What data do I need to start my AI transformation?

“AI relies on the ability to learn from data to make decisions, recognize patterns and automate processes. Depending on what you want the AI to do, you will need to provide it with the right data. Internal, external, structured or not, the AI experts who support you in your transformation must guide you.” Clément, Director of the Data Science and AI division at Araïko.

Understanding the essentials: why data is at the heart of AI

Data is at the heart of most AI initiatives. Without it, many algorithms and models would remain unusable.

AI mostly relies on the ability to learn from data to make decisions, recognize patterns, and automate processes. The richer and more representative the data, the more accurate and powerful AI models can be.

But that doesn’t mean you need to have a lot of it to start your AI project. You can build or improve your data ecosystem during your AI digital transformation.

The beginning. What types of data can you collect?

It is important to understand what types of data can be leveraged in an AI solution. Here is a summary of the different types of data to consider:

Internal data : This comes directly from your company, such as sales history, customer data, production data or financial data. This data reflects your business and is often the most valuable for identifying opportunities for improvement.

Example: A supplier can analyze sales history to predict periods of high demand and adjust inventory accordingly.

External data : this includes data from the market, competitors, or even open data. It allows you to contextualize your analyses and better understand your ecosystem.

Example: A food company could use weather data to adapt its supply chain to climate variations.

Structured data: This data is organized in a predefined format, such as databases or tables. It can be easier for AI algorithms to mine.

Unstructured data : This data includes, among others, emails, images, videos or even comments on social networks. Although they are more complex to process, they often contain rich and valuable information.

Most importantly: rely on reliable data

Large masses of unusable data can even harm your projects, by introducing errors or producing erroneous results. Here’s why quality is essential:

Accuracy: Data must be accurate and reflect reality. Errors in the data can lead to inaccurate predictions.

Consistency: Data must be consistent, i.e. without duplicates or inconsistencies, to ensure optimal use.

Relevance: Collect only the data needed for your use case. Unnecessary data adds complexity without adding value.

Regularly auditing your data can help you identify and correct issues before they become a roadblock to your AI projects.

What’s next? Building a sustainable data ecosystem

Once your data is provided and qualified, it’s essential to build an ecosystem that ensures its sustainability. This means putting processes in place to ensure lifecycle management and continuous enrichment of your data. Here are the key elements to consider:

Centralization: gather your data on a suitable platform to avoid their dispersion and facilitate their access.

Example: a company can centralize its data on a data lake to enable cross-functional exploitation by different departments.

Regular update: Data becomes outdated quickly. Make sure to keep it up to date for relevant analyses.

Team training: Raise awareness among your employees about the importance of data and train them on its use to ensure successful adoption of your AI projects.

Is your current data ready for AI?

Before you embark on your AI digital transformation, you can assess whether your current data is ready to be used. Here are some questions to ask yourself:

Is your data accessible? If it is scattered across multiple systems or formats, you will need to work on centralizing it.

Have they been quality checked? Carry out an audit to identify any gaps or anomalies.

Are they compliant with regulations? Make sure you comply with current standards, including GDPR.

Does your data reflect your current business? Outdated or unrepresentative data can skew your analysis.

Once you have the answers to these questions, you will be in a better position to start your AI transformation with a solid foundation.

Key points

AI relies on learning from data to make decisions, recognize patterns, and automate processes.

Data quality takes precedence over quantity: accuracy, consistency and relevance are the three criteria to remember.

To start your digital transformation with AI, you don’t need a huge volume of data. It all depends on the AI solution you are considering and the goals of your project.

Example: A chatbot for your customer service may only require a set of predefined questions/answers, while a predictive maintenance solution may require both historical and real-time data.

A lot of data is unexploited because it is not structured when it could be, it must be qualified. For example, your emails, your images or the comments on your site.

You can use different types of data to start your AI digital transformation: internal, external, structured, unstructured data.

Besides data, humans are one of the key factors for the success of an AI transformation. Involving your teams from the start, training them and supporting them in this change are essential to maximize the impact of your solutions. ? Learn more about AI acculturation