues in the data itself. There’s no one size fits all approach to data reliability, but in general, the best data reliability initiatives use quality control processes and checks throughout the collection and ELT or ETL processes. This could include double-checking inputs, using automated algorithms to detect suspicious or anomalous data points, applying statistical methodologies, and cross-checking with multiple sources. And it’s not a one and done program. As part of these initiatives, regularly auditing data sets is critical to ensuring ongoing reliability.
Ultimately, the key to data reliability lies in pairing a thorough understanding of the data collection process with an effective system for quality controlling data you’re collecting and using, whether that’s proprietary data or third party data. By doing this, organizations can ensure that their decision-making is based on reliable information.
How to make sure your data is reliable
Recognizing the importance of data reliability is the easy part. Actually implementing these processes is where the challenge comes into play. While there are nuances depending on the type of data you’re using, the use case it’s being leveraged for, and more, there are several common ways to ensure data is reliable.
Record lineage
Keeping an accurate record of all data collected, including when it was collected, the source it was collected from, and more, Many companies building a modern data stack, whether by an analytics engineer or data engineer, don’t want to stop tracing this lineage at collection. Instead, the best analytics engineers track any changes made along the way as they iterate with their business counterparts to drive value.
See how ThoughtSpot and dbt make it easy to launch self-service analytics, build data models, all while tracing data lineages
Quality assurance
Quality assurance is a process where data is assessed for accuracy against known standards or other reliable sources of information. This can be done both internally within the organization producing the data, or externally by a third party. Quality assurance ensures that the data is correct and can help identify any errors or inconsistencies in the data.
Data governance
Establishing a data governance framework or process is essential for ensuring that the data collected is reliable and properly managed. A good data governance process will define roles and responsibilities, as well as define rules and guidelines related to the collection, storage, and use of data.
Replication
Getting to the right answer once isn’t enough. Data reliability requires replication. The process of performing multiple studies or analyses on the same dataset helps to ensure that results are reliable since replication allows you to compare different interpretations of the same data. For example, if a study was conducted using a survey from one population, it can be replicated using the same survey in another population to see if the results are consistent.
Verification
Verifying data involves checking for accuracy against other reliable sources of information such as official records or databases. This helps to ensure that the data is accurate and up-to-date.
Using these methods can help to ensure that your data is reliable, allowing you to make informed decisions based on accurate information. Taking the time to properly check your data will pay off in the long run, as it will give you confidence in your results and ensure that they are not misleading or potentially damaging.
Data reliability vs data validity
Data reliability and data validity are two interlinked, albeit different concepts. Data reliability refers to the consistency of a set of measurements or results; in other words, whether the same result would be obtained if the measurement was repeated multiple times. Data validity, on the other hand, is concerned with the extent to which data measures what it is intended to measure.
For example, if a researcher was measuring the temperature of a room with a thermometer, data reliability would refer to whether similar readings were obtained when the thermometer was used multiple times in the same environment. Data validity would refer to whether the thermometer measures temperature accurately, or if it was measuring something else.
The two concepts can also be combined when assessing the quality of a set of data. This means considering both reliability and validity together to determine whether the data is suitable for use in a particular study or research project. A researcher should consider all aspects of their data before making conclusions or decisions based on that data. Doing so will ensure that the results are reliable and valid, and will provide meaningful insights into the research topic.
It is important to remember that data reliability does not necessarily correlate with data validity. Data can be reliable, but not valid, containing errors or inaccuracies that will lead to faulty decision-making.
One important way to ensure both data reliability and data validity is exposing data to domain experts and business users, especially in small iterative phases. By bringing in these business experts, who intimately know your space or use case, and arming them with self service analytics, they can interrogate the data and identify where there are gaps, where the data may clash with their knowledge or expertise, or where additional data could help tell a more complete picture.
Find and trust insights from your data
Data reliability is important for data-driven organizations because it ensures that the data being used is accurate and can be trusted. There are many factors that contribute to data reliability, including data quality, governance, and security. Data-driven organizations should consider all of these factors when ensuring the reliability of their data.
While you’re building data reliability programs and frameworks, ThoughtSpot’s AI-Powered Analytics can help bring your business users into the process and expose gaps or issues to help build long term trust in your organization's data. Once your data is reliable, you can also empower users of all types to turn this data into insights, and more importantly, into actions. Sign up for a free trial today and see how we can help make your data work harder for you.
