What is DataOps? And how does it works?

Admin - 23 de September de 2020.

Accelerate the use of data, ensuring the quality and accuracy of information. This is the purpose of DataOps, an increasingly common term in companies and businesses.

What is DataOps?

dataops

DataOps is the orchestrated set of people, technologies, and practices to deliver reliable, high-quality data in an agile manner. The first records of the term are from the IBM Big Data & Analytics Hub, in 2014, referring specifically to practices that bring end-to-end agility to processes that use data.

“DataOps is designed to solve challenges associated with inefficiencies in accessing, preparing, integrating, and making data available,” explains Sara Quoma on the IBM blog.

Why is DataOps becoming more necessary every day?

DataOps provides technological automation to increase teams’ effectiveness and productivity. However, to use its benefits, the Company’s Culture needs to be adapted to the Data-Driven.

In a recent report by Certain, companies saw a 10 to 20% increase in ROI after using data-based strategies. This very profitable investment, and with such a great capacity to move the market, brought with it a complexity of terms like Data Lake, Data-Driven, and Data Science.

Before analyzing DataOps, let’s take a look at some concepts:

Data Lake: “Data Lakes” are stored in raw data repository systems. All information collected, whether on smart devices, cookies, sensors, or programs, is stored in these repositories.
Data-Driven Culture: In addition to technologies and practices, the use of data requires the establishment of a corporate culture that places data at the center of all decisions. This was given the name of Data-Driven Culture, which has different developments for different sectors, such as Marketing and Design.
Data Science: Data science is an interdisciplinary area focused on data analysis, structured and unstructured, which aims to extract knowledge, detect patterns, and/or obtain variables for possible decision making.

As we have seen, handling data is not easy, and this complexity can mean that the information needed to make it useful is not accessed quickly enough. DataOps arises precisely in order to provide agility and coordination to data analysis.

Learn more: How Data-Driven Culture can revolutionize Digital Marketing

How does DataOps work?

DataOps brings together cross-functional teams that focus on diverse skills, such as operations, software engineering, architecture, planning, and management. With data science and data engineering jobs, he invests in collaboration and communication between developers, operations professionals, and data specialists.

For each environment, different processes are required to implement DataOps. Here are some tips to help you deploy DataOps:

Use the right tools

The use of tools is necessary to enable any automation. If a company is considering putting DataOps into practice, it must think of tools for five critical areas:

Data Curation
Metadata management
Data Governance
Master Data Management (MDM)
Self-service interaction

The use of automation tools for these processes will bring immediate progress towards the adoption of DataOps.

Tests on data streams

In a market that demands more and more data quality, it is essential to implement a phase of Data Flow Tests to keep track of errors and verify the need for maintenance.

Code versioning tools

By using versioning tools, it is possible to maintain the organization and encourage automation of code integration and delivery. This is because the tool takes care of organizing and managing the versions of the code. This control mitigates errors and problems during the journey.

Multiple work environments

It is simpler to put DataOps into practice with the use of tools such as containers, computers, and virtual environments. Bringing computing and data storage closer together is important to decrease response time, increase security, among other things.

Data Governance

Governance is necessary throughout the data processing process, from implementation to final documentation. This professional, required by data privacy laws, is increasingly important and the integration of data analysis processes is his main function.