A survey of Fortune 1000 companies across 10 industries shed some interesting light about how enterprise data quality improves business impact. The results found that if companies could improve the quality and usability of their data by even 10%, they could increase return on equity (ROE) by 16%, amounting to an increase in revenue of over $2 billion every year for the average Fortune 1000 company.
But how can enterprises improve data quality at scale as they continue to collect more data than ever before?
Enterprise data teams can’t rely on manual interventions to improve data quality at scale. They need a data observability solution with advanced AI/ML capabilities to automatically detect data and schema drift, anomalies, as well as lineage.
Data observability provides visibility into data operations across the entire data lifecycle
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 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.
While the specifics may vary from industry to industry, all enterprise data teams need to work with several data types, sources, and technologies throughout the data lifecycle. For example, a healthcare enterprise may need to collect customer details directly via phone or their website for certain administrative tasks such as enrollment. At the same time, for billing, they may also need to work with external software, databases, and third-party payment processors. They may also need to work with social media, voice, and video customer feedback to gauge the ongoing quality of their healthcare operations.
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.
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 the potential for fragmented, unreliable, and broken data. Additionally, bad data can lead to some of the most common data security challenges business typically face.
This is where a multidimensional 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 Uses AI Rules to Effectively Handle Dynamic Data
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.
Dynamically changing data can create unforeseen problems. Changes in source or destination can cause schema drift. And any unexpected changes to the data-related structure, semantics, or infrastructure can cause data drift. The right data observability solution can detect any structural or content changes that cause these issues. It also helps you reconcile data in motion to ensure data fidelity. This can help you avoid broken data pipelines and corrupt data analysis.
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. With this approach, data teams can do the following:
- Get an overview of all application logs as a time histogram, searchable by severity or service
- Identify slow queries and their runtime/configuration parameters
- Understand how queue utilization varies for different queries
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.