What is DataOps?
In This Article
Data is at the core of successful digital transformation. Effective data management helps support efficiency, resiliency and timely decision-making. However, the value of your data depends on how well it can be leveraged to generate accurate insights.
That's why modern organizations are increasingly turning to DataOps (data operations).
DataOps is the end-to-end management of data pipelines. It improves speed and agility in data collection and analytics delivery. It brings together development operations (DevOps) teams, data engineers and data scientists to create the tools and processes that support a data-driven enterprise.
It differs from similar strategies like MLOps (machine learning operations), which automates the machine learning lifecycle and brings teams of data scientists, engineers and operations professionals together to collaborate under a single platform to improve the ability to produce insights-rich data analysis. Tangentially, DataOps overlaps with AIOps (artificial intelligence for IT operations) in that it is supported by the continuous integration and deployment of key technical functions that AIOps provides.
DataOps applies the DevOps' principle of bringing two separate IT functions together to data lifecycle management. It leverages concepts such as continuous integration, delivery and operations to data analytics in order to "productionize" data science.
Some people call it "DevOps for data" since the process improves communication among data stakeholders while orchestrating people, processes and technologies to deliver high-quality data. It implements automation technologies to aid data flows and enhance delivery practices. DataOps also enables collaboration across business units to improve agility and speed at scale, while eliminating inefficiencies associated with accessing, preparing and integrating data.
There are a wide range of reasons that companies are using -- and should use -- DataOps.
The one that jumps off the page is truly something that everyone wants to achieve: Organizations that apply DataOps to support agile and deliberate data management are four times more likely to experience growth that exceeds shareholder expectations.
Beyond this, the main benefits of DataOps come from its ability to streamline the design, development and maintenance of applications based on data and data analytics to:
- Enhance collaboration among data scientists, engineers and technologists so they can utilize data effectively.
- Increase transparency so teams can better understand data and what it means for the organization.
- Make data more available and accessible to stakeholders and decision-makers across the company.
- Deliver accurate analytics, which translates into better insights and more effective business strategies.
- Leverage data automation to speed up IT projects while reducing costly human errors.
- Increase resiliency through standardization and repeatable data tasks.
- Ensure personal identifiable information (PII) compliance with industry regulations (e.g., GDPR, HIPAA and PCI-DSS).
- Support the implementation of cybersecurity measures and data governance policies.
- Provide high-quality data required by AI and machine learning (ML) applications.
- Transform raw data into valuable business insights and share them across the company in real time.
- Enhance data collaboration within DevOps and all business units.
- Eliminate inefficiencies associated with data silos, "tool sprawl," inefficient resource allocation and more.
- Build best practices and enable the delivery of value through continuous production.
Key areas you should focus on when implementing DataOps:
Data democratization makes sure that everyone in the organization, regardless of their technical capabilities, can work with data comfortably. Information should be constantly available to empower stakeholders to make data-informed decisions with minimal IT assistance.
Agile tech stack
DataOps practice requires a data science platform to support data movement, orchestration and integration. Consider implementing open-source tools that use languages and frameworks already used by your teams, so they don't have to reinvent the wheel.
Automation technologies help shorten the time-to-value of data-intensive projects by automating manual and repetitive steps (e.g., quality assurance testing and data analytics pipeline monitoring). Leverage microservices so that data scientists can deploy models using APIs without knowing how to code or waiting for IT assistance.
Data governance ensures data integrity and fosters trust in data. It manages and protects data assets by defining who can access the data and how it can be used. It also ensures the availability of high-quality data through complete data lifecycle management that involves people, processes and technologies.
While DataOps offers many benefits, it's not without its challenges.
For example, the lack of visibility -- regarding where the data came from, where it has been, how it's stored and who had access to it -- can impact the reliability of the information. This, in turn, can prevent you from building a data-driven culture. The lack of monitoring or standardization can lead to confusion and costly errors as well. Meanwhile, a poor data management foundation can impact data security and resiliency.
Implementing DataOps requires seamless coordination among people, processes and technologies in your organization to ensure that the entire data lifecycle is properly managed.
But when done right, and when taking all this into account, this process will allow all stakeholders to use high-quality data to drive real-time and accurate decision-making. The value of this proposition is clear -- and can pay dividends for years to come.
Ultimately, having the right technologies is the key to building a solid foundation that supports people and processes in data management.
See how WWT can help your company get the most out of DataOps.