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  /  Big Data   /  How Data analytics will be Conducive for DevOps?

How Data analytics will be Conducive for DevOps?

The collaboration of AI-based application and software helps in developing analytics-based product.

Data is the buzz word these days. For any business to be successful insightful data is imperative. And while businesses are executing data to learn about customer’s behavior, another area experts are optimistic about is DevOps. Integrating data analytics in the DevOps can be a game-changer in the use of analytics to create new products. This concept is still evolving, but software companies like TIBCO have utilized it for tracking COVID 19 and helping businesses.

This approach involves understanding customers’ ideas and assisting them to chalk out a digital strategy for the creation of the desired product. The data analysts must also aid the customers in considering the possible data sources that can be used. Once this process is done the product managers can aid in formulating a strategy that will enhance the product performance. All this is heavily governed by inputs of the customers. After data is created, it can be used to transform the software into the DevOps team, along with creating value-based products for the customers.

 

Collecting the Data 

For value-based delivery, collection of the data must not be limited to the particular source. As DevOps will require extensive data for integration in the software, data scientists must look for individual options to collect the data.

This approach is utilized by the software company TIBCO while creating an analytics app to track COVID 19 activities and support businesses. Since the data available from John Hopkins was limited, the company resorted to getting data from individual sources.

 

Building Different Teams within DevOps and Data analytics 

Data analytics and DevOps are the two branches with a limited acquaintance to each other. The teams are unaware of the processes, functioning and programming in the two specialties. To reap maximum benefits of this collaboration, organizations must opt for cross-pollination between the two specialties. This will help both the teams to understand the different processes in developing analytics-based products. It will be conducive in mitigating the data errors while developing the products.

Moreover, to track the progress of the product a control centre can be created. For example, in the retail business, data can be collected for creating the user-based application from individual sources. Data analysts can draw the analysis and the DevOps team can start creating the product. The control centre can be used for tracking the progress of the product and storing the available data in a cloud-based application, thus making the entire process a DevOps-led project.

TIBCO utilized the same approach to create an application TIBCO Gather Smart for tracking COVID 19 hotspots, involving different Volunteers. They created a control centre to assist people for symptom tracking. Once this was done, the information was stored in a cloud-based application.

 

Conclusion

Experts say that the foundational elements of artificial intelligence should be kept in focus for developing analytics-based products. This includes understanding the AI foundational aspect of the product, cloud-native part and data collection through open-sourcing. Moreover, the cross-fertilization between the DevOps team and data analytics team also accelerates product delivery. Since data is collected from different sources, the data analysts and product managers would require being cautious about the unsegregated data and the errors in analytics during product delivery.

As data is becoming highly democratized, the involvement of data analytics in product development has a positive outlook.