Data is a powerful tool for any organisation, but it can only be fully leveraged when it is managed and analysed efficiently. This is where DataOps comes in.

DataOps is a methodology that focuses on the collaboration, communication, and automation of data management processes. It is designed to help organisations use their data resources more efficiently and get insights from their data. With DataOps, organisations can improve the accuracy of their data, reduce the time it takes to gain insights from it and make better use of their data resources. DataOps is an essential part of data analytics. It can be used with various data integration and data management tools, such as Alteryx, to streamline the data management process and gain valuable insights more efficiently. In this blog post, we will take a closer look at what DataOps is, its benefits and how to use DataOps with Alteryx to unlock the full potential of your data.

What are the Benefits of DataOps

One of the main benefits of DataOps is the ability to automate data management tasks. This includes tasks such as data integration, data quality checks, and data governance. By automating these tasks, organisations can reduce the time and resources needed to manage their data, allowing them to focus on more valuable projects such as data analysis and insights.

Another benefit of DataOps is improved collaboration and communication between different teams and departments. This includes teams such as data scientists, data engineers, and business analysts. You can ensure that everyone works towards the same goals and quickly identify and resolve any issues by implementing a clear and consistent method of communication and collaboration. DataOps also enables teams to turn data requests into data sources and products faster and more accurately.

Lastly, DataOps allows organisations to take a more proactive approach to data management. This includes monitoring data quality, identifying potential data issues, and implementing preventative measures to ensure that data is accurate and reliable.

The Pillars of DataOps

In my book Data Engineering with Alteryx, I explained the principles of DataOps and how they apply to a data engineering project. The principles were taken from the DataOps Manifesto. Those principles all consolidated into three pillars; People, Delivery and Confidence; each of these pillars groups principles into the areas of a Data Project lifecycle.

People Pillar

The people pillar guides how the culture in a company can support the delivery of data products. The principles service both the clients and the data product delivery team. Alteryx helps the people pillar by enabling the data team to deliver new data products for clients, embrace changes that will take place in the raw data and requirements, and lets the team organise themselves but still work together with easily interpretable workflows.

Delivery Pillar

The delivery pillar is about how a data product is delivered. How quickly you can cycle change requests. What the process is for creating a data pipeline. What automation processes are available for deploying data products, and how to manage the workflow code (in Alteryx, this is the workflow XML).

Alteryx excels with increasing the cycle times for requests, allowing a user to get a data source quicker. It also allows an easily understandable data pipeline as the visual nature of an Alteryx workflow makes it more evident what is happening and when.

Confidence Pillar

The Confidence Pillar gives the processes to deliver great data sources. Confidence is about ensuring the client is sure the data source they receive is accurate and fills their requirements.

Alteryx gives the data team methods for monitoring data sources, records of pipeline performance, and ways to reproduce the data and the workflow processing when needed.

Using Alteryx in a DataOps Framework

While one of the principles of DataOps is to allow for whatever tools happen to work best for the application, Alteryx has many strengths that work well in DataOps. For example, Designer allows for creation speed, while Server supports the automation and integration of data pipelines.

Alteryx Designer is a tool that primarily supports the People pillar. It supports culture transformation by making data source creation and analysis available to more people and increasing the speed at which individuals can work.

Designer also allows for the creation of reusable processes with macros. When you create macros, you share a standardised transformation across your team. It also means that those changes can be shared to existing workflows and data pipelines when processes evolve.

Alteryx Server is a crucial part of the DataOps process. It is core to automating the process removing key man risks and knowledge silos. But it also allows for automatic monitoring of data pipeline performance and building data quality controls. Using Server cross-organisation resources can be implemented and distributed, reducing duplication of effort and making individual work more impactful

Conclusion

This post discussed DataOps, a methodology aiming to improve data management processes’ collaboration, communication, and automation. By implementing DataOps, organisations can reduce the time it takes to get insights from their data, improve the accuracy of their data, and make better use of their data resources. We highlighted the benefits of DataOps, such as automation of data management tasks, improved collaboration and communication, and a more proactive approach to data management. Additionally, the post explained how DataOps could be used with Alteryx to streamline the data management and data source creation process and gain valuable insights more efficiently. The article also breaks down the three pillars of DataOps: People, Delivery and Confidence, and how Alteryx can support each.

If you want to learn more about DataOps and how it can be used with Alteryx, check out Data Engineering with Alteryx. I cover the entire process of creating and delivering data sources following a data engineering approach using the DataOps framework.