Data Observability: What it is and Why it is important for your Business

Data Observability: What it is and Why it is important for your Business

It’s pretty much a cliché to say these days, but data really is the foundation of modern business. Not only is there an eye-watering amount of it, but with increased pressure from consumers and governments, data governance and regulation are as important as it’s ever been.

This shines a spotlight on how organizations maintain and monitor their data, as well as how they’re ensuring data quality.

Until recently, developers were able to use application performance management (APM) tools like AppDynamics and Datadog to investigate issues with data. But it’s clear that a more proactive approach is needed, with tools to match.

Data Observability has entered the chat

If you want to see how OvalEdge can help you to improve your Data Observability, book a demo here with our team

What is Data Observability?

In the simplest terms possible, Data Observability is a way to identify data quality issues and track data lineage, with the help of tools.

For a more comprehensive summary, Gartner has done a good job of defining it:

Data observability is the ability of an organization to have a broad visibility of its data landscape and multilayer data dependencies (like data pipelines, data infrastructure, data applications) at all times with an objective to identify, control, prevent, escalate and remediate data outages rapidly within expectable SLAs. Data observability uses continuous multilayer signal collection, consolidation and analysis to achieve its goals as well as to inform and recommend better design for superior performance and better governance to match business goals.

Data Observability is a method that can be used to solve a variety of data management problems. Every company is different, and so is their data, but however you’re set up, Data Observability has the potential to make a big difference.

Why is Data Observability Important for Business?

Companies rely on increasingly complex data solutions, which makes it harder and harder to maintain consistent data quality. APM tools are good for investigating issues, but this is often too late.

There are a number of issues that if not caught early, can cause a lot of problems:

  • Inaccurate data
  • Schema issues
  • Inconsistent data
  • Duplicate data
  • Hidden data

These data quality issues can cost companies both time and money. For example, 77% of businesses say that inaccurate data hurt their ability to respond to market changes during the pandemic, according to Experian.

This is huge, and highlights how impactful Data Observability could be if implemented effectively.

Data observability tools use advanced technology like automated monitoring, root cause analysis, data lineage, etc. These not only help save time and money, but they can also have a positive impact on the day-to-day running of your company.

Your teams can run more effectively, as they’re not held up by data issues, or bogged down investigating reported issues.

And your customers will be happier as there is less likely to be as much downtime, and their data is in safer hands.

Is Data Observability Part of Data Governance?

When we talk about Data Observability, it’s important to remember that it’s only one part of Data Governance.

For reference, this is the definition of Data Governance from our Ultimate Guide:

Data governance is the process of organizing, securing, managing, and presenting data using methods and technologies that ensure it remains correct, consistent, and accessible to verified users.

Even though Data Observability sets the framework of Data Governance, it also helps to solve larger data management challenges.

Using tools to clearly understand the health and state of your data will make it significantly easier to meet your targets and ensure you’re sticking to your guidelines.

You can use things like alerting to proactively tackle quality issues, and use advanced automation to identify problem areas before anything goes wrong.

How OvalEdge Supports Data Observability

There are a lot of Data Observability tools available, but most only focus on a small subset of features you need.

With OvalEdge, we’ve consciously built a comprehensive hybrid solution that solves a wide variety of data management problems. This includes key tools you need to achieve a high level of Data Observability.

Data Catalog

It’s impossible to implement effective Data Observability without having a holistic view of all your data sources. Without this, you can’t reliably compare data or identify cross-platform issues.

This is exactly what our Data Catalog solves. It connects with all of your data sources, and makes them all accessible from one place. From databases to custom applications, everything can be viewed and queried from OvalEdge.

This essentially becomes your Data Observability hub, where you can manually analyze your data and identify improvements. 

Monitoring & Rules

Manually locating issues can be useful when investigating individual issues, but it’s also important to take a proactive approach to data quality.

This is why monitoring is so important. You can define rules that automatically catch data quality issues, and alert you to them. We talked before about scheme issues, inconsistencies, duplicate data, etc. You can create rules that monitor and catch these issues as they’re introduced.

This not only means you can prevent further issues, but also means you always have a baseline level of confidence in your data quality. Which makes it easier to make crucial business decisions. 

Automation & AI

As well as crawling your data sources to build your data catalog, OvalEdge has a number of other automations that make it easy to implement Data Observability.

It profiles your data and collects useful statistics about your data, based on your bespoke needs and configuration. It also crawls the data, and combines this information to automatically identify relationships and patterns.

For more advanced automation, you also get an AI Assistant you can train to organize your data. We also use AI to detect and mask PII, in line with governance and regulation restrictions.

It’s time to get a better view

As you’ve hopefully learned from this article, Data Observability is critical in the modern age of data. By using tools to improve your view, and be more proactive, you can escape the existing struggle, highlighted here by Sanjeev Mohan:

Data scientists spend 80% of their time wrangling or munging the data, rather than putting their hard-earned PhDs to use to build predictive models

By using automation, monitoring, and many more key tools, you can free up your data scientists to do the things they’ve actually trained to do.

It won’t just make your team happier. You’ll be putting smiles on the faces of your accounts team, your investors, and most importantly, your customers.

What you should do now

  1. Schedule a Demo to learn more about OvalEdge
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