Worldwide, organisations are scrambling to integrate AI into their operations, hoping to enhance efficiency, decisionmaking, and innovation. However, the reality is that not all organisations are prepared to fully leverage AI, especially when it comes to data readiness. AI models rely on vast amounts of high-quality, well-structured data to function effectively, but many businesses face significant challenges in managing their data to meet AI’s requirements.
According to the Cisco 2024 AI Readiness Index, only 32% of companies show high readiness from a data perspective to adapt, deploy and fully leverage AI technologies. The report highlights data readiness remains one of the most significant barriers to AI adoption, impacting organisations’ ability to implement AI-driven solutions successfully. Poorly managed data, whether unstructured, biased, incomplete or insecure can lead to unreliable AI models, flawed decision-making and even regulator risks.
The effectiveness of generative AI is only as good as the data it is trained on. Poor-quality, biased or incomplete data can lead to unreliable, misleading or even harmful AI outputs.
Here we explore how generative AI uses data and why understanding the distinction between structured and unstructured data is critical for organisations looking to implement AI-driven solutions.
Generative AI models are trained on massive amounts of data to understand language patterns, visual elements and even human behaviours. This data acts as the foundation for their ability to generate text, images, videos, code, and other forms of content. The primary ways in which generative AI uses data include:
Learning from Patterns
AI models identify trends and relationships within datasets to produce coherent and relevant outputs
Generating Contextually Accurate Content
The quality of AI-generated responses, summaries and recommendations depend on the accuracy, diversity and completeness of the training data
Automating and Enhancing Decision-Making
AI leverages data to improve business processes, from customer service chatbots to predictive analytics in healthcare and finance
Data quality issues such as biases, inconsistencies or gaps can significantly impact AI performance. Models trained on biased or incomplete data may generate misleading or discriminatory results, leading to reputational and regulatory risks for organisations. This is why ensuring data readiness is a fundamental step in AI governance.
Not all data is equally suited for AI training and understanding the distinction between structured and unstructured data is crucial for organisations preparing their AI initiatives.
STRUCTURED DATA
Structured data is highly organised, typically stored in databases and spreadsheets, and easily processed by AI models. Examples include:
Since structured data follows a predefined
format, it is easier to analyse, categorise
and use in AI-driven decision-making.
UNSTRUCTURED DATA
According to the tech market intelligence firm IDC, 90% of the world’s data is unstructured. This type of data is not neatly organised and requires additional processing before it can be used effectively in AI models. Examples include:
To make unstructured data usable for AI, organisations must employ data processing techniques such as natural language processing (NLP), computer vision and data classification tools to extract meaningful insights.
As organisations integrate AI into their operations, they must confront a range of security challenges. AI adoption introduces complexities which could undermine security, from data privacy concerns to vulnerabilities in AI models and supply chains. Understanding these risks is essential for ensuring AI is deployed safely and responsibly.
Structured data is easier to feed into AI models, while unstructured data requires more effort to process
Structured data follows standardised governance practices, whereas unstructured data often needs additional layers of security and classification
Unstructured data, especially from user-generated content, can introduce biases and misinformation if not properly filtered
From fragmented data systems to security vulnerabilities, failing to prepare data adequately can reduce the reliability of AI-driven insights and increase organisational risks.
Many organisations store data across multiple systems, departments, and platforms, leading to data silos, isolated pockets of information AI models struggle to access. These silos can arise due to:
When AI models cannot access complete datasets, they produce incomplete or biased results. For instance, an AI-driven customer service chatbot trained on limited data might fail to recognise common customer queries, reducing its effectiveness.
Breaking down silos through data integration ensures AI has a holistic view of the information it needs to make accurate predictions and generate meaningful insights.
Poor data quality can significantly undermine AI performance. Common issues include:
If a fraud detection AI system is trained on inconsistent financial data, it may either fail to flag suspicious transactions or mistakenly block legitimate ones. Similarly, an AI-powered recommendation engine using duplicated customer profiles might make irrelevant product suggestions, frustrating users.
