With the ability to create code and content, and tools like Copilot that are already transforming the way people work, generative AI is helping organizations achieve more than ever before with their data and systems and create real value.
Data is at the core of AI-first operations. Data is no longer just a strategic advantage; it is paving the way for AI success and becoming essential to ensuring enduring competitive advantage. Nearly all (94%) business leaders recognize the need to invest in a data platform to realize their AI ambitions and scale it across the business, yet a staggering 63% say they still don’t have full confidence in the data their organization currently uses.
Strengthening your data foundation and equipping your workforce with the skills necessary to leverage these insights is a priority for any business that wants to reap the benefits of this technology, but navigating this path can be difficult.
Lead the Microsoft data business across Avanade and Accenture.
Prioritize your data platform investments
The reality is that many businesses face vast, fragmented data environments. Inconsistent information living in different silos doesn’t lend itself to AI that works on clean, unified data sets. Getting clean, well-governed data is both a significant task and an investment.
AI-centric transformation is not just about technology: There are still opportunities to transform operating models with existing IT investments and reimagine processes, products, and services with AI to unlock new business value. But ultimately, leaders must prioritize investments in their data platforms if they want to realize both short- and long-term value from AI.
Humanism is the key
A data platform manages enterprise data on a single, unified foundation, creating a single source of truth. A strong data platform, combined with an employee understanding of rapid engineering and rapid refinement, increases confidence in the outputs of AI, helping organizations realize value more quickly.
Making AI accessible is key: organizations need to put talent at the center of their AI efforts, equipping employees with the skills to effectively access, interpret, and leverage data. This fosters a culture of data-driven decision-making, leveraging insights at every step of the business process.
Tools like Microsoft Fabric bridge the gap between human and machine intelligence, facilitating the seamless integration of AI into workflows. By integrating an organization’s data and analytics, such tools become an asset for every employee, enabling deeper data analysis, data-driven decision-making, and automation of routine tasks. This accelerates the realization of value from generative AI and enables organizations to rapidly adopt new innovations.
Data governance is also important to ensure data quality, consistency, and security. Employees will be hesitant to participate in AI initiatives if the accuracy of the data is in doubt or if the risks of using AI seem too high. Business leaders must implement strong guidelines that allow employees to trust and confidently leverage data in AI projects. By fostering a data-centric culture, employees will be more willing to participate in AI efforts and contribute their expertise to derive value.
Leverage generative AI to clean up your data
One of the major challenges for businesses is the resource-intensive nature of data cleansing and management. The manual process, which involves meticulous checking, identifying errors, and correcting them, is not only time-consuming but also prone to human error. This can significantly slow down AI development and implementation, especially for businesses that deal with large and complex datasets.
Generative AI offers a breakthrough solution to this bottleneck. By automating the data cleansing process, these tools can significantly reduce the time and resources required to prepare data for AI models. Generative AI algorithms can be trained to identify common data inconsistencies, such as missing values, formatting errors, and duplicates. By analyzing historical data patterns and learning from predefined rules, these AI models can flag inconsistencies with high accuracy, freeing human data scientists to focus on higher-value, strategic tasks.
Once a discrepancy is identified, Generative AI can suggest potential fixes based on the context of the data. As Generative AI continuously learns and processes more data and receives feedback from human experts, it becomes increasingly adept at identifying new types of discrepancies and providing accurate fixes. This continuous learning ensures that the quality of the data fed into the AI model remains consistently high.
The impact of leveraging generative AI for data cleaning is far-reaching. Just as Robotic Process Automation (RPA) revolutionized deterministic, rules-based, manual processes, data management AI assistants act as co-pilots for data scientists. Accelerating data foundation preparation enables companies to deploy AI models faster and reap the benefits sooner. But careful execution is essential to maximize downstream competitive advantage and move beyond descriptive analytics to truly predictive and prescriptive models.
The future belongs to those who harness the power of data for AI, and there has never been a better time to drive a data transformation. Enterprises must increase investments in their data platforms to achieve an integrated and trusted data foundation. Only then can they realize the ideals of AI and scale it across the enterprise. This data-centric approach not only ensures relevance in a rapidly evolving digital environment, but also propels enterprises towards a future driven by intelligent insights and data-driven decisions.
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