Can you trust your organization’s data?

Every organization today is awash with data. Estimates put the amount of data the world creates annually in the zettabytes — that is, more than a billion terabytes, a.k.a. a ridiculously large number.

Some of the data that flows through each organization’s networks comes in through SaaS and web applications. Some comes directly from people’s data entry — think web forms — and people make mistakes. Some comes from machines, such as smart devices — and machines aren’t foolproof. And often the data passes through information systems coded by multiple developers — and even the best developers occasionally write code with bugs.

All of that adds up to a big challenge for anyone tasked with making sure that all of their organization’s data is trustworthy. But what does it mean to trust your data?

Data trust definition

Data trust simply means having confidence that your organization’s data is valid, complete, and of sufficient quality to produce analytics you can feel comfortable basing business decisions on.

How do you measure data trust? The Data Management Association of the UK defines six dimensions of data quality:  

  • Accuracy — The degree to which data correctly describes the real-world object or event being described
    Example: Do the statistics used to calculate a baseball player’s batting average reflect his actual number of hit and at-bats? 
  • Completeness — The proportion of data stored against the potential for 100%
    Example: In an address record, do you have all the address fields necessary to get a postal mailing to its destination? ZIP code? ZIP+4? Country name? Do you have address values for all records?
  • Consistency — The absence of difference, when comparing two or more representations of a thing against a definition
    Example: Does one table contain data characterized as belonging to a particular division, even though that division has been eliminated after a reorganization?
  • Timeliness — The degree to which data represent reality from the required point in time
    Example: In a field that represents company earnings, do you have access to the latest data? How quickly is that data made available to you?
  • Uniqueness — No entity instance (thing) is recorded more than once based upon how that thing is identified
    Example: When a system updates a record, can you be sure it isn’t creating a duplicate of the original record, but with more current information?
  • Validity or conformity — The degree to which data conforms to the syntax (format, type, or range) of its definition
    Example: A street address of 1000 Data Way is valid (though not necessarily accurate), while an address of 03H8 Data Way is not.

Other analysts would include factors such as reasonability, accessibility, and integrity. Whatever factors you include, the point is to be able to rely on your data to be useful across the enterprise.

The more highly you can rate your data across each of these dimensions, the more you can trust it.

Is it possible to have data that’s 100% trusted across all dimensions for all tables, records, and fields? You might have data that’s accurate but incomplete, or complete but not timely. Data teams must make their own assessments of the necessary level of data quality to qualify for data trust — and they should be able to certify that level of quality to data users, so they in turn can be confident using the data.

How to achieve trusted data

To get to trusted data, you need to implement and automate processes for auditing, assessing, and cleaning the data. The platform you use should bundle data integration, data integrity, and data governance features, ideally in a single integrated system. It should leverage the knowledge of line-of-business users to clean data where appropriate, and support sophisticated tools that let data engineers perform complex operations through an easy-to-use graphical interface. In short, with the right software, you can make it easier for everyone in your organization to trust their data.

Benefits of data trust

Trust is the key to making successful use of your data. When you have a way to ensure data trust, you can get the data you need to design exceptional customer experiences, improve operations, ensure compliance, and drive innovation.

Putting tools in place to ensure data trust yields many benefits. A data trust infrastructure can automate data quality checks and create reusable processes, saving data engineering time and increasing organizational productivity. With trustworthy data, everyone in the organization can be more confident that they’re making decisions based on a complete, accurate, and timely picture of the real world. When you make decisions using trusted data, you’re likely to make decisions that lead to better outcomes, which in turn leads to higher revenues. Trusted, accurate data also makes it easier for organizations to meet regulations for data privacy, such as GDPR and CCPA. Ultimately, clean, trusted data lets you respond better to your customers’ needs, and that drives improved customer satisfaction.

When you have trusted data available across an organization, you can more easily grow, adapt, and reach your business goals. When you trust your data, you can use it to make informed strategic business decisions with confidence.

Data trust solutions

Even though everyone relies on data to make decisions, getting data you can trust is often cumbersome, complicated, and time-consuming — if not impossible. Earlier we said that the solution for achieving trusted data was to employ a data quality infrastructure. Talend Data Fabric is the only cloud-native platform that brings together data integration, data integrity, and data governance capabilities in a single environment to simplify every aspect of working with data across your entire data landscape.

A unique feature of Talend Data Fabric is the Talend Trust Score™, an industry-first innovation that assesses the reliability of any data set. It gives you instant insight into how much you can trust your data. It shows at a glance the extent to which your data is

  • thorough — is your data clean, complete, and consistent across your systems?
  • transparent — is your data accessible and understandable?
  • timely — is your data readily available to the people who need it?
  • traceable — does your data tell you where it came from and how it has been used?
  • tested — has your data been rated and certified by other users?

With complete, clean, trusted data at your fingertips, you can make better decisions with confidence.

Do you have confidence in the quality of your organization’s data? Do you wonder how accurate, complete, and timely it is? Talend can help. Export a subset of your data and run it through the Talend Trust Assessor — a free tool that gives you feedback on the validity, completeness, and uniqueness of your data — or try it with our sample dataset.