Gartner indicates that the evaluation of financial impacts stemming from AI teams has reached a critical point.
In Brief
The challenge of measuring the economic impacts of Data, AI, and Machine Learning teams has become paramount.
In the context of AI's bright future, the discussion often revolves around the remarkable outcomes from data analysis and the actual value these teams can deliver. Have you noticed any real-world examples of financial benefits derived from data analysis initiatives? Such inquiries often lead to mixed responses. Consequently, Gartner has engaged in dialogue regarding the evaluation challenges faced by data teams during their prominent event. conference on data and analytics this year.
As per Gartner's findings, since 1975, there has been a notable decrease in the percentage of organizations that assess the concrete financial benefits of data analytics initiatives, such as revenue increase, cost savings, enhanced productivity, and risk mitigation. By 2020, over 90% of data investments (dramatically up from just 17% in 1975) were predominantly defended by so-called strategic objectives like fostering innovation, viewing data as an asset, and enhancing brand equity.
This opens up a wealth of discussion on the 'how' and 'why' of our current situation and what the future may hold, particularly with economic clouds gathering on the horizon.
Why has the trend formed?
Justifying the outcomes of data analysis through strategic goals has, in many instances, become the norm. The progress witnessed within the industry over the past few years is too significant for anyone to overlook. ChatGPT This moment serves as a wakeup call for any skeptics left. As we approach pivotal changes, no organization aiming for survival can afford to lag behind.
Sometimes companies justify their data initiatives purely on strategic grounds when they're not fully aware of the real financial benefits these investments could yield or how to quantify them. Many organizations allocate substantial budgets to improve their operations through data, yet skimp on developing proper methodologies for assessing these projects' impacts (such as AB testing and post-implementation analysis). Consequently, they become increasingly ensnared in uncertainty with each new project, heightening the risk that their entire data-driven endeavors could face collapse or that their data teams may become over-inflated without a clear understanding of their effectiveness.
On the flip side, implementing these assessment methodologies has consistently led to significant outcomes across data initiatives.
What will happen next?
One downside is the escalating exposure of data teams amidst challenging economic conditions globally. If 90% of the outcomes attributed to certain teams remain intangible, rooted in an uncertain future, it’s likely that they will be hit hardest during economic downturns. This troubling trend became evident in 2022, as early indicators emerged. layoffs in large companies.
On a positive note, there's a growing enthusiasm for assessing actual financial impacts. In light of all these factors, we anticipate a shift in 2024-2025, where more investments will start being backed by tangible financial outcomes.
This shift signals a surge in interest towards methodologies such as Reliable Machine Learning: focusing on how to structure data teams to ensure their work yields measurable and financially rewarding results. This effort will necessitate thoughtful consideration of ML system design (to avoid embarking on unviable or impractical projects), causal inference (to steer clear of misleading trends), and AB testing (to accurately gauge whether your prototype is capable of generating revenue during scaling).
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