Is it possible for enterprises to improve data quality at scale in the face of ever-increasing data collection? The answer is yes, but to do it, data teams need a data observability solution with advanced AI/ML capabilities to automatically detect data and schema drift, anomalies, as well as lineage. Using different data technologies and solutions along the data lifecycle can cause data fragmentation. An incomplete view of data prevents data teams from understanding how the data gets transformed, thus causing broken data pipelines and unexpected data outages, which in turn requires data teams to manually debug these problems.

Data observability starts with having reliable data, which gives data teams end-to-end visibility of their data assets and data pipelines, along with the tools to ensure the reliable delivery of trusted data. This includes automated and easy to use, yet powerful tools to ensure high data quality at scale, dashboards, and alerts to monitor data and identify problems when they occur, and multi-layered, correlated data, and drill-down to quickly identify the root cause of problems and remediate them.

Data observability can offer full data visibility and traceability with a single unified view of your entire data pipeline. This can help data teams to predict, prevent, and resolve unexpected data downtime or integrity problems that can arise from fragmented data.

So, enterprise data teams need to ingest different data types across a wide range of sources such as their website, third-party sources, external databases, external software, and social media platforms. They need to clean and transform large sets of structured and unstructured data across different data formats. And they need to wring actionable analysis and useful insights out of large, seemingly unrelated data sets. As a result, enterprise data teams can easily use multiple different technologies from ingestion to transformation to analysis and consumption.

All of that data requires query and data pipeline execution so it can identify data that is not arriving on-time so pipeline performance can be optimized. Teams need to be able to set SLA alerts for data timeliness (as well as other areas) and get alerts if SLAs are not met. Data must be followed all the way from source to consumption point to determine if the data arrived, its timeliness, and potential issues.

Using different data technologies can help data teams handle the ever-increasing volume, velocity, and variety of data. The trade-off in using these many technologies is fragmented, unreliable, and broken data.

This is where an enterprise data observability approach can help. With this kind of approach, data teams get a single unified view of the entire data pipeline across different technologies through the entire data lifecycle. And it can help data teams automatically monitor data and track lineage. It also helps to ensure data reliability even after the data transforms multiple times across several different technologies.

Data observability will enable data teams to define and expand the inbuilt AI rules to detect schema and data drift along with other data quality problems that can arise from dynamically changing data. This can help prevent broken data pipelines and unreliable data analysis. Data teams can also use data observability to automatically reconcile data records with their sources and classify large sets of uncategorized data.

Data Observability Can Automatically Identify Anomalies and Root Cause Problems

Advanced AI/ML capabilities from data observability solutions can automatically identify anomalies based on historical trends of your CPU, memory, costs, and compute resources. For example, if there is a significant variance in the average expected cost per day, when compared to the historical mean or standard deviation values, a data observability solution will automatically detect this and send you an alert.

An effective data observability solution can correlate events based on historical comparisons, resources used, and the health of your production environment. This can help data engineers to identify the root causes of unexpected behaviors in your production environment faster than ever before.

AI and ML Can Help Enterprises Improve Data Quality at Scale

Data is becoming the lifeblood of enterprises. In this context, data quality is only going to become more important. “As organizations accelerate their digital [transformation] efforts, poor data quality is a major contributor to a crisis in information trust and business value, negatively impacting financial performance,” says Ted Friedman, VP analyst at Gartner.

Organizations must improve data quality if they want to make effective data-driven decisions. But as data teams collect more data than ever before, manual interventions alone aren’t enough. They also need a data observability solution with advanced AI and ML capabilities, to augment the manual interventions and improve data quality at scale.