Guide to Data Reliability

Data reliability explained and how to improve it

Posted by PipeRider · 9 mins read

Modern organizations have gained unprecedented access to quantitative and qualitative data. With all this information available, it’s become best practice for every team to make data-driven decisions. But there’s a problem.

You may be collecting a large amount of information within your data stack, but are you certain that these data sets are complete, accurate, and up-to-date? If not, these data sets might cost you a lot.

IBM estimated that the yearly cost of poor quality data, in the US alone, in 2016, is a whopping $3.1 trillion. In 2021, Gartner reported that every year, unreliable data costs organizations an average of $12.9 million. And it’s safe to say that the number has very likely increased as data-driven decision-making is adopted by every business imaginable.

That’s why ensuring your data is trustworthy by improving data reliability is very important.

What is Data Reliability?

Data reliability means that data is complete, accurate, and valid. It’s the foundation for building trust in your data across the organization. One of the main objectives of ensuring data reliability is building data trust, which is also used to maintain data security, data quality, and regulatory compliance.

Reliable data helps decision-makers take the guesswork out of the daily and strategic decision-making process to keep their organizations running. But if your data is unreliable, those same decisions become less accurate and can ultimately affect your organization.

Why Data Reliability

When unreliable data is used in making a key strategic decision, it can result in a mistake that damages an organization’s reputation, and bottom line, or even causes its future. Data reliability issues might not seem like a big deal at first glance, but they can snowball over time if left unchecked.

For example, you use customer data to develop targeted online ads or recommend products to your consumers. If the data you use isn’t accurate, then there’s a good chance that the advertising budget will be wasted on either poor results or zero return on investment.

The unsettling feeling when you are not sure if you can trust your data to make a decision can be highly stressful, ut there are actions you can take to improve your data reliability.

How to Improve Data Reliability

Like many other managerial tasks, the process to improve your data reliability follows a series of logical steps. There are eight action items that your organization can take to improve your data reliability:

  1. Assess Data Status
  2. Build Data Infrastructure
  3. Clean Existing Data
  4. Optimize Data Collection Processes
  5. Break Down All Data Silos
  6. Integrate Data Stack to Connect Data
  7. Organize Your Data
  8. Use Reports and Dashboards

Assess Data Status

Assessing your current data status is the first thing to do to improve data reliability. It helps you to get a general view of how your organization treats data. You should also employ data profiling. Data profiling is the process of examining and analyzing data. This helps you understand if your data is healthy. Assess your current situation to understand:

What are your data sources; How and where you have stored the data; How and where you have used the data; The criteria used to determine data reliability.

Build Data Infrastructure

Once you’ve assessed your current situation, you can start updating your data infrastructure. No matter what the original data sources are, you need a secure and easy-to-use data repository. You need to define how your data will be stored, formatted, and organized. There are several steps you can take to create a data infrastructure:

Refine your strategy. Build a data model. Choose your data repository type – data lake, data warehouse, or hybrid. Build an extract, transform, and load (ETL) process. Implement ongoing data governance.

Clean Existing Data

If you have data sets in place already, you should examine the existing data and remove data that is:

  • inaccurate;
  • incomplete;
  • duplicative;
  • outdated;
  • incorrectly formatted.

You should employ data profiling to analyze your data continually, so you can clean, and update data errors as soon as they are spotted.

Optimize Data Collection Processes

Start by analyzing internal processes for data input. Automate data entry wherever possible to minimize human errors. Make sure that all data entry follows your standardized formats and is accurate, complete, and valid.

Next, look at other data sources you obtain new data from. Make sure that their data formats follow your standardized format and remove inaccurate and unreliable data.

Break Down All Data Silos

Organizations collect data from different departments or locations. This is necessary due to operational requirements or structure setup. But this might create independent data silos that would affect data reliability.

Not only do silos make it difficult to find and share data across your organization, but they also often adhere to different standards of organization and quality.

To ensure the most reliable data is available to those who need it internally, you need to break down your organization’s data silos. You should employ a central data repository for all departments and locations to minimize potential damage to data quality.

Integrate Data Stack to Connect Data

Quite often, different departments or locations use various tools and platforms. If you can get everyone to use the same tool and platform, great. If not, you should connect data from these tools and platforms across your entire organization to have a unified view of all your data. Therefore, when a piece of data is updated in one location, it is automatically updated wherever else it is used.

Organize Your Data

Every organization has its unique way of organizing data to meet its unique needs. Organizing data makes it easier to locate specific data and speeds up your data retrieval process.

Typically, you will find labels, tags, groups, and other information stored in metadata. Depending on the type and use of your data, you may find data segmented by customer age, gender, geographic location, demographics, etc. No matter how you organize your data, make sure you understand the overall organization’s expectations and what it would like to achieve using the data.

Use Reports and Dashboards

Finally, make sure you are able to get insight from your data with reports and dashboards. For example, a data profile report can continue to alert you of data errors when it occurs. Other reports that track key metrics in a visual way with detailed analyses put peace of mind in you when it comes to making data-driven decisions.

Automate Your Data Reliability With PipeRider

It may feel overwhelming when you manage a large amount of data, but once you lay the groundwork and build the foundation, there are many tools you can use to make the journey easier. If you’re interested in learning more about your data, with the aim of improving your data reliability, PipeRider can help you.

PipeRider is an open-source, free, and easy-to-use data reliability tool with data profiling and data quality checks through assertions. It executes no-code data profiling and test assertions against your dataset with simple commands. It recommends assertions to save you time and renders your test results into a visual report in minutes. Using the data profiling report you can verify that the data meets your requirements, enabling you to trust your data and make better decisions. PipeRider embraces the modern data stack and connects anywhere on your data pipeline that uses a supported data source.

How to get started with data profiling for data quality

PipeRider is available now and supports many popular data sources. Just install PipeRider, connect to your data source, and in minutes you’ll have a data profile with data assertion functionality. Find out more at the following links: