Measuring ROI for AI Efforts

Data ROI calculations are especially challenging since data efforts empower so many aspects of an organization to operate more effectively and efficiently. It’s often difficult to isolate the contribution of data alone to improvements, especially larger business outcomes (like higher profit margins, lower costs, etc.). While data teams can’t take sole credit for organizational wins like this, finding concrete wins is often the easiest way to calculate ROI.

McKinsey estimates that analytics will potentially unlock $9.5 trillion to $15.4 trillion in value annually, with AI activating about 40 percent of that (between $3.5 trillion and $5.8 trillion)

IIA Report: Analytics Maturity Powers Company Performance

ROI of Data Tools

While the factors that contribute to ROI calculations are unique to each organization, there are several that remain relatively consistent. For a data platform integration, they include:

• Cost of educating the data team: With any new tool, you’ll have a temporary cost to consider in terms of team education and onboarding. However, it’s important not to skimp out on education to ensure that the team will be fully activated.

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• Cost of tool: The tool you use will also come with associated licensing costs, however, ROI calculations can help you determine whether this is a good investment or not.
• Activation of analysts: When data analysts can be activated to fully collaborate and leverage their expertise into a data team, project operationalization will occur much faster.
• Decreased strain on IT resources: IT teams are often overburdened with data access requests. Minimizing this workload can increase data and IT team productivity.

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ROI & Dataiku

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. Hundreds of companies use Dataiku daily to provide tangible ROI for AI and data initiatives by:

• More quickly getting models to production with features like one-click model deployment.
• Speeding up time to value through features like AutoML and built-in, pre-trained deep learning models.
• Leveraging visual, point-and-click features in addition to robust coding features so that business analysts, data scientists, data engineers, IT, and more can all work together more efficiently to deliver value.
• Using integrated advanced processors and other data preparation features for faster ETL.