Understanding DataOps

Modern data challenges require modern solutions. DataOps provides a new type of data management pipeline that can help you better leverage your data to inform business decisions. By automating data-related tasks and optimizing data flow pipelines, you can take advantage of rapidly increasing data sources more quickly and with fewer process limitations. To decide whether this system might be right for your organization, explore what DataOps is, its benefits and challenges, and how you can start implementing it.

What is DataOps?

DataOps, which stands for Data Operations, is a data approach that integrates Agile development, statistics, and DevOps principles to automate data pipelines and improve quality. With DataOps, you treat data management as a collaborative and iterative process, allowing you to streamline your data workflows with existing data sources and evolve your methodologies as data sources change.

In traditional models, you needed to collect, process, test, and manage data manually. This was typically performed by a specific data team, which was separate from teams that analyzed and used the data directly. As the amount of data organizations use has expanded exponentially, this manual approach has slowed down data processes and deployment, and the lack of a centralized infrastructure has limited transparency into the information available. Data Ops aims to reduce bottlenecks caused by using traditional data management approaches with big data by creating a centralized hub for automated data collection and operations. This pipeline is designed to enhance collaboration across end users, reduce time and costs associated with data processes, and create reproducible methods to derive insights from your data. 

DataOps vs. DevOps

While DataOps and DevOps share similarities, they also have distinct differences. You would use DataOps when working with data workflows and pipelines, while you would choose DevOps for software development and deployment tasks. DataOps aims to improve data quality and delivery speed, while DevOps focuses on accelerating software release cycles. While both emphasize collaboration and iterative processes, they use these principles in different areas of development. 

Benefits of DataOps

By incorporating DataOps into your organizational structure, you can improve your data quality, reduce the time to insights, and enhance collaboration across teams, all while creating a more agile data environment. Take a closer look at each of these benefits.

Improved data quality

Automation streamlines data quality assurance through continual processing and testing. If something is unusual, the program flags data team members so you can address issues quickly. This automation process leads to higher data quality, which can improve downstream analysis.

Faster insights

With automation reducing time spent on manual data management tasks, your team can focus on deriving insights in real-time as data enters the system and metrics can update. DataOps helps to reduce cycle times, creating a streamlined process that is efficient and reproducible from collection through insights.

Enhanced collaboration

Data democratization is a foundational principle of DataOps, meaning that you are bringing your teams together to create better insights from your data. This allows each user to access and analyze the data in a way that suits their needs, providing an opportunity to share insights and create a more holistic view of the overall data findings.

Increased agility

Flexible data integration and analysis allow you to continuously evolve the way you manage and analyze data as new information and new needs arise. With DataOps, you’re constantly receiving performance metrics showing you how your current systems are performing, which allows you to make iterative changes to ensure you are optimizing your data methodology for your current environment. You can target specific areas of your data lifecycle, allowing you to continually improve your pipeline and create a more sustainable data environment.

Challenges of DataOps

If your organization doesn’t already have a similar system in place, integrating DataOps can require significant internal changes, which require resources and buy-in from organizational leadership. Depending on the current standards, you may face cultural resistance within the workforce, especially if the current system has been used for a long time. 

When advocating for changes in this area, it’s important to take a collaborative approach, keeping in mind the larger goal of bringing your teams together to better utilize the data you have. It can take time to reorganize existing systems, and you may need to start with small steps that build toward sustainable long-term system changes.

Who uses DataOps?

One of the goals of DataOps is to democratize data, which means that stakeholders across departments and industries can access data directly and derive their own insights. Because of this, you can think of DataOps as being for everyone and used by everyone. 

While used by everyone, the development of DataOps systems is typically performed by data-driven professionals. DevOps teams, data scientists, data developers, data analysts, and data engineers often combine their skills to develop the pipelines and frameworks that drive DataOps projects to success. 

How to start implementing DataOps

While you won’t find a one-size-fits-all for DataOps solutions, you can start with a few areas of focus to guide you and your teams in the right direction. Consider the following key concepts when designing a DataOps framework that works for your team.

• Make your data readily available. As data increasingly drives decision-making and machine learning tools depend on it for training, readily accessible data is an important tool to support ongoing innovation.

• Utilize available platforms. Many DataOps platforms and tools exist, and taking the time to learn them and integrate the ones that make sense for your team can prevent you from wasting time “reinventing” existing resources. They allow you to focus on more important tasks. 

• Take advantage of automation. If you’re finding that your team is spending a lot of time on the same processes for each data set, you may benefit from automation. Automation allows you to deploy models that improve productivity and reduce errors.

Popular DataOps tools

Depending on your DataOps needs, you can choose a combination of tools and technologies that have been specifically designed to solve common data inefficiencies. Areas of DataOp tools to consider when starting out include:

• Streamline data integration with Talend, Fivetran, and Census.

• Automate your data workflow with tools such as Apache Airflow and Prefect.

• Derive real-time insights with Datadog.

• Implement version control with Git and Data Version Control (DVC).

• Manage data quality with Datafold and IBM Databand.

Learn more about data management on Coursera

DataOps is an approach to data management that fosters faster insights and increased collaboration by improving data quality and democratizing access. With exciting courses and Professional Certificates on Coursera taught by data experts, you can continue learning about data analysis and management throughout the lifecycle. Try the IBM Data Engineering Professional Certificate or the IBM Data Science Professional Certificate to start building your toolkit.