Custom SoftwareDatabase Technology Trends

DataOps – Developing Processes for Analytics and Data Management

By September 17, 2019 No Comments

Big data and analytics mean big things for industries across all sectors. As data continues to grow, so do the applications. The newest challenge is streamlining the analytics process to improve efficiency and create actionable insights in a timely manner. That is where DataOps comes in. This newest development utilizes concepts learned from DevOps practices to simplify and streamline the data pipeline and analytics process. Let’s take a look at how this works.

The Science of Analytics

The analysis of big data is a complicated process of pattern recognition using statistical tools and algorithms. Your data scientists, engineers, and analysts may all play some part in this role. Managing the analytics process from an operational standpoint, as well as a statistical standpoint, can be time-consuming. If you want to have real, useable insights from your data it is imperative to validate both input and output data. It’s taxing to your analysts and means longer lead times on identifying key insights. But these steps are there for a reason, right? You can’t skip vital processes just because they’re lengthy. But you can innovate those processes and improve their efficiency.

The development world has been working on this for a while with Agile process management and DevOps. This is essentially the process of applying operational improvements, like process alignment and automation, to improve the time to production. And it’s working. The analytics arena has taken notice and is jumping on board with their own version, coined DataOps.

Project Monitoring with Agile Planning

DataOps gives analytics teams a big boost through the application of Agile management strategies. This popular development strategy utilizes short “sprint” cycles to create a rapid development environment that can quickly react to changes in the market. Likewise, in DataOps, analytics teams utilize shorter sprint cycles and the general concept of quick, agile movements to stay ahead of the data. Long-term projects require a significant investment and are vulnerable to shifts throughout the process. Data and analytics is particularly susceptible to this vulnerability. It is a volatile science, and conclusions made today can drastically change tomorrow. That is why it is so crucial to maintain flexibility in your data science and analytics teams.

Data Input Monitoring with Statistical Process

Advanced analytics is all about statistics. Understanding the data involves advanced pattern recognition and statistical tools and techniques. And data validation is extremely . This is where a lot of bandwidth can be taken up. But teams are working to implement statistical techniques to improve this process. Statistical Process Control (SPC) efficiently ensures the data pipeline is trustworthy and accurate. This technique uses a series of controls to ensure the data input is validated. It can dramatically reduce end result errors and quality control (output) issues.

In the end, DataOps is no “one-size fits all” solution. It is in part strategy, and part mindset of innovation. It means identifying areas within the analysis process that can be changed and improving that process to give us better insights on a shorter timetable. Just because we all do things one way, doesn’t mean there isn’t a better way. DataOps is building that better way for big data analytics.

Dobler LLC is a leading provider of database services, premier software development, and information technology support, servicing clients ranging from businesses to FORTUNE companies across multiple industry verticals. For more information about how Dobler Consulting can help you design your data strategy, visit DoblerConsulting.com or call us at +1 (813) 322-3240 (US) /+1 (416) 646-0651 (Canada).