Data observability is the term given to a system of practices and processes, designed to fully understand the health and functionality of a company’s data as it is created, collected, processed, and generally flows through the business.

Essentially, it means understanding the health and state of the data, which allows it to be managed and best practices applied. In order to allow the process of data observability to function, there are four key areas (the four pillars) that need to be managed, these are:

• Metrics

• Metadata

• Lineage

• Logs

What is each pillar used for?

As with most aspects of data science and data management, the ability to measure and monitor data is essential in order to make it functional and usable. Each pillar of Data Observability serves a specific purpose that supports the overall goal, and allows for measurable, quantifiable, and actionable operations as a result.

Metrics: These numerical values are applied to different components, such as CPU utilisation, response times, cache sizes, etc – they are values that allow for assessment, comparison, and tracking of performance.

To put it very simply, metrics are an internal characteristic, and the exact numbers that the data is comprised of – they are the most basic element required to make any sort of analysis possible. Without accurate metrics, the process cannot be started, let alone completed.

Metadata: In simple terms, metadata is considered to be ‘data about data’ and although various elements (such as volume, schema, or when the data was gathered) can have an impact on metrics, metadata can be scaled independently whilst still preserving the statistic characteristics.

In terms of data science and monitoring, metadata is used to identify issues with the quality of the data.

Lineage: Also known as ‘provenance’, lineage refers to bidirectional dependencies between upstream and downstream data, as well as the range of abstraction between individual systems.

This allows for datasets, that would otherwise exist in isolation (such as being stored in a data warehouse) to be checked and examined against specific criteria and turned from an abstract into a usable function.

Logs: So, the first three pillars set up what data is being evaluated, how it functions with external operations, and how it performs with expected processes. Logs capture the interaction between different systems, or between machines and people – and record who (or what) is doing what at any given time.

This element of monitoring allows for a deeper understanding of how, when, and why the data is being used.

Key technology to maintain Data Observability

By combining the four pillars of Data Observability, companies can effectively monitor what their data is, where it is being used, and how they can improve their processes to make the best use of the information gathered.

In order to do this with ease, there are a number of technologies on the market, and technology partners with tools, programs, and applications to assist.

Some of the most popular options for Data Observability include:

• Monte Carlo

• Datadog

• Grafana

• Databand

• Datafold

• Acceldata

Choosing the right tools will depend on your specific needs, and the sort of functionality that you require. If you need assistance in making the best choice for your business, our team of experts are happy to provide assistance, so your data observability is doing the best for your company.

Gaining data observability with Ardent

Did you know that organisations with data visibility with a data driven approach are 23 times more likely to secure new customers? If you are looking to leverage your data for the countless benefits on offer, we can help. Explore how we have helped our clients unlock the potential of their data:

Monetizing broadcasting data with timely and reliable data availability

Improving data turnaround by 80% with Databricks for a Fortune 500 company

Driving growth for global brands with robust, scalable data pipelines with AWS infrastructure

Get in touch to find out more or explore our data engineering services.