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Putting into production business use cases that are based on data often comes up against organizational, technological or data complexities. The DataOps approach aims to prepare and secure the industrialization of use cases via an adapted methodology.

What is DataOps?

To understand DataOps, we need to redefine what Data Science and DevOps are. Data Science combines scientific methodology, statistics, artificial intelligence, and data analysis to extract and deliver information. The DevOps methodology aims to bring together developers (Dev) and IT operations teams (Ops) to optimise the application lifecycle and deliver better quality IT products. For DataOps, the collaboration of Data science and Ops teams results in an agile, data analysis oriented methodology.


The benefits of DataOps

DataOps is a collaborative data management methodology that aims to improve the communication, integration and automation of data flows between data managers and data consumers within an organisation. The main goal of DataOps is to increase the efficiency and speed of data processing so that data can be used and analysed quickly by companies. The main advantages of this approach lie in reducing risks and improving the efficiency of data-related processes. In short, DataOps considerably improves operational results thanks to better agility between data, processes and people.


DataOps as an accelerator of digital transformation

Access to data is a crucial issue for companies. The growing demand and exponential volumes of data are forcing companies to focus on the digital transformations required to meet evolving customer needs. This is where DataOps plays an important role as it targets technological blockers by driving innovation and process optimisation in data analysis.

By relying on this approach, which is the result of the combination of several expertises, such as:

  • Data culture
  • Agile methodology
  • DevOps
  • Process optimization (Lean)

Sia Partners supports CDOs and CIOs in the industrialisation of POCs and in the implementation of a collaborative and "Data Driven" culture within business, Data and IS teams.