DataOps stems from the root of Agile Methodology and is an automated process that is process-oriented. DataOps is mainly used by the Analytics and Data teams to help in improving the quality of data analytics and also to reduce the duration of the life cycle. Initially, DataOps was introduced as a set of best practices, but has now advanced to be an integral part of data analytics and can be considered as an independent approach to data analytics. As a professional in the field of data, you need to understand more about this term. It also promotes smooth communication between the IT operations team and the analytics team in any organisation. Streamlining the way that data is managed and how products are created using this data is one of the main functions of DataOps. 

DataOps Objectives

1. Data stack should be reproducible

Let’s assume that you lose all the datasets that you have worked on in the previous month. How will you recreate them? Will it be easy to recreate them? Let’s say that you had the code in the source control. Then, it is easier to do so. However, if the datasets are built with Python scripts, notebooks or SQL queries, it is much more difficult to do so. 

2. Data definitions must be centralised, discoverable, and shared

The various departments and teams in an organisation must be able to find the code or the query generated in a dataset, model, or a dashboard. Thus, ensuring that the data definitions are centralised, discoverable, and shared is important. Everyone must be able to collaborate via the datasets and ensure team growth. 

3. Production data should be isolated

Since the production data is highly important, it is essential to keep the development data clear of production. Ideally, the production dataset will change as a result of the source-control being updated. 

4. Change your data definitions quickly

For any data-driven company, it is essential to make use of new software as soon as they are introduced. Product reporting requirement should not be a hurdle, and it must be possible to change the data definition and update it within a couple of hours or at the latest, within a few days. 

5. Know if your data pipeline breaks 

Knowing whether a script breaks, if your data pipeline stops working, or if the quality of data has been impacted is essential to maintain a data-driven culture. 

7. Automate manual processes 

If your organisation automates any manual work, it becomes easier to reduce the workload and to reduce mental stress on any of the teams that are involved in the work. Automating also helps in freeing up time and reduces the chances of human error as well. 

8. Data access should be controlled

Depending on the size of your organisation, controlling the data access may or may not be an issue. In the case of larger organisations where there are over a hundred employees, controlling data access is a necessity since data is highly-sensitive. 

Also Read: Unthinkable Trends to watch out for in the Cloud Computing Market in 2021

How can we achieve these objectives? 

  1. Source control everything
  2. Automate all aspects that can be automated
  3. Write tests for your data
  4. Avoid data mutation
  5. Use a development environment

Benefits of DataOps

One of the main goals of DataOps is to build a collaborative environment between IT operations and data scientists while each of them is working towards intelligently leveraging the data. We have a large amount of data available to us today, and ensuring that this data is used to its full potential is important to gain a better understanding and insight, to come up with better solutions and also gain greater profits. Let us now take a look at a few benefits of DataOps. 

1. Data Problem/Solving Capabilities

With the advent of the internet, and as we have entered the digital age, the amount of data being generated daily is increasing rapidly. It is said that the data created is doubling every twelve to eighteen months. With the help of DataOps, we will be able to convert this raw data into actionable information quickly and efficiently. 

2. Enhanced Data Analytics

The use of multifaceted analytics techniques is promoted in DataOps. Machine learning algorithms that can help in guiding data through the various stages of analytics are used. Machine learning algorithms also help in collecting, processing, and classifying the data before it is delivered to the customers. The suggestions or feedback from the customers is also given quickly. 

3. Finding New Opportunities

The entire work process within an organisation can be changed with the help of DataOps as it provides a greater amount of flexibility. New opportunities are presented to us as the priorities shift and a new ecosystem is created that has no borders or barriers between the different departments in an organisation. Data engineers, data analysts, developers, operations managers, and marketers are now able to collaborate in real-time and to plan and organise ways in which corporate goals can be achieved. Through this, response time is accelerated, and the organisation can also provide better customer service. 

4. Providing Long-term Guidance

The practice of strategic data management is promoted through DataOps. Multiple groups work towards negotiating the needs of the clients and work towards organising, evaluating and studying the data and the feedback given by the customers. Automating processes helps us in making the business more efficient and effective and thus providing long-term guidance. It can be considered as a two-way street between the data users and the data sources. 


This brings us to the end of the blog on DataOps. Inculcating DataOps into your organisation need not be a difficult task. It can have a great impact on your organisation to follow a data-driven approach. If you wish to learn more about this concept and also learn about other Cloud Computing concepts such as AWS, join Great Learning’s PG Cloud Computing Course and upskill today! Feel free to leave your queries below. 

Also Read:
Career Opportunities for DevOps Professionals in India
What are the career prospects for a DevOps Engineer?



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