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When DevOps teams talk about monitoring a database, the primary motivation is to ensure that the database won’t suffer a performance hiccup.

Long queries, timeouts and table scans are among the most popular causes behind lousy customer experience.

However, in recent years, more data has been shifted to cloud databases. And we’re seeing vast improvements in data cloud experts, with SnowFlake, Redshift, RDS, and BigQuery, among the offerings enhancing the scale and quality of service at which companies now operate.

As companies evolve, and become more data-driven, collecting massive stores of data, there is now a new need. How can this data be leveraged faster and more accurately to gain a competitive edge? How can data-driven companies work smarter?

The more data companies collect, the greater the potential to use that information to drive smarter decision-making – from optimizing spend, to improving customer experience, to preventing churn.

One of the biggest frustrations for these companies is when the data is wrong – or rather, the system in place to monitor and analyze the data is flawed. Perhaps a data analyst didn’t realize that data had been improperly transferred. Working with flawed data severely can mislead company initiatives, whether it’s coming from marketing channels, revenue streams, or even customer experience logs. The damage incurred by misleading data can be catastrophic, and it can take years to recover from it.

How can monitoring protect your data and better support business decisions?

It starts with automation. Companies that use artificial intelligence and machine learning to independently monitor databases and the data that’s being stored are reaping huge wins in saved time and costs. And it’s typically the DataOps teams that can take this project on to success.

So where do we start?

By analyzing every value in every table and every column across your databases, you can then identify unusual patterns that can otherwise go undetected.

There are many ways to make sure your data is healthy. At Anodot, we are seeing customers invent new ways to apply Anodot’s Autonomous Business Monitoring platform to efficiently monitor business KPIs and get more accurate alerts.

Suppose, for some reason, a specific value is not appearing at all or is changing its characteristics. What’s the likelihood that a team member would catch that – and quickly? With an automated solution, there’s a significantly higher chance that it will catch the issue early and that you can remediate faster.

So what KPIs does a platform like Anodot monitor? These are some of the most common metrics:

Measures:
  • Number of distinct values, per table per columns per hour
  • Number of specific value, per table per columns per hour
  • Average of the values, per columns per table per hour
  • Aggregations of the values,  per column per table per hour
  • Number of occurrences, per table per column value per hour

Dimensions:

  • Table Name
  • Column Name
  • Aggregation Type

 

See what Anodot can do for you and your team – schedule a demo today.

Written by David Drai

David is CEO and co-founder of Anodot, where he is committed to helping data-driven companies illuminate business blind spots with AI analytics. He previously was CTO at Gett, an app-based transportation service used in hundreds of cities worldwide. Prior to Gett, he co-founded Cotendo, a content delivery network and site acceleration services provider that was acquired by Akamai Technologies, where he also served as CTO. He graduated from Technion - Israel Institute of Technology with a BSc in computer science.

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