Can I outsource my Industrial DataOps?

If you are curious on how to get the most out of your Cognite Data Fusion subscription, you have come to the right place. This is part of a series of posts where we share some of our experience from working with customers in their journey towards an Industrial DataOps organization. We want to share lessons learned, mistakes made, good practices observed, and observations of pitfalls and risks. This is not the absolute truth, but hopefully a way to spark good discussions around an inherently complex topic!

To quickly introduce ourselves, we are @Arjo Oosten, Digital Transformation Leader, winter sport addict and passionate about driving hands-on digital growth strategies and value based decision making, and @Karolina Luna, Solution Architect, cat lover, and passionate about the lifecycle perspective of everything (like solutions and data products). 

To learn more about Cognite Data Fusion, we recommend this post.

Outsourcing FTW?

As many companies have found, it can be hard to attract the right technical talent. It may not be worthwhile to invest in all types of skills internally either and outsourcing of DataOps and digitalization-related tasks happens in all organizations. The traditional method has been to hire consultants to develop what is needed at the customer location and on their infrastructure, with resources contracted over time for operations and maintenance. While still not common, we have seen increasing requests for, and more suppliers offering, development and operations as a service. The service comes with a more predictable cost and under some service level agreement, providing better incentive models for solutions that are stable and easy to maintain and operate. A potential downside can be that outsourcing in this way may reduce flexibility with respect to making changes to the data product, since these may need to be submitted through a change request and the consultancy would need to allocate resources to execute the change, whereas an internal team or a in-house team staffed with consultants may be more flexible to handling changing priorities. 

While outsourcing some roles can align very well with strategic choices at your company, there are some roles that we strongly believe should never be considered for outsourcing. These are the data product owner and similar roles that have the key understanding of your data and sets the direction for the data product development. The key reason for this is that you are and will always be the experts on your own data, which is a core asset.

What are your lessons learned when it comes to outsourcing and DataOps?

Previous posts in the series

• Customer & Cognite operational responsibilities
• Roles, tasks and skill sets for development and operations 
• Cross-functional Industrial DataOps 
• Customer operating model examples for Industrial DataOps