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If your projects lack agility and time-effectiveness, DataOps might be the missing part of the process. Though for the majority DataOps may still seem like a foreign concept, some businesses have started actively implementing DataOps practices to increase agility and stability of their data and analytics teams. Many believe in the misconception that DataOps is just “DevOps for data”, and to the same extent, those notions have the same goals: faster and safer building of complex systems. However, when it comes to data operations processes, the procedure, tools, and requirements of achieving this aim significantly differ from DevOps. Let’s dive deeper into this concept and find out what benefits it brings to organizations that implement it.

What is Data Operations?

DataOps is an automated, process-driven approach to data analytics and data delivery. Its framework consists of 3 variables: agile development, DevOps, and statistical process control.

The initial purpose of DataOps is to scale down the overall time that all development cycles require by using automation technology to streamline the result of the projects. Contrary to traditional data pipeline development, it doesn’t require a lot of time and effort from employees, vice versa, DataOps gives data engineers more time to focus on processes that are vital for business and eliminate the risks of human error. 

DataOps ≠ DevOps for Data

DevOps is a software development approach that is aimed at automating the systems to accelerate the build lifecycle. Focused on constant integration and delivery, DevOps significantly reduces time to deployment, minimizes possible errors, and guarantees faster time-to-market speed. DevOps has enabled companies to drive their software release time to a minimum, so that behemoths like Google, for instance, are able to release software a few times a day.

However, DataOps expands the core principle of DevOps and moves the ball further down in the field of data and analytics. Its main purpose is decreasing the end-to-end cycle time of data analytics from the appearance of ideas to the actual value of data for users. Apart from using top-notch tools to accelerate production, DataOps relies on the agile development principle to ensure that data teams are able to collaborate effectively and fast.

McKinsey Global Institute states that data-driven organizations have 23 times more chances to win customers, 6 times more likely to retain them, and 19 times more likely to become highly profitable. What are the reasons for this “DataOps 2021 trend”?


Benefits of DataOps 

# Real-time insights

DataOps company is centered on a collaborative data management approach. This approach builds a so-called “bridge” among the team which collects the data, the team which analyses it, and the users who take advantage of those findings. All team members have real-time access to any data that allows them to collaborate and solve problems without any delays. What is more, the Statistical Process Control tool, output tests are able to catch incorrectly processed data beforehand, ensuring the reliability of the final result.

# Fast time-to-market

Presenting new features and functions ahead of the competitors is vital if one wants to have an edge over competitors. PwC surveyed 15k customers and found out that 1 in 3 will leave a brand they like after having just one negative experience while 92% stated that they would quit a company they love after 2 unpleasant cases. DataOps helps to speed up delivery times to immediately respond to changes in the market.

# Better customer experience

According to the research held by Gartner, companies that manage to successfully implement customer experience projects start with focusing on how they collect and analyze customer data and feedback. DataOps gives businesses a chance to provide their customers with desired services and items when they need them most, and as soon as possible.

# Strong security and conformance

Security is one of the central tenets of DataOps, as data in any organization includes piles of sensitive information about customers, employees, and business procedures. Centralized analytics development enables organizations to enforce security and governance, and eliminate risks of data leaks. 

The transformation of the data landscape driven by the appearance of Artificial Intelligence and Machine Learning has forced businesses worldwide to implement more advanced tools and techniques across their organizations. Many of them have already integrated DataOps to automate the data flow, ensure better security, and create predictable data delivery processes. For sure, DataOps will continue to evolve in the next few years, bringing agility and stability to data and analytics. Contact us to learn more about the benefits DataOps can bring to your business.

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