Regular data validation, cleansing and standardisation processes help mitigate these risks, ensuring AI operates on a trustworthy and consistent dataset.
AI models require access to huge amounts of data, but failing to implement proper safeguards can lead to serious security and compliance risks. Key concerns include:
In healthcare, an AI-driven diagnostics tool trained on improperly anonymised patient data could expose private medical records, breaching privacy laws. In finance, AI models handling customer data without encryption could become prime targets for cyberattacks.
To avoid these pitfalls, organisations must implement robust data governance frameworks, enforce access controls, and ensure compliance with industry regulations.
AI models are only as good as the data they are trained on. Poor data quality, fragmentation or security lapses can lead to:
Ensuring AI readiness starts with preparing high-quality, well-governed data. Now let’s explore how organisations can assess their data maturity and take steps toward effective AI governance.
The first step in AI readiness is understanding what data exists, where it’s stored and whether it’s AIready. Conducting a data audit helps organisations:
Without a clear inventory, organisations risk building AI models on fragmented or outdated data, leading to unreliable outputs.
Since AI interacts with structured and unstructured data differently, organisations need a clear method for distinguishing between them. Here are some practical tips:
Properly classifying data allows organisations to determine the best processing strategies and ensure AI models are trained on the right datasets.
Many organisations struggle with:
By identifying and resolving these issues, organisations can cleanse and standardise their data, improving AI performance and reliability.
AI models need seamless access to data across an organisation. To achieve this, organisations must map out:
Understanding data flows ensures organisations streamline integrations, eliminate inefficiencies and strengthen security, making AI adoption smoother.
AI systems should only access relevant and authorised data to maintain security and compliance. Organisations must evaluate:
Establishing strong data governance policies protects sensitive information while ensuring AI systems operate within ethical and legal boundaries.
Once organisations have assessed their data landscape, the next step is ensuring data is structured, accessible and optimised for AI applications.
AI models function best when data is consistent. Differences in date formats, numerical representations or naming conventions can lead to misinterpretations and errors. Standardising data formats across systems ensures:
Unstructured data, such as emails, PDFs and images, can be challenging for AI to process. Metadata tagging helps categorise and organise this data, making it easier to retrieve and analyse. Effective tagging includes:
AI models rely on data from multiple sources, databases, cloud platforms, customer relationship management (CRM) systems, and more. If these systems don’t communicate seamlessly, AI may miss critical insights. To enable interoperability:
A connected data ecosystem ensures AI can access and analyse the right information without unnecessary delays or inconsistencies.
Traditional storage solutions may slow down processing, leading to delays in generating insights. Organisations can optimise data storage by:
Certain AI applications such as fraud detection, chatbots and dynamic pricing models, require up to date information. Without real-time data, AI decisions may be based on outdated insights. Organisations can enable real-time data processing by:
AI models must be trained and deployed responsibly, ensuring sensitive or confidential information remains protected. Strong security and governance prevent AI from processing unauthorised or sensitive information, reducing risks of data breaches and compliance violations.
Key security measures include:
If training data is biased, AI-generated insights and decisions may also be skewed. By proactively addressing bias, organisations can build AI models that produce more accurate and ethical outcomes.
To reduce bias:
Before fully integrating AI into business operations, organisations should test its effectiveness using pilot models. These smallscale trials help:
AI is not a set-and-forget solution. As business needs evolve, regulations shift and AI models refine their capabilities, data management must keep pace. Without continuous oversight, AI systems risk generating outdated, biased or inaccurate insights.
Let’s now explore the key strategies for maintaining high-quality data and ensuring AI remains a reliable, ethical and valuable asset.
For AI applications that rely on real-time information, ensuring up-to-date data feeds is essential. Regular data audits help identify inconsistencies, duplicates or missing information, while automated cleansing and validation processes minimise errors.
By making data maintenance a routine part of operations, organisations can prevent AI from working with obsolete or misleading information.
AI models can drift over time, meaning their outputs may become less accurate or introduce unintended biases. To prevent this, organisations must continuously evaluate AI generated insights against real-world outcomes, flagging anomalies or patterns that could impact decision-making.
