Data Quality Binning: What is it and Why do you need it?

In today’s world, data engineering teams constantly deal with petabyte-scale data that flows through their complex data ecosystems. Keeping the accuracy and reliability of data is the foundation for data-driven decision-making, but doing this without impacting the cost and performance SLAs of the data pipeline is becoming a challenge.

A minor error can propagate through the pipeline, leading to inaccurate insights and misguided decisions. Telmai’s new Data Quality Binning feature comes into play here. It enables data teams to ensure high-quality data flows through the pipeline for faster and cost-efficient machine learning model training and reliable analytics.

What is Data Quality Binning?

Data Quality Binning is a pre-processing technique used to segregate ‘suspicious’ data from your pipeline and allows ‘good’ data to flow through to the downstream of your pipeline. This segregation is important in ensuring that only accurate and reliable data flows through your pipelines, while the erroneous data is set aside for further analysis or correction.

Telmai’s Data Data Quality Binning operates on predefined correctness rules, a set of guidelines that you establish to define the accuracy and relevance of your data. 

As data flows through the pipeline, Telmai checks each data point against these correctness SLA’s to identify and isolate any data that deviates from these SLA’s. The good data, which adheres to the SLA’s, continues on its journey through the pipeline.

What makes Telmai great is its ability to conduct this scrutiny in real-time, ensuring that your data pipeline remains uninterrupted. 

Data Quality Binning in Action

Now lets understand how Telmai’s Data Quality Binning works. Let’s consider a scenario where we are tracking the user count of an app that’s available for Android and iOS. But a bug in the Android version has caused the user count to double.

The initial step in implementing Data Quality Binning is to define the correctness rules. This criteria helps in identifying the data that is considered to be the ‘suspicious’ data. For instance, in our scenario, a rule might be set to flag user counts that are significantly higher than the historical average.

Once the correctness rules are set, Data Quality Binning springs into action. It scrutinises each piece of data against these rules, identifying those that comply as the ‘suspicious’ data.

Now, specifying the binning destination is crucial as it determines where the segregated data will be directed. In the case of our app tracking scenario, the ‘suspicious’ data, which is the inaccurate user count from the Android version, is routed to a specified destination for further analysis or correction.

Post segregation, the ‘good’ data continues its journey through the pipeline, ensuring that the analytics and insights derived remain accurate and trustworthy. Meanwhile, the ‘suspicious’ data awaits correction or further analysis, thus preventing any propagation of inaccuracies through the pipeline.

The capability to automate this process of segregating ‘good’ and ‘suspicious’ data in real-time significantly reduces the manual effort and time required to ensure data quality, thereby accelerating the time-to-value in your operations.

Steps to improve your Data Quality

Making sure data is accurate and reliable is more than just a task; it’s about doing the job right.

With Telmai, you’re not just fixing data mistakes, you’re proactively identifying and separating them early on, making sure the data that flows through is dependable and consistent.

Ready to take the next step in enhancing your data quality? Request a demo of Telmai today and see how it can streamline your data.