Yes, you read right. DataOps just like we have DevOps, we also need DataOps in Big data. According to Gartner “Data ops is the hub for collecting and distributing data, with a mandate to provide controlled access to systems of record for customer and marketing performance data, while protecting privacy, usage restrictions, and data integrity.”
DataOps = Agile + DevOps + Lean Manufacturing
In my opinion, I don’t think we place enough emphasis on this area. The Hype Cycle For Data Management, 2018 has DataOps on the rise, hence analytics leaders have to strategize a way to keep up with these emerging data management technologies. DataOps involves delivering high-quality projects at a fast rate. In the age of agile methodology, we need to be able to meet customer needs. There is a Gartner report that says 50% of the all analytic projects fail to meet customer requirements.
Thinking about breaking into Big Data? DataOps is another option. So if you are already part of an operations team, you most likely have some of the skills required. You might be wondering don’t I need all of the Machine Learning, Deep Learning, Python that the Data Scientist and Data Engineers require? Not quite. The mistake most companies make is interlocking roles in Big Data, as essential as it is to collaborate it’s best we leave data engineering or data scientist out of data operations.
Please share your opinion. I would love to learn more as this is relatively new.