Empowering the AI-enabled workforce with data
With code and content creation capabilities and tools like Copilot already transforming the way people work, generative AI is helping organizations do more with their data and systems than ever before, creating real value.
At the heart of AI-first operations is data. It’s no longer just a strategic asset – data has become a necessity to create a path to AI success and gain a lasting competitive advantage. Nearly all (94%) business leaders recognise the need to invest in data platforms to realise their AI ambitions and scale them across their businesses, but a staggering 63% still say they don’t fully trust the data their business uses today.
Strengthening data foundations and equipping employees with the skills to leverage these insights should be a priority for any company hoping to reap the benefits of this technology. However, navigating this path can be daunting.
Leads Microsoft Data activities within Avanade and Accenture.
Prioritizing investments in data platforms
The reality is that many companies are dealing with a vast and fragmented data landscape. Inconsistent information sitting in different silos makes it unusable for AI, which thrives on clean, uniform data sets. Getting clean, well-maintained data is a significant task and investment.
AI-centric transformation isn’t just about the technology, which means 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 data platforms if they hope to realize both short- and long-term value from AI.
People-oriented is the key
Data platforms manage enterprise data in a single, unified foundation to create a single source of truth. A strong data platform complemented by employees’ understanding of rapid engineering and rapid refinements increases confidence in AI outputs and will help organizations realize value faster.
Making AI accessible is essential. Organizations must put people at the center of their AI journey, equipping employees with the skills to access, interpret, and leverage data effectively. This fosters a culture of data-driven decision-making, where insights impact every step of the business process.
Tools like Microsoft Fabric can bridge the gap between human and machine intelligence and facilitate the seamless integration of AI into workflows. By unifying an organization’s data and analytics, such tools become assets for all employees, enabling deeper data analysis, data-driven decision-making, and the automation of mundane tasks. This accelerates the realization of value from generative AI and enables organizations to rapidly adopt new innovations.
Data governance is also critical to ensuring data quality, consistency, and security. If data accuracy is questionable or the risk of using AI seems too high, employees will be reluctant to participate in AI initiatives. Business leaders must implement robust guidelines that empower employees to trust their data and use it confidently for AI projects. By fostering a data-centric culture, employees become active participants in the AI journey, contributing their expertise to unlock value.
Using Generative AI to Clean Data
A key challenge for businesses is that data cleaning and management are often resource-intensive. Manual processes involving detailed checks, error identification and correction are not only time-consuming but also prone to human error. This can significantly slow down the development and implementation of AI, especially for businesses dealing with huge and complex datasets.
Generative AI offers a breakthrough solution to this pain point. By automating the data cleaning 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 duplications. By analyzing historical data patterns and learning from predefined rules, these AI models can flag inconsistencies with high accuracy, freeing up human data scientists to focus on higher-value, strategic tasks.
Once inconsistencies are identified, generative AI can suggest potential corrections based on the context of the data. It can continuously learn, and as it processes more data and receives feedback from human experts, it becomes increasingly adept at identifying new types of inconsistencies and providing accurate corrections. This continuous learning ensures that the quality of data fed into AI models remains consistently high.
The impact of leveraging generative AI for data cleaning will be far-reaching. Just as Robotic Process Automation (RPA) revolutionized deterministic rule-based manual processes, AI assistants for data management will act as co-pilots for data scientists. By accelerating data foundation readiness, they will enable companies to deploy AI models faster and reap the rewards sooner. However, to maximize their downstream competitive advantage and move beyond descriptive analytics to truly predictive and prescriptive modeling, careful execution will be critical.
The future belongs to those who harness the power of data for AI, and there’s never been a better time to drive data transformation. Businesses must increase their investments in data platforms to achieve a unified, trusted data foundation. Only then can they realize their AI ambitions and scale across their enterprises. This data-centric approach not only ensures relevance in the rapidly evolving digital landscape, but also propels businesses toward a future fueled by intelligent insights and data-driven decision-making.
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