What is DataOps and how does it improve data anlaysis

DataOps is the newest agile methodology used to increase the accuracy and speed of data analysis, learn more with Selerity.

If you are familiar with the word DevOps, then you may have heard about DataOps. The new methodology for squeezing the most value out of your data. As a data expert of more than 20 years, I have seen the industry evolve significantly in the way it collects and processes big data to keep pace with technological developments. Today, the rise of big data, the potential of IoT and advances in analytics are inspiring data analysts to seek out new methods of collecting and analysing data. Therefore, I am going to explain what exactly DataOps is and how it improves data analysis.

What is Dataops?

As mentioned before DataOps is an offshoot of DevOps. It is the latest agile operations methodology used by data professionals to improve data quality, data access, data integration, automation, deployment, data management practices, as well as the speed and accuracy of data analysis.

If you are familiar with DevOps, then you will know that it is a collaboration method between software development professionals who place focus on agility and responsiveness. DataOps is very similar, it involves the collaboration between the data professionals and is focused on making data analysis more responsive and agile. It is focused on improving and optimising all steps of the data analysis process from storage to performance optimisation.

How does data analysis improve?

DataOps provides benefits to all parties involved. Organisations that adopt this methodology have been known to outperform their competitors, which explains why the likes of Facebook and Netflix have embraced this method to execute data analysis. Thanks to DataOps, organisations can yield more data and improve the quality of data analytics. Thus, yielding better and more accurate insights.

DataOps improves strategic management practices because it encourages data scientists, data engineers and technologists to work together to gain better insights into data and improve its value. With DataOps, developers are encouraged to take on the latest technology, like machine learning because a huge part of DataOps is improving the speed and process of data analysis. This leads to more efficient processes, which means discovering new insight at a faster rate.

When DataOps is successfully implemented, it allows teams of data professionals to work faster and do more in less time. They are in a better position to respond to requests, which means they can respond to real-time goals set by the organisation.

The quality of data improves because many vital functions like data quality assurance will be automated and statistical process control (SPC). Quality improves because it even shortens the amount of time dedicated to identifying and fixing bugs or defects.

How do you setup DataOps?

Data analysis benefits tremendously from DataOps, so how do you implement a DataOps strategy? Because it involves so many steps in the analysis process, you need to implement changes across the board. It involves the democratisation of data, leveraging open source platforms and automation.

Democratise your data

Data analysis benefits from the democratisation of data. Having data in silos serves as a bottleneck for innovation and improvement. The necessity occurs because chief data officers believe that business leaders are demanding more data to aid in decision-making. In addition, providing different departments access to data encourages collaboration and access across the board, which leads to better results. An excellent example is Facebook, the social media giant was suffering a bottleneck in innovation until they moved to a different data analytics platform.

Automate

A key part of DataOps is speed and to accomplish that they need to automate many areas, like data analytics pipeline monitoring and quality assurance testing. These areas tend to take a long time and are manually intensive. Automation allows for self-sufficiency to deploy models as APIs allows engineers to integrate code without needing to restructure it, which improves productivity.

DataOps and better data analysis

DataOps refers to a new methodology for collecting, storing and analysing information. The new methodology is based on agility, responsiveness and collaboration. DataOps draws much of its inspiration from DevOps which was an agile method of software development. DataOps bring several benefits to data analysis, allowing data analysts to glean more data, accelerate the rate of work and improve the accuracy and quality of findings. To execute DataOps, organisations need to make changes to the processes across all stages. The changes consist of several steps like the democratisation of data and automation.

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