AI is making headlines almost every other day. You’ll often hear about new discoveries, advancements, and applications around these technologies. It’s almost impossible to predict the upper limit of its future possibilities.
Currently, the tools are making a solid mark in automating tasks, unlocking insights, and enhancing operational efficiency across industries. It should come as no surprise that 79 per cent of corporate strategists believe AI is critical to their success over the next couple of years
However, making proper use of the technology requires significant attention and precision. It needs a well-formulated strategy to ensure high-quality data inputs into the system. In the world of AI, the better the input, the better the outcome.
SAP highlighted this in their latest whitepaper: “There is No Good AI Without a Good Data Strategy”, underscoring the importance of great data for leveraging AI’s full potential. Let’s have a closer look into the topic.
AI’s effectiveness is dictated by the quality of data inputs provided to it. Accurate, well-managed information helps the AI/ML algorithms produce relevant insights and exact automation.
Industry experts and analysts constantly highlight the relevance of a coherent data strategy, backed by high-quality expert governance. The well-knit mechanism is key to breed consistent, accurate, and reliable results from the AI models.
In-depth quality checks to ensure data accuracy, consistency, and reliability is key to bring out meaningful, trustworthy AI/ML outputs.
AI experts follow the adage “garbage in, garbage out.” That is, poor data inputs will lead to inaccurate, unreliable results and outcomes. When AI models are fed with incomplete, outdated, or inconsistent data, the algorithms can generate biased or irrelevant results, which may undermine business decisions.
This also leads to data hallucinations, resulting in ill-informed decision-making that harm businesses. Around 40 per cent of businesses in the USA and Europe reported some kind of data hallucinations in their AI outputs.
In an SAP-led business environment, where data flows are interconnected, the consequences of poor data inputs can be even more pronounced, affecting not just isolated functions but entire business processes. Therefore, maintaining high data quality is not just beneficial—it's critical for ensuring the effectiveness and integrity of AI solutions.
From our discussions above, it’s clear that a robust data strategy is the backbone of any successful AI implementation, particularly in complex ecosystems like SAP. A proper combination of data governance, integration, and management practices helps ensure that data inputs are always clean, well-organised, and accessible.
Businesses should also ensure continuous data monitoring and improvement to adapt to their evolving needs and technological advancements. It’s important to set up an advanced information management approach to proprietary data.
A solid data strategy is key to help organizations maximize the value of their AI investments, driving innovation, enhancing operational efficiency, and gaining a competitive edge in their industries.
Industry experts and business leaders across the world agree that poor data quality can lead to significant setbacks, while high-quality data opens the door to innovation.
For instance, companies that have implemented advanced data governance frameworks have seen significant improvements in their AI initiatives. These frameworks ensure that data is accurate, up-to-date, and accessible, allowing AI algorithms to function at their highest potential.
As organisations continue to invest in AI, those with a robust data strategy will be best positioned to leverage the technology’s full capabilities and drive innovation.
One such example highlighted in SAP’s whitepaper is the use of SimpleMDG—the tool that helps businesses manage their master data across systems. It’s easy-to-use interface and advanced integration capabilities make it a great tool for making master data accurate and AI-ready.
The paper highlights how a multinational corporation witnessed a dramatic improvement in data quality after integrating SimpleMDG into their SAP ecosystem. It helped the company drive accurate AI-driven forecasts and enhance their decision-making processes.
This just goes to show how integrating high-quality data strategies within SAP environments can significantly enhance AI's impact, driving innovation and operational efficiency.
It’s important for businesses to recognize that data is the lifeblood of AI technologies. A well-crafted data strategy isn’t just a nice-to-have, it's a strategic imperative that directly determines the quality and reliability of your AI outputs.
Following this, more and more businesses can ensure that their AI initiatives are successful and sustainable, while driving meaningful outcomes and long-term growth.
Photo: SAP World