Do you know what DataOps is? Like  DevOps, it is an agile methodology used by data experts. In fact, mainly in the area of Information Technology (IT), the application of agile methods is very common to organize the scope of work when dividing projects into delivery cycles.

When we talk about data, the use of agile methodologies makes a lot of sense. After all, how to handle a huge amount of data and still be able to deliver analytical insights continuously and efficiently? With  DataOps, you can quickly orchestrate data, code, tools, and environments from start to finish.

Continue reading to learn more about the application of this concept and check out numbers that indicate how critical it is to the company.

What is DataOps?

In general, DataOps is a data-driven methodology, combined with agile practices, which acts as a bridge between data science, infrastructure,  business intelligence  (BI), and operations. It takes into account communication, collaboration, integration, and automation between data engineers, scientists, and other professionals in the field.

The concept is part of the principles applied in DevOps, which is focused on agile methods for development teams. In the case of DataOps, what counts is the data, or “data silos”, as the decentralized databases are called.

Through a set of good practices, the methodology is used in order to make data management more efficient throughout the process. With them, it is possible to dilute any barriers or complications between the areas of development and analytical operations.

What are the phases of DataOps?

For you to better understand what  DataOps is, let’s talk about the phases that make up the process. It starts in data acquisition, goes through storage and monitoring of quality and performance, then comes the battery of tests, predictive analysis, and improvements, in a stream of continuous and orchestrated deliveries. In short, it looks like this:

Analysis > Development > Orchestration > > Delivery > > Administration

The first phase is data analysis, which goes through development until you reach the orchestration part, one of the most important steps in the process. At first, it organizes the data, handles exceptions, and distributes between upcoming streams.

From there, the tests come until they reach the delivery. Then it returns to the second moment of orchestration, which makes the same organization monitor error control in post-production testing. Finally, the last phase is administration, which aims to optimize the use of resources.

Why is the concept fundamental to the company?

In addition to concept, DataOps is a cultural practice that values the importance of using data to improve processes within the organization. Indirectly, the methodology also generates impact for people, who are surrounded by a more cohesive organizational climate and with well-defined work scopes. Like this:

  • the area gains more intelligence;
  • the working day becomes collaborative and unified;
  • the processes are automated, which results in greater agility;
  • the communication channel becomes more open.

Since DataOps allows you to align all innovation and data project teams, the feature is critical to give a competitive advantage to any IT area and keep all areas in full tune. This goes for all hierarchies, from CEO to project leaders and developers.

We talked about the possibility of combining the use of DataOps with agile methods (such as Scrum or  Kanban), but applications do not stop here. Technologies such as Artificial Intelligence (AI), Machine Learning,   and Big Data can also be used together to give it even more business intelligence in real-time.

The reason for this is that they all extract data to generate insights and more income for strategic decisions. They can help with predictive analytics, transaction processing, and many other internal processes through an integrated methodology.

What do the numbers about DataOps say?

The DataOps methodology has the potential to solve gaps between areas, that is, connect different sectors, and allow everyone to extract more value with data. And since we’re talking about data and opportunities, shall we see some numbers?

According to a report by the Eckerson Group, which spoke with 175 BI directors and managers in April 2019 (via email and social media), the methodology brought some benefits:

  • 60% of the areas that applied the approach gained speed in the implementation cycle;
  • 50% of the interviewees stated that there was more agility in deliveries;
  • 48% believe that access to new data sources has been facilitated;
  • 47% were able to meet customer adjustments in a more agile way.

It’s important to note that today’s data delivery needs to be as fast and effective as a Google search. No for nothing, there is the saying “time is money.” This comes in handy in the idea we want to pass on what  DataOps is and its importance to organizations, in particular IT areas.

We live in the age of digital transformation, where most things can be done from anywhere with the mobility of a mobile phone or tablet. This ease of access also contributes to a mindset that prioritizes agility. That is, if you are able to deliver fast and with quality, you will already be one step ahead of everyone.

In summary, the area gains credibility within (teams) and outside (customers) of the company. After all, the organization can deliver what everyone needs and when they need it, thanks to the use of data in an orchestrated way, integrating all the sectors involved.

Each sector has its particularities and requires distinct implementations. But the fact is that the DataOps methodology brings benefits to your team by working to bring different areas in IT together through data. Not to mention the competitive factor, which paves the way for the organization to become a benchmark in strategic decisions based on data analysis, combined with techniques such as AI,  Big Data or Machine Learning.

Have you learned what DataOps is? Take the opportunity to understand more about the importance of agile methodology for companies.