From delivering personalized customer experiences and optimizing supply chains in manufacturing to detecting fraudulent activities, the common thread in many initiatives is advanced analytics. Over the years, 90% of businesses have recognized the value of advanced analytics and have started to put internal analytics organizations in place with the aim of scaling use cases, according to McKinsey. However, most often, they struggle to scale their analytics initiatives, as they fail to collaborate well and adopt AI/ML.
Business success depends on harnessing data well and creating trust in that data to accelerate an organization’s journey to AI. Here, DataOps can prove to be a game changer. Just as DevOps took the software industry by storm, enabling collaborative development and testing of apps across the value chain, DataOps fuels collaboration across teams for continuous and faster innovation across the enterprise. Gartner defines DataOps as “a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization.”
How can DataOps help your organization? Let’s look at four main points:
DataOps promotes collaboration and reduces the cycle time of data analytics. DataOps can help improve decision-making by providing a more comprehensive view of the data that an organization has. By bringing together data from different sources and providing tools for analyzing and visualizing that data, DataOps can help teams better understand the data they are working with to make more informed decisions.
A streamlined data flow between CXOs and data analysts accelerates time to value from data and empowers organizations to make business decisions based on results. Teams can also provide feedback on pipeline development, leading to the tailored insights required to boost revenue. DataOps focuses on automating and streamlining many of the processes involved in managing data to increase efficiency, which can help build trust among team members.
A mature DataOps strategy brings automation to data transformation, which reduces time-consuming and error-prone steps in the pipeline, thereby improving analytics operations and performance. As it reduces manual processes and offers business-ready data, the productivity of data teams goes up, as they can focus on higher value-add tasks for advanced AI and ML tasks that make production faster.
Agile development techniques in DataOps are especially useful in accelerating time to value and offer rapid response to constantly changing demands on data pipelines. This drastically reduces the time spent on maintaining operational analytics, as well as updating and improving current analytics with the least amount of labor.
Setting up data pipelines is a fairly complex challenge, as it requires continuous optimization, updates and maintenance. DataOps enables the continuous delivery of data, as well as the smooth execution of data pipelines. Enterprises can easily transition from on-prem to the cloud with a robust DevOps strategy.
