Data Quality: 4 Proven Methods for Evaluating the Health of your Data

Source

Inferior data quality leads to underwhelming business outcomes. What’s not so straightforward is identifying whether the data available is actually accurate and trustworthy. In this article, we will cover some key metrics and processes you can use to enhance your data quality, irrespective of where your business lies on the data maturity curve.

Today, users produce more data across multiple platforms than ever before. Now, to translate this information into data-driven decision making and customized experiences, businesses need to provide their user data to N number of tools for activation. In the absence of a data infrastructure tool used to merge cross-channel data points into user profiles, these novel activation systems gather data independently of one another by using native APIs and SDKs.

Over a period of time, as all these tools collect more data and store them based on their schema requirements, anomalies can develop between data sets located across the business stack. Due to this, the data quality at the end users’ disposal gradually falls. It is understood that information regarding your users can be your business’ greatest asset, it can quickly become a major liability if not handled properly. For instance, inaccurate data identifiers on customer profiles could result in “personalization” campaigns turning impersonal. Inaccurate data collection or aggregation leads to your product team having an inaccurate picture of user journeys, and sending your next project down the drain. There is a long list of things that can go wrong.

A team that relies on data also relies on data quality, and stakeholders who make these decisions about how data is collected, stored, and used have an impact to prioritize data quality from collection to activation. However, the trouble arises when data is impacted as it cannot speak for itself. When there is a user event with an inaccurate data identifier, or two profiles in different systems that belong to the same user, this data stays in your systems of record, waiting to be queried and used to target your customers and prospects. Data quality issues do not pose a major threat until they start causing negative results for your organization.

As humans take full control of their health by getting access to proper healthcare, businesses should periodically assess the user data health. If you are unsure, then get in touch with Conneqtion Group, India’s 1st Oracle PaaS Partners for assistance. In this blog, we will take a detailed look at how you can achieve this by checking the key metrics that can serve as a guide for the future. After that, we will shift base to advanced processes that demand involvement of data engineers and proper data infrastructure. Moreover, we will try to cover a trustworthy way to check data quality that businesses with mature data can benefit from. So, let’s explore.

Source

Track High-level Metrics

Email Bounce Rates

One of the easiest ways to monitor the quality of data is to keep a close tab on your email bounce rates. Almost every other business maintains customer email lists and sends messages to users frequently. These days, email service providers offer an easy way to track your bounce rates, apart from other metrics like click-through and open rates. Moreover, apart from tracking the success of your email campaigns, bounce rates can also offer a window into your business data health.

To get a clear picture of your current email bounce rates, Mailchimp offers a useful set of benchmarks according to different industries. If you think that your bounce rates are higher than industry averages, or if they are increasing over a period of time, it shows a problem with the timeliness of your data. As outdated email records means that other user identifiers are of no use anymore, the problem may not be limited to the data you are using for email campaigns. When bounce rates increase, it is important to check the data residing in other systems, and purge and update outdated records when required.

Time-to-data Value

“Time-to-data” value means the amount of time and effort it takes to collect your data and use it to gain competitive advantage for your business, and it can act as a critical benchmark for your data health. If the time between collecting and activating data increases rapidly, or if data analysts have a hard time preparing datasets, it might be the time to take a closer look at the data sets before making any decision.

Various factors of data quality, like accuracy, completeness, and consistency, can lead to longer time-to-data value. If these records are stored in incompatible formats, data engineers and analysts may have to spend more time updating data sets to query audiences. If records are found to be inaccurate or incomplete, data sets may have to be reviewed and cleaned randomly, which can reduce the speed with which data can drive results.

You might also like: 11 Cloud Computing Challenges in 2022

Direct Data Quality Checks

Single-System Sanity Check

Another commonly used data quality measure is also known as a sanity check. This data validation check needs a random sample of records in a database, and evaluating the values, data types, and structure of this sample to a set of expectations. For better sanity checks on customer data in an engagement or analytics tool, query a random sample of a particular number of user profiles from the system. Once that is done, compare the values for an identifier on each profile - like email, for instance - to what you would expect them to contain. In case of email, check the total number of blank email values are found? How many values contain valid addresses? If you find a large number of blank or invalid records, it means the quality of the remaining data may be compromised and a complete analysis of this data set is required.

Data Set Comparison

Till now, we have covered high-ticket flags that can potentially signal a greater issue with data quality. With the help of email bounce rates, time-to-data value, and throughput irregularities as indicators of data quality is like checking a human’s health. As abnormal results suggest that there is an issue that needs to be solved, it cannot accurately diagnose the issue, nor can it propose an optimal solution. To do so, you will have to conduct a detailed analysis and employ analysis techniques that can predict data accuracy and consistency.

Cross-System Profile Comparisons

In businesses that collect, process, analyze, store and activate a huge amount of data, various teams often depend on separate systems for their data use cases. In most cases, and particularly when identity resolution is managed in individual downstream systems, these systems can change your data silos that house fragmented and inconsistent copies of incoming data.

To understand if the data quality issue is affecting the data sets residing in your activation tools, your data team can perform a simple query and comparison across systems. For this to happen, determine two systems in which you would expect to find the same set of user profiles. For example, a product analytics tool like Indicative, and an engagement tool like Braze. Choose a random sample of users to yield meaningful results, and query both the tools to get identical sets of user profiles from each system.

In Indicative, each profile should absolutely match its corresponding profile in Braze (all identifiers like email, phone number, device IDs, etc.) and user attributes (first name, last name, address, age, etc.) should be the same. While a small amount of variance between datasets is to be expected due to inevitable data degradation in those tools, wide variations can show that there is a fundamental issue with data accuracy in these systems. In that case, you may think about conducting a deeper cross-system comparison and purging all inconsistent records. It will not stop the problem from developing again. An ideal way to do this is to have a robust and flexible identity resolution capability in place.

A Standard Data Plan

For businesses that develop and maintain data plans, there is a more reliable and effective variation on the above analysis. Instead of querying the same set of user profiles from multiple systems and comparing like profiles to each other, it is best to use your business’ data model to identify the values and format you expect to see on each user profile in a particular system. It enables you to check the downstream profiles against a universal standard defined by your team. This allows you to maintain a better accuracy than comparing downstream profiles against each other.

Conclusion: Prevention is Better than Cure

The metrics and methods to evaluate data quality in the blog above can allow your business to leverage data with precision and take your business to the next level. These are tried and tested methods and none of these solutions should be considered as a long term solution to maintain data quality. To address this pressing need, businesses should build a robust data planning practice, and adopt other systems and practices that assist data quality at the point of collection and protect it during delivery and activation. We hope that this blog has helped you understand the importance of quality data and how to protect your data in a sustainable manner. If you have any questions or concerns, please feel free to comment below or get in touch with us at [email protected].