Many businesses have learned the hard way that not every AI project leads to glory and success. In fact, a 2023 CIO.com survey found that more than half of AI projects fail to produce actionable results at all. There are many reasons for this, but one of the biggest causes we frequently see is a disconnect between the data scientists who are actually building the models and the end users who would consume or use the models.
Most data scientists would agree that deep data exploration of all the relevant data is crucial to any analytics project. Unfortunately, these same data scientists are regularly faced with tight deadlines and often have no clear way to quantify the ROI for data exploration. As a result, data scientists frequently do not spend as much time as they would like when framing and scoping new projects and exploring the corresponding data. Additionally, the onus of data exploration typically falls to the data scientist who may be fairly removed from the end users within the organization. This means that when data exploration happens, it happens apart from the business analysts closest to decision-making. As a result, organizations miss out on domain expertise that could guide bigger data-based projects such as AI.
To tackle this problem, it’s time to transform the role of business analysts.
Rethink the Analyst Job Description
For too long, analysts have been stuck managing BI dashboards and spreadsheets for data consumers in the business. This keeps them focused on reporting current state conditions using the same metrics that have been reported on for years, instead of exploring the useful, unseen insights contained in the broader dataset.
A lack of time, tools, and skills has prevented analysts from exercising their talents on complex analytics projects. But now analysts can leverage AI to assist them with advanced data analysis. New tools empower analysts to do more strategic analysis upstream before data scientists get involved.
For example, a business analyst might look for customer journey signals across credit scoring models, risk management systems, and regulatory requirements to identify the best AI solutions for a new marketing program. The right tools can help them do this kind of deep investigative work.
Using AI-generated and AI-guided data exploration, any business analyst can explore all relevant data, get recommendations on the most promising use cases, pass those off to the data science team for refinement, and arrive at pilot projects with the best chance of success. This framework speeds up the time to insights, helping organizations increase their competitive advantage with the right AI projects sooner.
When analysts have the power of advanced analytics in their hands they can discover business advantages buried within mountains of data. Decision-makers can have confidence that any AI project proposal that emerges as a result of deep analyses has emerged organically from data and was put together in full collaboration with those on the business side—ensuring there’s value in pursuing it.
There’s no doubt that the organizations that invest in their analysts before costly AI projects will be the ones that thrive in today’s data-first world.
To Know More, Read Full Article @ https://ai-techpark.com/why-ai-use-cases-fail-and-what-to-do-about-it/
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