The world is driven by data. But most of the time we don’t even think about how complex our data systems are.
The complexity is hidden in plain sight. We check the stock market, read our health report, and review our bank statements. Most of the time, we never think about if the data is correct.
We can’t see the complex data science and data engineering that’s driving these experiences. We don’t really have any way of knowing if the data is correct. But we take its reliability for granted.
However, data professionals know the challenge to drive data quality continues — as do the risks. Except now IT not only faces the ongoing challenge of securing the data-driven enterprise; IT also faces the demands of a business that sees data more and more as the cornerstone of competitive advantage.
The expectation that your data is trusted and reliable has never been greater. And there’s more reliance than ever on enterprise data governance and more focus on the importance of data quality.
It has never been a more exciting time for data professionals.
The problem with bad data
We all know companies use more data than ever before. We know that the variety, velocity, and volume of data is growing incredibly fast — but there are hidden factors at play that highlight why this is so important now.
Imagine you built a new health care app that could monitor heartbeats.
Now, imagine the ramifications of bad data for your app. Or imagine you were building a new trading algorithm with a breakthrough financial strategy. Imagine what would happen if the data was incorrect?
The truth is we’re realizing we are all at companies that rely on data quality. And if we’re using bad data, we’re likely making bad business decisions.
Today, the way most companies consume data in the IT function is that they bring in a lot of data and they syndicate it out to the business – and it turns out that the individuals loading the data may not know that much about the data itself.
We need to move past the old manual processes and embrace Observability as the next evolution of data quality and data governance.
Modern stacks are complicated, making it hard to keep track of data quality. Traditionally, data teams have been saddled with manual rule writing, limited data connectivity, and a siloed view of data quality.
These constraints have been a drag on business. The lack of enterprise data quality erodes enterprise ability to respond effectively to regulatory, analytics, and AI demands. It limits the ability to scale. And it leads to real productivity losses, costly fines, and even significant revenue losses.
As organizations become more strategic about leveraging growing volumes of data, key business and technical stakeholders are working through the case to invest in next-generation data quality solutions.
Recently, the rise of Observability solutions that leverage machine learning offer a profound evolution over previous measurement and remediation solutions.
Collibra applies Observability to the challenge of data quality.
Collibra Data Quality & Observability leverages machine learning to generate adaptive checks and rules that proactively identify data quality issues across a variety of databases, files, and data streams. Now, you can monitor data quality and data pipeline reliability to rapidly remediate anomalies. Run Collibra Data Quality & Observability on any cloud and connect to more than 40 databases and file systems.
Automation is key. Predictive, continuous, and self-service data quality helps rule writers focus on high-impact tasks such as eliminating the root causes of data errors, and helps business users access high quality data for their analytics and AI modeling work.
Leveraging predictive data quality and autonomous rule management, Collibra centralizes and automates data quality workflows to gain better control over end-to-end data pipelines and streamline analytics processes across the enterprise.