For those of us who spend our days thinking about, working on and stressing over data, our holy grail is to use data to create a unique benefit or advantage, whether that’s for our business or for society. DataOps exists because getting that value from data is difficult — and is made even more so by legacy processes, technologies and organizational structures that were established at a time when we were far less dependent on data. 

DataOps has emerged as a methodology that borrows from and builds upon the concepts of Agile and DevOps and applies them to the world of data. Our favorite description of DataOps comes from Forrester’s Michele Goetz, who describes it as “the ability to enable solutions, develop data products, and activate data for business value across all technology tiers from infrastructure to experience.”

Enabling DataOps Through a Data Exchange

Making the pivot to DataOps requires evolving not just technology, but also processes, organizational structures, and mindsets. The DataOps Manifesto outlines 18 core principles of DataOps, which span 6 important themes. Let’s take a look at the imperatives of DataOps and how an enterprise data exchange is the ideal accelerator for organizations seeking to embrace DataOps.

#1 It’s customer focused.

DataOps puts the aim of satisfying the customer — whether that’s an internal or external customer — at the center of everything. That is the very same objective that has driven the emergence of the data exchange, whose primary purpose is to make data easy for its consumers to access and use. This consumer-first approach is unlike any other data technology, which are universally designed for technologists and data experts.

#2 It emphasizes data quality.

The combination of large volumes and inconsistent quality are among the main inhibitors enterprises face in realizing the full value of their data is. Aggregations, like data lakes and warehouses, intend to bring all data into one repository, and then we layer on tools like data catalogs that help us navigate it. But it all leaves the data consumer with the task of poring through massive amounts of data to find something of potential value. Then they must invest even more time and effort to access that data before understanding its quality and usefulness. 

An enterprise data exchange instead tackles data quality in two ways: curation and presentation. First, the exchange is designed to eliminate the clutter and to showcase only high-quality, high-value data assets. Secondly, the packaging and presentation of data products within the data exchange serves to make it easy to understand and assess the quality and usefulness of data very quickly through an intuitive, self-service digital experience.

#3 It treats data as a product.

DataOps is based on the notion that data alone has limited value. It is when that data is combined with tools and people to access, integrate, model and visualize it that it becomes meaningful and actionable.

In our world, this translates to the concept of the data product. An enterprise data exchange presents curated data products that combine data, models, code, descriptions, instructions and even a visual identity that all help make data useful to a potential consumer. It also embeds the tools and provides the access needed to deliver value quickly.

#4 It facilitates collaboration.

DataOps embraces the notion that data is a team sport. It requires daily interactions between data suppliers, consumers and technologists to deliver value in an ever-changing environment. Going further, it promotes the notion that the best data outcomes emerge when teams can self-organize around a common problem or requirement and encourages those groups to reflect on how data is being used in order to further improve.

These notions of how people interact and behave are perhaps the most challenging shifts to make due to legacy organizational structures, processes and technology silos. A data exchange is built to empower these new ways of working by promoting distributed data ownership across business domains and eliminating the middleman between data suppliers and consumers to foster collaboration. Furthermore, Harbr’s enterprise data exchange boasts the ability for any users to come together in secure and disposable collaboration environments with the tools they need to combine, segment and analyze data.

#5 It improves the efficiency of data teams. 

Many of the concepts that are core to DataOps tie back to one simple thing: efficiency. Data management and analytics have been rife with cumbersome, manual processes for too long.  DataOps focuses on orchestrating data and the work that surrounds it, making it reproducible, eliminating technical overhead and continuous process improvement. Importantly, it aims for simplicity and reuse of analytics work.

Likewise, an enterprise data exchange is built to streamline and automate the processes surrounding data and analytics. It brings substantial automation across data workflows, simplifying the process for data suppliers, consumers and supporting teams. And very uniquely, Harbr’s enterprise data exchange facilitates reuse by enabling data products to be published for broad use, customized, then re-published so the value created through analytics can be scaled — all in just a few clicks.

#6 It makes data immediately actionable.

We’ve all heard the adage that data analysts and scientists spend 80% of their time just getting the data. But that doesn’t have to be the case. DataOps measures success based on the ability to deliver accurate data for analysis and to turn that data into insight with minimal time and effort — even under changing conditions.

A data exchange curates quality data products and enables access through a fully self-service experience. Those data products are easily understood and explored on the platform before the data is ever moved. When a data consumer finds the right data, they can subscribe to it and have it delivered to where it’s needed for use in just a few clicks. Furthermore, updates to data products are delivered to subscribers on a scheduled or ad hoc basis through that same automated process.

Realizing the Full Value of Data With DataOps

DataOps is a transformational methodology that aims to eliminate the obstacles that make getting value from data so difficult and time-consuming today. It’s principles align closely to those codified through an enterprise data exchange platform like Harbr’s.

Learn more and request a Harbr demo to discover how we can accelerate your journey to DataOps.

The 18 Principles of the DataOps Manifesto

  1. Continually satisfy your customer
  2. Value working analytics
  3. Embrace change
  4. It’s a team sport
  5. Daily interactions
  6. Self-organize
  7. Reduce heroism
  8. Reflect
  9. Analytics is code
  10. Orchestrate
  11. Make it reproducible
  12. Disposable environments
  13. Simplicity
  14. Analytics is manufacturing
  15. Quality is paramount
  16. Monitor quality and performance
  17. Reuse
  18. Improve cycle times