What is DataOps? Why Is It Important In Enterprise Data Management?
Many companies are facing the problem that they do not yet know how to implement their enterprise-scale data platforms. This inability to make their business data-driven limits their success in achieving strategic objectives.
This is where the concept of DataOps comes in, a practice that automates the delivery of data with an appropriate level of security and quality to enhance the value of data in a dynamic environment. Below, we discuss its importance in data analytics initiatives and as a driver of value in a business.
What is DataOps?
DataOps is a collaborative data management practice that focuses on the integration and automation of data flows between all data-consuming parts of an enterprise.
Therefore, DataOps could be defined as an agile methodology that aims to optimize the design, development, and maintenance of data-driven applications and their analysis. DataOps helps to generate high-quality collaborative and scalable channels for a large number of use cases in an enterprise.
DataOps vs. DevOps
In recent years, we have seen how the DevOps concept and its ‘Ops’ variants (MLOps, ModelOps, or AIOps) have gained more and more strength. DevOps was born in 2000 and is based on three main principles: integration, delivery, and continuous deployment.
DevOps was born with the objective of uniting development and systems management. And, despite its complexity, it has been able to integrate both scenarios and consolidate itself as a fundamental methodology when considering development.
DataOps was born more recently from the DevOps philosophy and combines Lean Thinking, but totally focused on data analytics to achieve automation, speed, and accuracy in the data processing. In other words, while DevOps focuses on technology, DataOps focuses on data and the value extracted from it. The technological layer on which they are based takes a back seat.
On the other hand, with both DevOps and DataOps, companies must rethink the entire problem, goals included. DevOps broadens the scope of the problem and sees it as a development and/or operations problem. DataOps does the same by reframing the data processing flow throughout the execution. However, DataOps encompasses more groups within an organization, as all parties depend on the data.
In fact, with DataOps you have the implementation of production and data processing flows to execute these and train data models.
Types of DataOps
When we talk about DataOps types, we are referring to the tools that enable the correct operation of this methodology.
Many are directly inherited from DevOps and allow the execution of automated regression tests between new versions of reports to speed up the testing processes. Therefore, the use of Business Intelligence is very important for the constant updating of data for each project.
On the other hand, we find the use of Machine Learning algorithms and models, which, applied to the data, allow us to obtain the best results thanks to an automated system.
Finally, another fundamental tool in this whole process is the cloud. More and more companies are taking advantage of the benefits of having all their data in a flexible, scalable, and real-time storage.
The term DataOps was introduced by Lenny Liebmann in 2014 to explain the need for a practice that would allow for a greater likelihood of success for Big Data initiatives.
The reasons? The very negative results that companies were showing when it came to data. For example, in 2016 Gartner estimated that nearly 60% of Big Data projects failed. In 2017, instead of this figure dropping, it rose to 85%. And, by 2021, a Harvard Business Review survey showed that only 24% of companies gave the results of their analytics initiatives a 7 or higher. This is a very worrying figure.
As a result of these studies, a very important global need has been identified, where the mindset of companies must change completely if they want to drive the necessary changes in matters of definition and execution. They need data to be not merely stored, but also managed and displayed in reports and dashboards so that it becomes a dynamic element of corporate operations.
Therefore, when implementing a DataOps methodology, there are five main steps to consider in order to achieve the full potential of data:
- Evaluate and adjust the technology portfolio and processes to eliminate redundancy and consolidate team control.
- Once consolidated, encourage sharing and reduction of weaknesses that hinder collaboration.
- Now, integrate DataOps practices into teams and data channels.
- Automate data processing flows to make them more efficient.
- Empower data consumers to serve themselves by unleashing their full potential of information and knowledge.
The Benefits of Implementing DataOps
Many companies find that they have problems related to their data and how to get the most out of it. DataOps is one of the keys to promoting changes that help achieve a company’s goals. Some advantages are:
- Avoid duplication of data: facilitates the creation of more accessible, quality, and operationally available data products.
- Data strategy development: facilitates the collaboration of teams at all stages to make data available in less time.
- Improved data analytics: thanks to the use of machine learning algorithms, large amounts of data can be collected, processed, and analyzed.
- Operational efficiency: efficiency, agility, security, and transformational change are optimized.
- Advanced processes: by adopting DataOps, enterprises move faster in terms of transitioning to the cloud and executing digital transformation strategies.
- Support for automation technologies: by eliminating time spent on manual tasks and automating them, quality data provisioning is achieved.
DataOps is a very useful tool, but it is important to have a clear understanding of the needs and objectives of each organization. That is why it is so important to have a partner that gives us that perspective to ensure the success of the investment. At Plain Concepts, we help you to promote the right changes. Contact us!