5 Red Flags your Enterprise Data isn’t Ready for GenAI

Generative AI (GenAI) is driving a paradigm shift across industries, redefining possibilities and reshaping the way organisations harness technology. 

Yet, as powerful as GenAI can be, it’s only as effective as the data it relies on.  

Poor data quality can diminish AI’s potential, derailing projects, eroding trust, and leading to flawed decisions. 

According to analysts, 30% of GenAI projects are abandoned after proof-of-concept, and an alarming 85% fail to deliver their expected business value. The underlying cause is frequently inadequate data preparation and the lack of a robust data quality framework. 

To maximise GenAI’s value, organisations must start by building a foundation of high-quality data.  

Here are five key indicators that your data quality may need improvement and practical steps to address them. 

1. Your data is siloed across multiple repositories 

Siloed data is a prevalent challenge in enterprises, particularly those with legacy systems or sprawling IT infrastructures. When critical information is located across disparate repositories, file shares, or platforms, creating a unified view becomes almost impossible. This fragmentation undermines decision-making and limits the effectiveness of AI models that require complete and integrated datasets. 

The risks: 

• Missed insights: Fragmented data limits AI’s ability to identify meaningful patterns and generate reliable insights. 

• Operational inefficiency: Significant time and resources are wasted reconciling disparate datasets, slowing down processes and decision-making. 

The solution: 

Implementing automated data quality management solutions can discover and organise structured and unstructured data. Platforms which connect to on-premises and cloud environments can also support centralised data management, improving data quality governance while preparing data for GenAI initiatives. 

2. You lack visibility over your data 

Can your organisation confidently identify what data it holds, where it is stored, and how it is being utilised? Without this clarity, your organisation operates at a disadvantage in a landscape where transparency and precision are non-negotiable. 

The challenge is particularly acute with unstructured data (such as emails, PDFs, and images) that often hides critical business value.  Siloed and unstructured data complicates decision-making and reduces agility, with large and mid-size organisations reporting that unstructured data is often fragmented across systems. 

The risks: 

• Compliance concerns: Unidentified sensitive data increases the likelihood of regulatory breaches. 

• Missed opportunities: Valuable insights remain inaccessible, buried within unmanaged data. 

The solution: 

AI-powered information management solutions can provide a clear view of your data landscape. By finding and enriching both structured and unstructured data with metadata, these solutions can enhance data quality and data governance, making it easier to conduct data quality checks and safely leverage GenAI services. 

3. You don’t understand your data’s business value 

Not all data is created equal. Some datasets drive strategic decisions, while others are redundant, obsolete, or trivial (ROT). If your organisation lacks a clear understanding of which data matters and why, you’re likely wasting resources managing information with little to no business value. This reduces the effectiveness of data management and hampers the reliability of GenAI outcomes. 

The risks: 

• Resource drain: Managing ROT data inflates costs and increases inefficiency. 

• Compromised GenAI outputs: Feeding irrelevant or outdated data into GenAI models results in unreliable insights. 

The solution: 

A robust data quality framework helps organisations identify high-value data and align it with business objectives. Automated solutions can categorise and enrich data, so that only meaningful datasets feed into AI models, enhancing the reliability and relevance of AI insights. 

4. Your data governance is weak and reactive 

Without robust governance, sensitive information is at risk of being mishandled, regulatory compliance becomes uncertain, and GenAI outputs can suffer from bias or inaccuracies. Poor data quality governance also complicates data lifecycle management, exposing organisations to significant legal and ethical challenges. 

The risks: 

• Regulatory penalties: Mishandling data and failing to comply with regulations can result in hefty penalties. 

• Reputational damage: Inadequate governance undermines trust among customers and stakeholders, affecting business credibility. 

The solution: 

Automated data quality governance solutions enable proactive management of sensitive data, applying consistent compliance and privacy policies in-place to de-risk enterprise data. By establishing comprehensive governance protocols, organisations can mitigate risks and ensure data remains reliable and secure. 

5. Your data accuracy and reliability are questionable 

Errors, inconsistencies, and incomplete records are significant challenges for organisations seeking to implement GenAI. When inaccurate or unreliable data underpins GenAI models, the results can range from flawed predictions to critical operational failures, undermining both trust and effectiveness. 

The risks: 

• Flawed decision-making: Inaccurate data results in misguided strategies and poor business outcomes. 

• AI biases: Erroneous or incomplete data skews GenAI models, leading to ethical challenges and compromised performance. 

The solution: 

Automated data quality management solutions continuously find, enrich, organise and de-risk enterprise data. By ensuring accuracy and consistency, these solutions create a strong foundation for GenAI readiness, reducing the risk of errors while improving data quality metrics. 

The bigger picture: why data quality matters for GenAI success 

Data quality is the foundation for effective GenAI initiatives. Forrester reports data quality is now the primary factor limiting GenAI adoption. 

Ignoring data quality issues is akin to building on unstable ground. Ultimately, the entire structure collapses, posing significant risks to broader business strategies and objectives. 

Key risks of poor data quality include: 

• Operational inefficiencies: Fragmented, unreliable data leads to wasted time, duplicated efforts, and inflated costs. 

• Ethical concerns: GenAI outputs derived from biased or incomplete data can result in unintended harm, eroding trust among stakeholders. 

• Missed opportunities: Inaccurate or inaccessible data limits GenAI’s ability to generate actionable insights, hindering innovation and growth. 

Addressing these challenges will allow organisations to maximise GenAI’s potential while safeguarding their reputation and bottom line. 

Real world application: Data transformation in manufacturing  

A real-world example of the importance of data quality can be seen in how a leading automotive manufacturer partnered with EncompaaS to address fragmented warranty data spread across multiple systems.  

By leveraging EncompaaS’s enterprise information management software​, the organisation transformed chaotic, siloed information into a centralised, actionable foundation. This case illustrates how automated data quality management and a strong data quality framework can solve complex challenges, protect profitability, and enable better decision-making. 

Practical steps to improve data quality  

Improving data quality is achievable when approached strategically: 

1. Conduct a comprehensive data audit: Assess your current data landscape to identify silos, inconsistencies, and gaps in your data management processes. 

2. Leverage automated tools: Use platforms that automate data preparation, organisation, and data governance at scale. 

3. Align data with strategic objectives: Focus on datasets that directly support your organisation’s business goals and priorities, ensuring resources are allocated effectively. 

4. Establish a robust governance framework: Develop and implement automated policies to maintain compliance, mitigate risks, and manage sensitive information proactively. 

5. Monitor and iterate: Continuously evaluate data quality metrics and refine processes to meet evolving business needs. 

Preparing your enterprise data for the future of GenAI 

The success of GenAI relies on a strong foundation of well-organised data. Common challenges like data silos, inconsistent records, and weak governance can hinder GenAI performance and prevent organisations from achieving their strategic objectives. 

The EncompaaS platform addresses these challenges by discovering, understanding, and managing structured, unstructured, and semi-structured data. With advanced AI technologies, EncompaaS transforms content chaos into actionable, high-quality data, providing comprehensive visibility, automating governance, and ensuring data accuracy.  

Whether you’re starting your GenAI journey or scaling existing capabilities, our platform helps you remove barriers, reduce risks, and fully leverage the power of GenAI. 

Data quality defines the success of AI initiatives. Let’s make sure your foundation is ready.