BI lesson: a comparison between data reliability and data validity

Data has become central to the success of any modern business, regardless of domain or industry. Despite this, data generated by electronic communication, connected devices, and decision-makers and managers.

Recent research has revealed that only 28% of North American businesses have well-established big data projects in place. This is due to a lack of understanding of how data is collected, organized, and used. Business leaders can sometimes confuse data reliability with data validity. Despite overlapping with each other in certain areas, each of these metrics has a valid place in business and research. 

The difference between reliability and validity

Data validity is a subset and precondition for data reliability, referring to the practice of correctly storing and formatting data. Data reliability, on the other hand, refers to the accuracy and completeness of the data that is the basis for extracting insight. In other words, it is impossible to achieve data reliability without data validity. Here is a quick breakdown of what each of these metrics reveals to business leaders and data teams.

What do data reliability assessments reveal?

When teams evaluate data reliability, they are testing if a particular data set consistently produces the same results. This allows businesses and researchers to be sure that the outcomes of data analysis use a consistent foundation based on reliable data.

To build the right foundation, businesses and research teams must ensure that each piece of data is consistently stored in the appropriate format and in the appropriate location. This is particularly challenging for companies that regularly move large amounts of data or are undergoing cloud migration. These assessments are usually done over the course of multiple tests and are typically done at regular intervals. 

What do data validity assessments reveal?

Data validity assessments assure data teams that the outcomes produced by a data set are truly representative of the reality on the ground. There are a variety of established data theories and assessment methods that should be referred to when evaluating data validity. The theories and methods that are used to measure data validity depend on the type of data that each team wishes to evaluate. 

Once data validity is achieved, data teams can expand their assessment to include data reliability tests. Here are some ways business and research teams can overcome data challenges and ensure high data reliability across their entire organization.

Best practices for ensuring high levels of data reliability 

1. Create clearly defined data foundations from the data collection stage

Modern businesses and organizations generate a staggering amount of data and this amount is increasing exponentially. Individuals alone can generate 2.3 zettabytes of data each day. This can make it difficult for organizations to identify and build rules for collecting data.

Business leaders and data leaders must collaborate to discuss and identify why data is being collected and what are the pieces of data that are most important to the organization. With connected devices growing in popularity across the world, organizations can now easily pinpoint the areas from which data should be collected and establish systems to ensure that information gets captured and stored appropriately. 

2. Store and manage data effectively by improving data organization

Once data is collected, data teams must devise a method for this data to be organized. This is a critical step in ensuring that data sets remain reliable. In order for data sets to be reliable, each member of the team has to add and manipulate data using the same format every time. It is extremely easy for team members who are not familiar with the right format to sometimes use variations—such as using U.S. instead of U.S.A. to identify countries. 

3. Regularly evaluate and minimize the impact of dirty data on research outcomes

Despite an organization’s best efforts, errors can find their way into its data sets. This can happen due to a wide variety of reasons from human error, storage failure, and incomplete data sets. These mistakes can be minute and still skew the outcomes of any data analysis, especially if unaddressed. This means that data teams must proactively conduct checks for inaccuracies and errors of any kind in their data sets on a regular basis. 

4. Build systems that allow data to flow seamlessly across business silos

One of the barriers that companies and organizations face to achieving data reliability, and by extension data observability, is the existence of information silos. Data silos refer to any barrier that prevents information from crossing the gap between operational teams. With gig work becoming more popular and teams being spread across the globe, these gaps become even wider. Business and research teams must ensure that each piece of data that is collected by them is integrated into a larger data plan that is cohesive and coherent. 

5. Build a data-driven culture that spans the entire organization

While data teams are embedded in the data collection and management processes, the responsibility for data management actually spans the entire organization. The volume and specificity of data sets that are important for full observability are often hidden away in small operational teams. This is why business leaders must develop a culture that is built around understanding the benefits of data collection and management and ensuring that each operational team is meaningfully engaging with those processes. 

Every organization in the world has access to data in some form or another but few make full use of this information. As data analytics becomes an increasingly popular way to differentiate from competitors, having an effective, valid, and reliable data set can help give companies and research teams the insight they need to help them become data-driven industry leaders.

Author: Loretta Jones

Source: Datafloq