Regular retraining with updated, high-quality data ensures AI models remain reliable and relevant. Proactive monitoring helps maintain AI as a trusted tool rather than a liability.
AI technology is constantly advancing, and so are the ways organisations use data. To stay ahead, businesses should regularly review and adjust their data governance policies, ensuring they align with emerging AI capabilities.
Additionally, exploring new data storage and integration solutions can improve efficiency, while assessing AI-driven analytics tools can enhance decision-making. An agile approach to data management enables organisations to fully capitalise on AI advancements without being left behind.
AI governance and data privacy regulations are continuously changing, and non-compliance can lead to legal and reputational risks. Staying updated on global and local AI related regulations, such as GDPR, CCPA and Australia’s Privacy Act, is crucial.
Organisations should also strengthen data protection measures as new laws emerge and review AI training data to maintain ethical and legal standards. Prioritising compliance ensures businesses can integrate AI confidently without exposing themselves to unnecessary risks.
AI success depends on more than just technology, it requires a workforce committed to responsible data management. Encouraging data stewardship across teams helps prevent data silos, fosters collaboration between IT, compliance, and business units and promotes accountability in data handling. When employees understand the value of high-quality data, AI systems can operate more effectively and deliver better insights.
By establishing a strong foundation for continuous data management, organisations can ensure their AI initiatives remain accurate, compliant and valuable in the long run. AI driven success isn’t just about adopting the latest technology, it’s about maintaining the integrity of the data that powers it.
AI has the power to transform organisations, driving efficiency, innovation, and smarter decisionmaking. However, the effectiveness of AI depends entirely on the quality, structure, and governance of the data it relies on. Without proper data readiness, even the most advanced AI models will struggle to deliver meaningful insights, leading to biased outputs, unreliable predictions, and compliance risks.
As we’ve explored in this guide, organisations must take a structured approach to data management before embarking on AI-driven initiatives. This includes breaking down data silos, ensuring data quality, implementing robust security measures, and adopting clear governance frameworks. By addressing these foundational elements, businesses can create an environment where AI thrives, unlocking its full potential to enhance operations, improve decision-making, and maintain a competitive edge.
At Insentra, we help organisations navigate the complexities of AI data readiness. From assessing your current data landscape to implementing best practices for governance, compliance, security, and optimisation, our experts ensure your data is primed for AI success. With our tailored solutions and deep expertise, we empower businesses to harness AI with confidence, knowing their data is structured, secure, and ready to deliver real value.
We’ve sent a copy to your inbox. Remember to mark hello@insentragroup.com as a “safe sender”, and to check any junk or spam folders so you receive your copy.
We’ve sent a copy to your inbox. Remember to mark hello@insentragroup.com as a “safe sender”, and to check any junk or spam folders so you receive your copy.
Hybrid work has introduced new challenges in the landscape of device management. Level up your endpoint management with Microsoft Intune!
Insentra can augment end user service capabilities and accelerate business growth. Whether it’s an opportunity you can’t address, some pre-sales assistance, clients asking for a Professional or Managed service you can’t deliver, you’re struggling to break into new markets and accelerate your channel, or you’re frustrated trying to juggle multiple providers for all your IT needs – Insentra can help.
Empower yourself to seize every opportunity. Partner with Insentra.
We’re a certified amazing place to work, with an incredible team and fascinating projects – and we’re ready for you to join us! Go through our simple application process. Once you’re done, we will be in touch shortly!
Gain a clear understanding of how AI applies to your business, uncover immediate wins and develop a roadmap for success.
Imagine a business which exists to help IT Partners & Vendors grow and thrive.
Insentra is a 100% channel business. This means we provide a range of Advisory, Professional and Managed IT services exclusively for and through our Partners.
Our #PartnerObsessed business model achieves powerful results for our Partners and their Clients with our crew’s deep expertise and specialised knowledge.
We love what we do and are driven by a relentless determination to deliver exceptional service excellence.
SYDNEY, WEDNESDAY 20TH APRIL 2022 – We are proud to announce that Insentra has achieved the ISO 27001 Certification.
