Build or buy? This ever-eternal debate is likely taking place in your transformation programmes right now. Deciding which projects to focus your own data engineers and scientists on, and which require buying in the capability is a key decision--one which has the potential to make or break your competitive edge.
Adding to the debate's complexity is the need to implement successful DataOps processes in an era of rapid change. With company-generated data continuing to rise exponentially thanks to connected devices and digital-first policies, the need to implement an effective DataOps process is increasingly vital. Not only to allow the business to manage its ever-increasing data efficiently but also to derive real value from it. However, the art of successful DataOps is complex and, if not properly implemented, can prove a barrier to innovation for many firms.
Here, we'll delve into the build versus buy debate when it comes to DataOps; outlining key considerations to ask at each stage in order to deliver the benefits of an effective DataOps process without the headache, risk and unnecessary impact on profit margin.
First, a note on DataOps
DataOps has become increasingly crucial at a time of change to provide a data management workflow, analytics and artificial intelligence, which functions in tandem with the end goal of DevOps, that is, to allow businesses to provide new features for end users with growing consumer demands while delivering on time-to-market. DataOps involves joining together all the data points across an organisation in order to make sense of the company's world--which is intrinsic to how successfully it can operate. To do this, big decisions must be made from the top down to implement restructuring, taking apart established areas of data analytics.
DataOps involves streamlining data sources, data pipelines and analytics workflows, so that it includes parts of machine learning, allowing businesses to bring data into their decision-making. DataOps involves numerous solutions; from data engineering platforms and orchestration toolsets to data warehousing automation and data science platforms. Some DataOps solutions are built by teams in-house, while others can be bought off the shelf. A third option is to buy a solution and then orchestrate the flow of data and code using the data engineers and analytical tools an organisation already has in place, along with external experts' advice.
As such, the build versus buy, and now...