DataOps is enterprise data management for the AI era. Now you can seamlessly connect your data consumers and creators to rapidly find and use the value in all your data. Data operations is not a product, service or solution. It's a methodology: a technological and cultural change to improve your organization's use of data through better collaboration and automation. That means improved data trust and protection, shorter cycle time for your insights delivery, and more cost-effective data management.
More than a discrete, technology platform, DataOps is an approach or a methodology. It means assembling many data technologies and practices into an integrated environment. Data flows easily through this system from your data sources through a data refinery and a data repository to data consumption, which then helps you make a positive impact on your business. Along the way, your technologies, processes and people are vital to its effective conclusion.
The framework for your DataOps combines five essential elements. The first element is enabling technologies, many of which are probably in your enterprise already (including IT automation, data management tools), as well as AI and machine learning (ML). The second is an adaptive architecture that excels at continuous change with major technologies that are interoperable. The third is intelligent metadata that the system creates automatically, often at ingestion to save time later in your data pipeline. The fourth is the DataOps methodology to build and deploy your analytics and data pipelines, following your data governance and model management.
The fifth element of a DataOps framework is the most important and most difficult: culture and people. To fulfill the potential of DataOps, you must have or build a culture of collaboration among your development, test, operations and business teams. Only then can DataOps put the right data in the right place at the right time to foster real business value.
A DataOps architecture calls for significant adaptability as data requirements and data usage change rapidly and continuously. Your data consumers – data analysts, data scientists and business managers – develop new and different needs as their business priorities and market conditions evolve. An adaptable architecture accepts and adapts to these changes, allowing the flow of data and quality insights to improve at each step.
Successful DataOps architecture supports and requires collaboration across your company. As your data consumers pull data and insights for their business initiatives, they must be able to quickly build and shape their data and the data pipelines it comes through. And the architecture must make these data operations as easy and convenient as possible to foster adoption and smart business.
You can imagine how big this question is. But we'll give you six major points or steps on your journey to reaching the full potential of your data. Start with your DataOps foundation. That's technology and process that allow, encourage and require collaboration and sharing among your teams. Next, tune your technology portfolio to remove redundancy and consolidate control within your teams. Third, consolidate between your teams to reduce the inconsistencies that hamper collaboration. Fourth, integrate DataOps practices across your teams and data pipelines. This is often a difficult stage where collaboration requires your people to use unfamiliar processes and trust other teams that they've not worked with before.
By the fifth step, you have aligned your people and it's time to automate your processes. Automation makes your data pipelines more efficient and your data operations more effective. But you're not done yet. The sixth and last step is giving your data consumers the ability to serve themselves. This is where data quickly becomes information and insight to unshackle the full power of your DataOps, which is now evident across your organization. Don't forget to revisit every component of your data operations and measure your processes meticulously to continue to improve, adapt and upgrade them to keep your insights as powerful as they can be.
A wide suite of tools and processes enable DataOps in your enterprise, including data management technology (data catalogs, data virtualization, data pipelines, AI model management) as well as technology for versioning, test automation, deployment automation and release management, and runtime orchestration, or even collaboration. Automation for testing and deployment use AI and ML to support processes and workflows, helping you avoid manual configuration. You'll want to rely on technology to lower barriers to interoperability. Whether you integrate your technologies into a single foundation or form a collection of interoperable technologies, you want those technologies to work across all of our current and anticipated data environments: on-premises, cloud, multicloud and hybrid.
Smart metadata is vital. Use smart technologies with extensive AI and ML, which your intelligent metadata will use to improve its inferences. When you create metadata automatically at ingestion, automatically detect it at runtime, and tag data objects accordingly, you significantly reduce your team's manual effort. As a result, you speed development of your data pipelines and accelerate adoption and effective analysis by your teams.
DataOps is a methodology and mental attitude that has collaboration at its core. There is no single engineer – or any other role – that makes DataOps succeed. Your company's collaboration extends from your IT staff to your data experts to your data consumers. Siloed thinking and territories will dissolve into cross-team collaboration and a broad understanding that your data belongs to the whole company.
