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You Won’t Scale AI Without Enlisting Non-Experts to the Cause

Dataiku Product, Scaling AI Catie Grasso

2021 was the year that a significant number of organizations had this critical realization. In a variety of ways, the pool of people building and benefiting from AI is expanding and, in this article, we’ll highlight how and in what capacity.

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We’ve observed an influx of roles that are now involved in AI projects — project management and leads, risk managers, subject matter experts (SMEs), annotators, IT operations, and more. So, what’s fueling this diversification of involvement? With the increase in adoption and scale, new players are joining the teams developing, deploying, and managing AI. With success from initial AI projects comes more involvement from business stakeholders who want visibility into projects and potentially even review and sign-off at key steps. Spoiler, if you want to move from theory to practice when it comes to getting more people and roles involved in AI projects, check out how Dataiku 10 can help.

Further, while “traditional” roles like data scientists (and other data teams) are undoubtedly a critical piece of organizational data and AI transformation, we know — and have observed throughout 2021 — that they are not enough to make the difference exclusively by themselves. Specifically, three groups of business stakeholders’ roles are evolving and will continue to evolve in 2022 and beyond:

1. Analysts

There are data roles that have existed for a while and that have always worked with data, such as business analysts. These players are becoming increasingly effective because of factors like the move out of spreadsheets and internal enablement to upskill them into citizen data scientists.

analysts

2. Business Users

Next are the business users who, in the past, may have operated with data, but with low data and analytics maturity (i.e., not analysts, but people such as marketers or supply chain managers). They are increasingly empowered to directly build or co-build analytics workflows with experts because they have more access to data which, combined with upskilling and proper tooling (e.g., Dataiku), leads to faster time to impact.

business users

3. AI Consumers

We’re seeing a growth in the very end users who aren’t necessarily the ones building the solutions or directly working with data, but who are benefitting from it by being surfaced AI tools and applications built by data teams or one of the other groups above. 

Below, we’ll break out some of the trends we are observing among these groups. 

Citizen Data Science Takes Center Stage, Data-Minded People Become More Efficient

Business people, including analysts and domain experts, now have the tools to create production-ready data science and machine learning (ML) projects that can be used to solve real business problems. As data analytics becomes more democratized, companies are starting to consider how citizen data scientists can help them reduce costs and risks. 

A critical part of this equation is to empower citizen data scientists in smart ways. This doesn’t just mean allowing them to crank out models without proper training or understanding of the process such that those models are totally disconnected from the business questions they’re trying to answer. In order to achieve transparency, citizen data scientists need to be equipped with the right tools.

Citizen data scientists rely on end-to-end platforms that contain a powerful automated ML engine. With such tools (like Dataiku), citizen data scientists can optimize and deploy models with minimal intervention. It is important to note, though, that not all data science and ML platforms are created equally in terms of enabling seamless production and value. When considering an end-to-end data science platform, teams need to consider specific features and capabilities — including AutoML for the citizen data science profile.

upskilling

Dataiku upskills business teams while empowering IT to manage production to free data scientists to pursue significant initiatives. For example, Standard Chartered Bank has developed a data marketplace that people across the organization can use when they need to get answers from other datasets (i.e., an analyst trying to understand the cost of property can use the balance sheet from the data marketplace and plug in lease data). 

The model represents a unique take on a self-service data program where the Center of Excellence (CoE) owns the core structured intelligence of the company, but enables other teams to build experiences on top of that data, relevant to their specific function or line of business. As a result, people from various teams around the organization can use shared analytics, datasets, and apps within the enterprise-level data marketplace to get answers to day-to-day business problems, which, therefore, gets more people to use data on a regular basis. 

Empowering More Data-Adjacent People to Use Data

Early on, these people (who aren’t analysts but might be starting to incorporate data into their day-to-day work such as marketers, engineers, and technicians) might need to work with the data science team or the analysts mentioned in group one under more of a CoE model but, eventually, they are empowered to work with data and even build models on their own. 

Dataiku customer NXP, one of the largest semiconductor suppliers in the world, has seen great success empowering these exact people with its Citizen Data Science Program. Available to anyone at the company to elevate his or her competencies and skills around data science, the four-month program drives collaboration, upskilling, and self-service analytics at NXP by improving advanced analytics competency and data literacy among non-data professionals, addressing the challenge of solving business problems which have increasing complexity not served by legacy BI tools/methods, and positioning their business leaders to make better, more informed decisions. 

AI Consumers Benefit, Too

AI consumers might not know they’re using AI, but it’s making their lives better by drastically improving their day-to-day work and decision making. With Dataiku, for example, these stakeholders engage with AI systems in the context of day-to-day work as a part of existing workflows, tools, and technologies. They can use dashboards that automatically update with the latest data for accurate KPI and value tracking and get predictive insights with custom visualizations and applications to make better everyday decisions.

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