Spring is traditionally a time to commence Spring cleaning. It’s also a great time for IT teams to look at how to better streamline the development and delivery of data to better support the business operations.
Here is where a DataOps approach to data integration comes into play. Similar to DevOps, DataOps is an emerging set of practices, processes and technologies for building an enhancing data and analytics pipelines to better meet the needs of the business. The promise is that this methodology will improve productivity, streamline and automate processes, increase data output and create greater collaboration across teams.
According to a survey during the recent Information Management webinar “5 Key Requirements to DataOps Success," 52 percent of respondents said they are interested in adopting a DataOps approach and an additional 41 percent are still researching the possibility. With a majority of firms interested in pursuing DataOps, it is clear that greater education is needed to assist in moving this methodology forward and break down the silos between those that own the data, the database administrators and data consumers.
Companies are operating at a fast-pace and having the right information and analysis could mean the difference between leading the market or being one of the many followers. DataOps accelerates time to insight and solves the many challenges associated with data availability.
While data managers, architects and engineers are embracing new cloud and data lake initiatives to provide a more scalable and agile infrastructure, they need to be careful to not make the same mistakes that the majority of first-generation big data projects that failed to deliver real business value and become nothing more than a large landing zone for corporate data.
For organization who operate at this speed of change, they require modern data architectures that allow for the quick use of the ever-expanding volumes of data. These infrastructures – based on hybrid and multi-cloud for greater efficiency – provide enterprises with the agility they need to compete more effectively, improve customer satisfaction and increase operational efficiencies.
When the DataOps methodology is part of these architectures, companies are empowered to support real-time data analytics and collaborative data management approaches while easing the many frustrations associated with access to analytics-ready data.
DataOps is a verb not a noun, it is something you do, not something you buy. It is a discipline that involves people, processes and enabling technology. However, as organizations shift to modern analytics and data management platforms in the cloud, you should also take a hard look at your legacy integration technology to make sure that it can support the key DataOps principles that will accelerate time to insight.
