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Rapid advancements in artificial intelligence (AI), particularly generative AI are putting more pressure on analytics and IT leaders to get their houses in order when it comes to datastrategy and data management. If you go out and ask a chief data officer, a head of IT, ‘Is your datastrategy aligned?’,
Proving the ROI of AI can be elusive , but rushing to achieve it can prove costly. Instead, CIOs must partner with CMOs and other business leaders to help quantify where gen AI can drive other strategic impacts especially those directly connected to the bottom line. Below are five examples of where to start.
But CIOs will need to increase the business acumen of their digital transformation leaders to ensure the right initiatives get priority, vision statements align with businessobjectives, and teams validate AI model accuracy. 2025 will be the year when generative AI needs to generate value, says Louis Landry, CTO at Teradata.
Similarly, Deloittes 2024 CxO Survey highlights that while CDOs prioritize AI and business efficiency, sustainability remains a secondary focus. However, embedding ESG into an enterprise datastrategy doesnt have to start as a C-suite directive.
This is why many enterprises are seeing a lot of energy and excitement around use cases, yet are still struggling to realize ROI. So, to maximize the ROI of gen AI efforts and investments, it’s important to move from ad-hoc experimentation to a more purposeful strategy and systematic approach to implementation.
Adding another position may not be terribly appealing, but there is one C-suite role every company should consider—chief data and analytics officer (CDO or CDAO). Data is the lifeblood of modern business, the fuel that powers digital transformation, and every company should have a datastrategy.
We are also seeing productivity increase, lower total cost of ownership and higher ROI. For an Australian financial technology firm, having a single pane of glass view across all their data is critical to support their fraud detection programme, mitigating risk for themselves and their customers. The Power of Two.
The use cases and customer outcomes your data supports and the quantifiable value your data creates for the business. How does defining data landscape in this way help your organisation? In the next section, we’ll discuss more about why your data landscape is so vital to your company’s success.
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