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A HandBook Dictionary on DataOps and Its Importance

Big giants like Google and Amazon, release software quite often in a day! Reason? They started to implement DevOps, which helped them improve upon their quality of codes and reduced their product cycles.

Optimizing and releasing codes swiftly was once a pipedream for most of the organizations. However, the end-to-end cycle time has greatly been reduced for the organizations that have already started implementing the practices and making value out of it.

After observing the success of Big Giants, companies want to get into the process ending with -Ops treatment. They want to embrace the revolutionary change the DataOps practices are bringing into the process.

DataOps, under its umbrella, covers Agile methodologies, DevOps, and Lean Manufacturing processes and collaboratively helps in focusing on communication improvement, integration, and automation of the data coming in the data pipeline.

Nick Heudecker, an analyst at Gartner, confirmed that the implementation of DataOps in the Test Environment Management Tool automates the data flow between the managers and consumers.

Additionally, it mitigates the chances of any miscommunication between the makers and the buyers. He further added that it is a people-driven practice first and then a technology-oriented.

The inconsistency, inflexibility, bottlenecks, long cycles, and a waste of time almost becomes negligible with the implementation.

It is observed that nearly 75 % of an employee’s day is unproductive because of unplanned and more work scenarios. It does happen with the organizations that resources are there.

However, they still feel the need to hire more to improve the overall productivity of the process. Such scenarios are a suitable example of poor business processes.

It was quite a surprise for everyone to know when Amazon declared that their team releases 50,000,000 codes every year, while for others, it requires a minimum of 6 months for the data team to deliver a 20-line SQL change.

Imagine the wonders this implementation can bring to your company if followed well.

DataOps helps in making procurement and storage of data efficient and fast. It also gives real-time insights into the large volume of data collected automatically by the tools.

It parallelly works with different processes related to data handling, including the DevSecOps practices and quickens the software release time, thereby improving the quality of products.

Having said that, there are challenges and troubles faced by the management in handling data for quality analysis. If not implemented in the right manner, the collected data loses its value, and the delivery time starts fluctuating.

And this enforces the data management team to remain on their toes and ensure that all the queries are resolved on time, and no process is delayed beyond expectations.

Adding to this, the data in the pipeline is growing, and so is the requirement and expectations from data analytics, scientist, and data-hungry applications.

Also, the data is received in different ways through different platforms that demand more control over the system in order to identify the loopholes.

Some daunting challenges are bad quality and manual processes. Let’s have a look into them briefly.

Bad Data Quality: ●

The entire data loses its credibility if the collection of data is badly performed. The whole program and the team is left in jeopardy if the data formats are different and don’t match with the requirement.

Various data types and formats can lead to errors like duplication of entries, scheme change, and input failures.


When this goes out of hand, it becomes difficult for the team to know the root cause and trace the error. Additionally, constant and regular updates in the data pipeline mess up the situation more.


Coping up with these changes is a tad difficult and time-consuming task for the organizations.

Manual Processes:

Manual integration of testing and analytics is a tedious and time-consuming process. It takes hours and effort to analyze the data and come out with meaningful data insights.

The team involved with the analysis has to commit more and make watchful steps without a single compromisation.

Hence to overcome these challenges, a tool wouldn’t suffice; instead, you need to bring change in the underlying processes involved with the data management.


DataOps, with its agile methodology, helps organizations overcome hurdles and data management complexities without any compromisation. It focuses mainly on data integration, cooperation, collaboration, communication, measurement, and automation. \


This speed of process reduces the life cycle of product delivery and sets up a clear transparent platform for communication between engineers, data scientists, It and the Quality assurance team.

DataOps Implementation:

Just a few minor changes in the ongoing process helps in setting up DataOps effectively into the organization. It mitigates manual errors and efforts, thereby saving a lot of time.

The implementation also notifies the company if any projection is done or any security alert is detected.

It keeps the data intact in high quality and gives ultimate control over statistical processes.

Implementation of these practices keeps the organization’s working culture structured, boost reusability while supporting multi-developer environments.

It also facilitates customized version control over tools and systems, which further will save a lot of development time and also speed up the analytics related to the process.

DataOps provide the utmost flexibility in dealing with the data analytics pipeline. With minimal changes in the processes, DataOps enables getting desired results in the system.

Are you excited to streamlines the processes using class-apart tools and automating the workflow within the organization?

The processes also impact the production environment and keep a check on the quality of data received in the pipeline.

You get live insights into the data and report generation, which enables developers and stakeholders to speed up the process and thereby reduce the product delivery time.

DataOps is a promising phenomenon that evaluates each and every step involved in the process.

Not just a single step, but the whole process continuously participates in making an organization DataOps-compliant.

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A HandBook Dictionary on DataOps and Its Importance  

DataOps provides flexibility in dealing with the data analytics pipeline. Implementation of DataOps in the Test Environment Management Tool...

A HandBook Dictionary on DataOps and Its Importance  

DataOps provides flexibility in dealing with the data analytics pipeline. Implementation of DataOps in the Test Environment Management Tool...

Profile for enov8