Generative AI is shaking up how businesses operate – offering unprecedented opportunities to innovate, automate, and lead. To thrive in this brave new world, one thing is essential: generative AI data preparation. 

No longer limited to data scientists or tech experts, AI is now embedded into tools we use every day. Think ‘smart compose’ features that help us draft emails, AI assistants taking notes in meetings, and chatbots triaging requests for busy customer service teams. It’s reshaping how we work, boosting productivity, and streamlining processes in ways once thought impossible. 

According to McKinsey, generative AI could automate up to 70% of business activities across nearly all industries by 2030, potentially adding trillions of pounds to the global economy. And this is just the beginning. The technology is rapidly evolving, growing smarter and more sophisticated by the day. 

Businesses that fail to prepare for AI risk being left behind in an increasingly competitive landscape. But that doesn’t have to be you! 

In this article, we’ll explain how your organisation can help lead the charge – and it all starts with generative AI data preparation. Let’s dive in. 

The strategic importance of being generative AI-ready 

Generative AI has the power to transform virtually every industry, from life sciences to logistics. And it sparks a lot of attention: boards are hosting workshops, leaders are brainstorming potential use cases, and teams are running early experiments.  

But turning this excitement into meaningful results requires more than enthusiasm – it demands solid preparation. 

Chief Data Officers (CDOs) are at the centre of this challenge. Research from Harvard Business Review shows that while many organisations are excited about generative AI, few have the data strategies to make it work. Without the proper preparation, these tools can’t deliver on their promise. 

Generative AI development depends on clean, accurate, and well-organised data. The results will fall short if the data feeding into these systems is outdated or poorly managed. That’s why getting AI-ready should be a strategic priority for organisations that want to lead in an AI-driven world. 

Core steps in preparing data for generative AI 

Recognising the importance of high-quality data is one thing, but achieving strong generative AI data preparation is another. So, where should your organisation start? 

Data quality improvement 

As the Harvard Business Review research highlighted, nearly half of CDOs (46%) identified poor data quality as the biggest challenge to unlocking generative AI’s potential. Simply put, unreliable data leads to unreliable AI. 

To overcome this, start by tackling common problems. Missing values or duplicates in your data can skew results, so fill gaps with well-considered estimates or remove incomplete entries if they’re not critical. Make sure your data follows consistent formatting, such as dates, times, and currency – small inconsistencies can snowball into bigger issues. 

You’ll also need to manage outliers – unexpected or extreme values that can distort AI predictions. These can often be adjusted or removed to keep your data reliable. Finally, focus on clearing out any ‘noise’ or irrelevant inconsistencies so your AI models learn from patterns that genuinely matter. 

Breaking down silos for data accessibility and integration 

To make the most of generative AI, your data must flow seamlessly across your organisation. One way to achieve this is by centralising it in the cloud. Platforms like AWS, Google Cloud, or Azure allow you to combine siloed data, consolidating storage and processing for greater efficiency and scalability. 

Cloud-based tools such as data lakes and warehouses (e.g., Snowflake or BigQuery) are particularly valuable for managing large volumes of data. They provide the flexibility and scale needed for ambitious AI projects. APIs are also powerful tools. They connect different systems and help fill gaps in your data infrastructure. For instance, APIs can enable text, image, and voice generation, adding new capabilities to your organisation. 

By breaking down silos and integrating your data, you’re building on the data quality improvements mentioned earlier. This means your AI models work with accurate, up-to-date information, improving performance and results. 

Structuring unstructured data using NLP and metadata systems 

Unstructured data is often messy and hard for AI to process. That’s where metadata and natural language processing (NLP) come in – two key tools for turning chaos into order. 

By tagging files with information like creation dates, authors, or categories using metadata, you add structure and context, making it easier to organise and retrieve. For example, tagging a research report with ‘enzyme activity’ or ‘clinical trials’ helps group similar content for analysis. Using domain-specific metadata standards, such as Dublin Core for general information or MeSH (Medical Subject Headings) for life sciences, can ensure consistency and scalability across your systems. 

NLP takes this further, helping AI systems understand and organise text. Tools like SpaCy or NLTK can break down text into smaller parts, identify key terms, and even spot patterns, like recognising mentions of specific genes or proteins in a life sciences context. Together, metadata and NLP transform unstructured information into structured formats that are easier to analyse and integrate into generative AI workflows. 

Ensuring compliance with data security and privacy regulations 

There’s one element of generative AI data preparation that’s non-negotiable: protecting privacy.  

Breaching data regulations can result in hefty fines, reputational damage, and derailed projects. The good news? With the right steps, you stay compliant and build trust with your customers. 

Here are some practical tips to keep your data secure and within the rules: 

• Anonymise sensitive data: Remove or mask personal details, like names or email addresses, using techniques such as pseudonymisation or encryption. This protects privacy while keeping the data usable for AI. 

• Control access: Use role-based permissions to ensure only the right people or systems can access sensitive information. 

• Get clear consent: Always obtain explicit consent for using personal data, and be clear about how it will be used. Keep records to show you’re following the rules. 

