85% of all AI models and projects fail because of poor data quality. That’s what Gartner recently reported. In a day and age when Artificial Intelligence has everyone on their toes, statistics like these are truly heart-wrenching!
The AI Revolution is Stuck (And Bad Data is to Blame)
You have the data. You’ve chosen your model. You are clear about what outcomes you are looking to achieve. Yet, when you look at the real results, you’re disappointed. And you cannot fathom why!
Inaccurate, inconsistent, or irrelevant data is to blame for this. And this doesn’t just impact startups or small-sized companies. Poor quality data and the lack of governance can bring even the most established businesses down.
In 2024, McDonald’s was forced to call off its AI drive-thru project because it encountered several problems. The AI system couldn’t take simple orders, took requirements from the wrong cars, and advised strange food combinations, like ice cream and bacon.
In a similar incident, Air Canada’s AI chatbot wrongly assisted a passenger on bereavement fares, causing him to spend almost $2000 on tickets without receiving any discount.
The underlying issue? Poor data = Poor AI outcomes
Diagnosing Your Data Debt: Top Roadblocks
Investments in AI are at an all-time high; IDC estimates they will yield a global cumulative impact of $22.3 trillion by 2030, representing approximately 3.7% of the worldwide GDP. However, despite these investments, enterprises still struggle to extract real value.
From duplicated tools to fragmented data lakes and enterprise applications that don’t communicate – let’s look at the top roadblocks that negatively impact or even stall enterprise AI projects:
- Legacy Systems: Several organizations today are racing ahead to embrace the AI revolution, but one big problem is restricting them from achieving the intended results: legacy systems. These outdated and rigid platforms store much data that cannot be scaled or integrated for AI systems and use cases.
- Siloed Data: Organizations’ reliance on several disconnected systems means data lies in silos, often creating blind spots. For AI models to deliver impressive results, all organizational data must be combined into one unified place. But with these detached systems, that’s hardly the case.
- Unstructured Chaos: In addition to siloed data, enterprise AI projects struggle with unstructured data. With critical data stored in PDFs, emails, and messy text, unearthing insights becomes a challenge.
- Inconsistent Truths: The presence of several platforms and tools across multi-cloud environments means there is no single source of truth. Every system paints a different data story, making it confusing for AI models to determine which data to use for analysis.
- Lack of Data Governance: Despite the growing importance of data governance, no clear ownership or compliance is in place. This lack of governance makes it easy for bias and hallucinations to creep in, diluting the overall outcomes of any AI initiative.
Fixing Data: Preparing Your AI Fuel
Investments in AI tools and skills can deliver the intended results only when training data is of a certain quality. Here are some tips on fixing your data and preparing your AI fuel:
- Breaking Down Silos: While preparing AI fuel, the first step is to break down silos. This means combining information from multiple sources to create a single view of organizational data. Data integration solutions can simplify data management efforts and maintain data integrity for AI initiatives.
- Taming Unstructured Data: AI models require clean, structured, and accurate data to deliver the correct conclusions. Cleaning and preprocessing unstructured data and converting it into AI-supported formats can help overcome this issue. Planning governance programs can lessen data complexity via standardization and ensure AI strategies align with business objectives.
- Investing in a Modern Data Warehouse: Data piles up faster than ever. If companies want to keep up, they need a data warehouse that scales with them, not some rigid, outdated system. The proper setup acts like a central hub, making it effortless to store, analyze, and actually use data when it’s needed. There are no bottlenecks, no chaos—just smooth, scalable access.
- Enhancing Data Quality: Good-quality data is the lifeblood of every successful AI initiative. Thus, organizations must adopt tools that help identify and rectify data errors, inconsistencies, and duplicates. Modern data quality solutions ensure that data used for AI projects is accurate, complete, and current.
Ensuring Master Data Management: Investing in advanced MDM tools can enable organizations to manage their data systematically and strategically. They can maintain data quality and ensure consistency, relevance, and reliability. Organizations must also define solid governance policies, standards, and processes to consolidate, standardize, and enhance the data quality that AI systems consume
Taking Critical Next Steps for Data Health
As organizations look to maximize value from AI, they expect quick wins. Poor data quality can hinder AI projects, resulting in a loss of revenue and eventually causing the workforce to become less productive and demoralized.
High-quality data has become essential for quick and favorable wins from AI projects. This involves tearing down silos, scrutinizing data sources, updating legacy systems, applying investments in data management and governance tools, etc.
At Ascennt, we offer various data services to help you turn your AI projects into a successful reality. Explore our solutions today to ensure proper data quality, integration, warehousing, management, and governance! Contact our data experts to get started!