That doesn't mean that individual skills dissolve into companywide activity. But it does mean that your teams use their existing skills differently. Data engineering, data quality, data profiling, data science and data management remain necessary and useful. They now help your DataOps infrastructure supply your data consumers, who are your data and business analysts. Through DataOps they improve their ability to explore the data quickly and on their own. Data stewards shift in their role, now asked to maintain data quality and enhance the metadata. There are, of course, data engineers who bring in the data and discover data gaps to fill. And the IT and operations staff maintain and optimize the data operations.
Well, we are at Hitachi Vantara. We're pioneering DataOps and its development in our own business, and we work with our customers and partners to build and optimize their DataOps. With almost 110 years in operational technology and more than 60 years in IT, Hitachi has deeper experience than anyone else can bring to DataOps.
We empower our customers to realize their DataOps advantage through a range of offerings that support what we call the Data Stairway to Value (or SEAM). These offerings enable our customers to:
STORE: Store, manage and protect data at the lowest cost and at the right service levels across edge, private, hybrid and multicloud solutions.
ENRICH: Enrich data with metadata classification and cataloging to provide context for intelligent data management and governance.
ACTIVATE: Discover, integrate and orchestrate enterprise data assets and leverage analytics to generate actionable insights for every enterprise interaction and application.
MONETIZE: Deliver outcomes that capture the full economic value of all the data inside our customers' enterprise and beyond.
In the end, it's crucial that our customers choose Hitachi Vantara – a partner who will co-innovate with them to successfully deliver on their vision. This means we always start at the business outcome they want to drive. We pair this approach with the industry expertise only we have, and implement integrated systems that maximize the value of data at each step of SEAM. Excellence at each step enables success at the next level and empowers our customers to go from crawling to walking to running towards digital maturity and a true DataOps advantage.
Because DataOps is a methodology, it's not a product that comes as a storage-as-a-service (SaaS) offering. But SaaS can be part of a DataOps practice, adding microservices, orchestration and data flow management across our organization. Several of the necessary DataOps tools you'll need are available in the SaaS delivery model.
The quick answer is that no business has fulfilled the complete potential of DataOps quite yet. There is an ongoing need for improvement. That said, there are some companies that are further ahead than most – often hi-tech companies that have large DevOps teams who now also support DataOps initiatives. But in a broader sense, many organizations may be doing some form of DataOps already, without actually knowing it or calling it that. Initiatives for data agility often align closely with DataOps initiatives.
We should add quickly that we practice DataOps in our own business. For example, we put enterprise analytics, reporting and an IoT platform on a data lake architecture using object stores and Pentaho. As a result, we increased efficiency, reduced cost of operations and grew new business opportunities. We achieved a 30% improvement in data analytics operations, saw a more than 50% improvement in data quality and consistency, and achieved a 20% reduction in the costs of operating the platform. We call that Your DataOps Advantage.
DataOps is a newer concept that is broader than DevOps. Just like DevOps, DataOps automates, simplifies and relies on fresh collaboration between teams and departments. DevOps builds collaboration between development and operations within IT. DataOps builds – and requires – collaboration across the entire enterprise, from IT to data experts to data consumers. DevOps makes IT more effective. DataOps makes the whole company more effective.
In both DevOps and DataOps, companies rethink the whole problem from end to end, including all the goals. DevOps expands the scope of the problem, seeing it not as a Dev problem or an Ops problem, but a DevOps problem. DataOps does the same thing with organizations thinking through the flow of data from its creation to its use. But DataOps affects far more groups as the entire organization relies on data. DataOps is also more complex. In DevOps you essentially have one delivery pipeline (code to execution), but in DataOps you have production deployment and data pipelines to train data models and execute data flows. You need to continuously adapt, improve and measure all of those.
Recent research by 451 Research, "DataOps: the foundation for agility, security and transformational change," offers significant insights from companies that are already using DataOps methodologies. The report concludes with these findings: