2021: The Year of DataOps
Centralizing an organization's data in a cloud data warehouse gives all stakeholders big-picture access to everything going on at the company.
Join the DZone community and get the full member experience.Join For Free
The past year was a rough one for companies large and small. As the pandemic spread and living and working indoors became the norm, customer preferences and demands changed seemingly by the minute. The companies that were able to respond quickly to those changes thrived.
The key to their agile response? They utilized the most up-to-date, current data to generate deep and timely insights into their business. If data is siloed or dated, it can’t be used effectively across an organization. Centralizing an organization's data in a cloud data warehouse gives all stakeholders big-picture access to everything going on at the company, including customer data, product information, and research. In today’s rapidly changing climate, this is crucial.
DataOps has emerged as an effective process to manage all this data because it automates data flows and transformations throughout the business. DataOps comprises two main ideas: governance and automation.
Governance means bringing best practices from software development to data analytics. This includes:
- Version control of changes.
- Peer review of code.
- Separated testing and product environments.
- Tests of data quality throughout the data flows (most important!).
Each of these best practices enables the trust and maintainability of data pipelines. Achieving trust and maintainability enables a data engineering team to move onto other projects, knowing that they will be alerted whenever something goes wrong in their existing processes — before it impacts the business.
Data governance is important because it is the process companies use to manage the utilization, security, and integrity of their data. Key to any new initiative is the trust and standardization of data using best practices. Data governance has proven to be critical in industries (aerospace, pharmaceuticals, and many others) where the most up-to-date and accurate data is essential to achieving both quality and safe results.
Many projects are undertaken with a “get it done” ad hoc process in place — for example, someone’s personal Excel spreadsheet, with no institutional knowledge of how it was created. DataOps centralizes and standardizes data management so business analysts and data engineers benefit from talking the same language (e.g., SQL).
The other key component of DataOps is automation, which means replacing manual data integration work with off-the-shelf integration and orchestration tools. Data orchestration is the plumbing that moves, transforms, and validates your data. The biggest benefit of automation is freeing up data engineering time from routine tasks. Now data engineers have the time to partner with the business to innovate data-driven insights and products that impact the bottom line. A great example of this is Drizly, the world’s largest alcohol marketplace. Its data team was able to take its freed-up time to build a new data product — a recommendation engine for their retail partners.
Today’s modern data stack allows us to move away from the old way of collecting and siloing data. For example, spreadsheets are a useful tool and generate nice-looking reports, but there is time and effort involved in getting multiple data sets into a spreadsheet that will not be up to date by today’s real-time standards. This approach routinely results in a reliance on older data that keeps companies from getting the timely insights they need. For example, in a recent report by Dimensional Research, forty-one percent of data analysts surveyed said they have used data that is two months old or older. In the last year, two months often felt like a lifetime ago with how fast the market was changing!
Instead of having data teams busy reconciling one-off reports about what’s already happened, DataOps streamlines the process so you can find out what is happening in the business. Leveraging that centralized data with DataOps also lets you get a more complete picture of your customer’s journey by combining product usage data, marketing website interactions, sales interactions, and support engagement.
A Proven Model
The value proposition for DataOps follows the popular DevOps model. Just as DevOps breaks down the silos between developers and operations teams, DataOps encourages collaborative data management practices for how data flows across and throughout a business, as well as orchestration, quality, security, and ease of use. Note also that DataOps is not tied to a specific technology, architecture, tool, language, or framework.
By using automated pipelines within a DataOps environment, analysts will spend less time finding, integrating and cleansing data and more time actually analyzing it. The Dimensional Research report notes that over 60 percent of data analysts said they wasted time waiting for engineering resources several times a month and spent only 50 percent of their time analyzing data.
The Importance of Real Automation
The massive increase in data and variety of sources is well documented and shows no signs of letting up. It’s not unusual for a large firm to have hundreds if not thousands of applications — a trend that is only increasing with the adoption of SaaS applications. It’s virtually impossible to keep up with all the data those apps generate unless the data is centralized and the collection process automated.
The automation part of DataOps is extremely important and many vendors tout their automation. In most cases, it means their software will automatically run on a schedule. But typically this also means the user has to enter what the software is going to do, and if that code ever breaks, you (your company) are responsible for fixing it.
If that’s automation, it’s only the easy part. If your cloud vendor has an outage or there are changes to your network configuration, you may see changes to what you thought was automated after the system recovers. When you evaluate DataOps solutions, be sure they include automated schema migration, so whenever you are doing data migration there is no data loss and all aspects of replication are completely automated.
Preparing for What’s Next
The pandemic revealed which businesses could adapt quickly — and which couldn’t. In this rapidly shifting business climate, the need for true business intelligence has never been greater because big data is not just diverse, it’s constantly changing. DataOps paves the way for faster, more accurate decision-making, giving companies the real-time business intelligence they need to be successful in the 21st century.
Opinions expressed by DZone contributors are their own.