Machine Learning For The Rest Of Us: How To Cultivate The Citizen Data Scientist

Today’s IT environments present unprecedented challenges for IT teams. Doing more of the same won’t cut it if IT teams are to address all of the urgent imperatives of their organizations. One of the ways IT teams can address their increasing demands is through the use of artificial intelligence for IT operations (AIOps).

As outlined in our prior post, AIOps can yield significant benefits and have fundamental implications for people, processes and technology. However, like machine learning more generally, the true promise of AIOps will only be realized when it moves out of labs and into the business.

While AIOps is emerging as a key imperative for many organizations, the reality is that many haven’t broadly deployed or fully operationalized AIOps, meaning its vast potential isn’t being fully realized.

A Methodology For Mass AIOps Adoption: The Citizen Data Scientist

For some time now, Gartner has been reporting on the concept of the citizen data scientist. At a high level, this approach refers to capabilities and practices that allow users to extract insights from data without needing to be as skilled and technically sophisticated as expert data scientists.

This is an important objective because it only continues to get more critical for businesses to leverage data science to compete and realize enhanced business results. However, around the world, expert data scientists are in short supply, and it looks like that will remain the case for quite some time to come.

In a number of reports, Gartner offers insights for cultivating the development of citizen data scientists. By establishing processes and capabilities in support of the citizen data scientist, organizations can more quickly unleash the power of machine learning and artificial intelligence to fuel business gains. While this citizen data scientist concept applies to data science in general, it’s very well aligned with AIOps more specifically.

Fully Leveraging AIOps: Four Keys To Success

As teams set out to cultivate the development and support of the citizen data scientist in their organizations, they’ll be well-served by applying a number of core principles. Most fundamentally, they’ll need to develop processes and frameworks that enable consistent use and sharing of data across the organization. This is fundamental to data science and to AIOps. When it comes to maximizing the utility of AIOps across the organization, here are four key recommendations to follow:

1. Expand the variety of data accessible for analysis. To make the most of AIOps, the widest range of data sources and types needs to be aggregated. When considering an AIOps implementation, teams are therefore well-served by taking an all-of-the-above approach. This includes aggregating and correlating data from across environments, architectural layers, data types and technology domains.

2. Increase the range of analytics capabilities available. Today’s teams need tools that offer maximum support in facilitating analytics. This includes leveraging capabilities for data preparation and self-service analytics. Teams need unified tools that offer a complete set of prepackaged capabilities, such as dashboards and interfaces, that make it fast and efficient to normalize and aggregate disparate data sets.

3. Make advanced analytics accessible to a wider audience. To foster maximum success across an organization, AIOps solutions must enable the broadest number of business users to leverage machine learning, without requiring the assistance of expert data scientists. Toward this end, it is important to leverage prepackaged algorithms and unified capabilities. With these capabilities, advanced AIOps solutions make machine learning much more broadly accessible.

4. Maximize analytics agility. It is important to recognize that the analytics life cycle is composed of a number of essential steps, including acquiring, organizing, analyzing, delivering and measuring data. It is vital that advanced AIOps platforms offer strong support for each of these areas. The more teams can leverage a single unified platform for all these efforts, the better equipped they’ll be to gain optimized agility and operational efficiency in their AIOps administration.


The demand for AIOps is high, and intensifying. In order to maximize the benefits of AIOps, however, these capabilities can’t be restricted to the select few. When teams align their AIOps implementation with the cultivation and support of citizen data scientists, they’ll be poised to establish the pervasive AIOps capabilities that fuel optimized efficiency and service levels.



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