How to assemble your AIOps team

AIOps is no longer seemingly science fiction, but rather a reality of how we innovate today. Some organizations might not know where to begin - but getting the right team in place is your first best bet. Here's how.

The unicorn

A team for an AIOps project can be as small as one person, a data scientist who wears many hats. That's what we call a unicorn-someone who has mastered every step and role of an enterprise data science team.

Enterprise-scale teams

Enterprise production AI teams normally have the roles of data scientist, data engineer, system architect, business analyst, data subject matter expert, and business sponsor. Product owner, product manager, program manager, and scrum expert are also roles that work within the defined software development lifecycle.

Teamwork makes the dream work

The data scientist, data engineer, and business sponsor must have a good systematic working relationship to understand the business problem. It's equally important for teams to have a solutions-oriented mindset that seeks opportunities through new challenges and works through iterations to solve the problem with the available (or lack of available) data.

Data engineering is at the core

The majority of data science formal course work focuses on machine techniques; data engineering is often thought of as someone else's problem. 80% of the work in getting an AI project off the ground to solving problems is in the data engineering, which includes knowing what data is available and isn't available and how to access it in a usable form for data science.

Focus on business results

The clarity of the desired business result between the business sponsor, data scientist, and data engineer is critical to keeping direction for the AI team in meeting the business outcome. Clarity is also important in knowing when to fail fast and look for other data or other problems to solve.

At NetApp^®, our business focus for AIOps is to save time for our customers so they can focus on their business. This is the process we've taken to build out our AIOps teams to provide the insights, prescriptions, and automations that help to make our hybrid cloud data fabric a reality.

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