• Follow regional regulations: Make sure your data handling complies with laws like GDPR in Europe, HIPAA for healthcare, or CCPA in California. 

• Monitor and audit regularly: Check how your data is accessed and used. Regular reviews help spot and fix any compliance risks early. 

These steps will keep you on the right side of the law and build the transparency and trust that set successful AI initiatives apart. 

Long-term strategies for AI readiness 

The pharmaceutical industry provides a valuable lesson in AI readiness.  

Many companies in the sector are resource-rich, with top talent and cutting-edge innovation. However, a recent study found that most scored no higher than 3 out of 5 on execution – in other words, their ability to implement AI plans and scale them effectively.  

This gap often stems from a lack of a guiding long-term plan. Even the most innovative organisations can struggle to execute without a clear strategy. 

So, to ensure your organisation is ready for an AI-driven future, focus on these long-term strategies: 

1. Develop a focused AI roadmap 

Generative AI can’t solve every problem, but it can make a big difference in the right places. Start by identifying areas where it could add the most value. Is it speeding up processes? Streamlining workflows? Driving innovation? Once you’ve pinpointed your priorities, create a roadmap to guide your efforts and focus on the applications with the greatest impact. 

2. Commit to workforce upskilling 

AI works best in skilled hands. Make ongoing training a priority so your teams feel confident using AI tools in their day-to-day work, whether that’s analysing data, improving customer experiences, or managing operations. The goal should be a workforce that collaborates with AI, not one that feels left behind by it. 

3. Encourage experimentation 

Gen AI is still evolving, so there’s no one-size-fits-all approach. Give your teams the freedom to try new ideas, test tools, and learn from mistakes. Not every experiment will work – but fostering a culture of curiosity and adaptability will help your organisation find unique ways to succeed. 

4. Measure and refine your approach 

Don’t expect to get everything right the first time. Regularly check in on your AI initiatives to see if they deliver results. What’s working, and what’s not? Use these insights to refine your strategy as you go, ensuring your approach evolves alongside the technology. 

Integrating machine learning to enhance generative AI 

Machine learning models are the backbone of generative AI, helping it make smarter decisions and produce more reliable results. Two key types – predictive and prescriptive models – work together to supercharge its capabilities. 

Predictive models use historical data to forecast what’s likely to happen next. For example, in healthcare, they might predict how a molecule will behave in clinical trials, helping generative AI focus on the most promising options during drug discovery. They can also help anticipate things like how a patient will respond or how a customer might behave. This helps make the AI outputs more precise and relevant. 

Prescriptive models go one step further by recommending actions based on those predictions. While generative AI might suggest treatment plans or strategies, prescriptive models weigh up the options – like considering a patient’s history – to guide better decisions. 

What makes this pairing so effective is how it balances creativity with practicality. Generative AI can explore a range of possibilities, while machine learning ensures those ideas are rooted in data and ready for action. 

GenAI data preparation and usage – example from the life science sector 

For a great example of how data preparation and generative AI can deliver tangible results, look no further than the pharmaceutical firm Roche.  

Back in 2020, Roche’s data team realised they needed a better way to handle a growing number of data use cases. Their solution was a data mesh – a system that gives different business units more control over their data, letting them create tailored ‘data products’ that can be reused across the organisation. 

To make this work, Roche launched the Roche Data Insights (RDI) program – essentially, a playbook for setting up and using the data mesh. They started small, onboarding a couple of departments, including manufacturing, and scaled up gradually. Fast-forward to today, and Roche’s data mesh supports 30 domains and over 300 data products, all easily accessible through their self-serve Roche Data Marketplace. 

One standout success comes from their legal department. By integrating generative AI models from AI software developers Dataiku, Roche has transformed how they analyse case law and patents. Generative AI now helps them quickly extract insights from court cases, saving hundreds of lawyer hours a year and cutting costs by an estimated $100,000 – $250,000 annually. 

So, what can we learn from this example? Testing the concept at a smaller, more manageable scale is a great way to start, as Roche did by onboarding just a couple of departments. Building gradually gives teams time to adjust and ensures the system can handle more complexity as it grows. And pairing a strong data foundation with the right AI tools can help organisations solve specific challenges while delivering measurable results. 

The importance of generative AI data preparation 

Yes, preparing your organisation for generative AI is a technical challenge – but it’s also a strategic opportunity.  

As we’ve seen, success depends on laying the groundwork: cleaning and organising your data, integrating the right tools, and adopting a long-term approach that evolves alongside the technology. 

The key to thriving in an AI-driven world is to take things step by step. Experiment, test ideas on a manageable scale, and build gradually as Roche did with their data mesh. But don’t forget the bigger picture. Generative AI works best with a clear vision and a willingness to learn and adapt. 

If you’re looking for a practical starting point, Holisticon’s Data Discovery Workshops can help. These workshops are designed to align your data capabilities with your business goals, giving you the framework you need to turn raw data into a competitive advantage.  

Generative AI is still evolving, but with the right preparation, your organisation can be ready to lead – not follow – in the AI revolution. 

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