Artificial Intelligence (AI) has become a hot topic in the business world. Many companies are looking to leverage the benefits of this technology to optimize their operations, improve their products and services, and stay competitive in the market.
However, many brands are faced with crucial questions: Is their data adequate? Is its reliability guaranteed? Does it meet the specific requirements of AI processes? If you are also wondering about these aspects, this article is specially designed to answer your concerns. Guided by experts in the field, you will discover in this article the essential steps to make your company’s data usable by AI.
0. Réfléchir à ses objectifs
Before you even start manipulating your data, it’s imperative to clearly define your goals: What specific problems or challenges is your business trying to solve? How could AI add more value to your business? What are the concrete outcomes you hope to achieve with AI?
This foundational step is the foundation upon which your entire AI integration strategy will be built. Without a deep understanding of what you want to accomplish, it will be difficult to focus your efforts and measure your success. For example, you might want to:
- Predict your stocks: Forecasting sales volumes, optimizing storage costs, anticipating moments of friction… artificial intelligence (AI) can intervene at practically all levels of your supply chain, and consequently, improve the profitability of your company.
- Automate your administrative tasks: AI can also be a great way to automate tedious processes such as document management, invoice encoding or data entry.
- Analyze your sales cycles: By analyzing your sales data in depth, AI can help you identify trends, patterns, and untapped opportunities in your sales pipeline, enabling you to make more informed decisions to drive growth for your business.
1. Prepare your data sources to feed the AI
Once you have your goals clearly defined, the next step is to make sure your data is accessible and ready to be used. Indeed, for AI to work effectively, it needs access to quality data, and this requires some preparation up front.
1.1 Machine accessibility of data
One of the first considerations is making your data accessible to IT systems. This involves storing your data in locations that are accessible via networks or application programming interfaces (APIs).
Data stored locally – such as business tools that don’t have APIs – can pose accessibility issues. Of course, even if your company doesn’t use the cloud, there are ways to move your data and make it accessible. However, we recommend opting for online storage solutions such as cloud services (e.g. SharePoint), which allow remote and anytime access.
While these changes may seem significant, it’s important to note that they also provide a valuable opportunity to streamline and modernize your tools, paving the way for increased operational efficiency.
1.2 Processing of non-text data
If your data includes images—such as blueprints—or other non-text formats, they must be processed appropriately to be used by AI. For example, scans of paper documents may need to be converted to text using techniques such as optical character recognition (OCR).
By properly preparing your data, you lay the foundation for effective use of AI in your business. By ensuring your data is accessible and processed properly, you maximize the chances of success for your AI initiatives and position your business to take full advantage of this technology.
2. Faire de l’ordre
Once your data is machine-accessible, the next step is to organize it in a way that makes it usable and actionable by artificial intelligence.
Ce dont on ne se rend pas toujours compte, c’est que dès cette étape, l’intelligence artificielle peut déjà intervenir pour aider à mettre de l’ordre dans les données. L’IA peut en effet aider à rendre les données propres grâce à des algorithmes.
One of the first things to consider is the use of metadata. Metadata is essentially labels or descriptive information that you assign to your data to categorize and organize it. This allows AI to quickly understand the content and context of your data. So make sure to provide clear indications, relevant categories, and appropriate keywords for each piece of data.
The next step is to select the data that is relevant to the previously defined use case (see point “0. Think about your objectives”). Sometimes this represents the entirety of the company’s data, but often only a subset of the data is selected for the first iterations.
3. Manage access levels and data security
When working with data, access management and security are major concerns. Here’s how you can ensure your data is protected while allowing secure access to your employees:
3.1 Access control for employees
To ensure intra-company security, it is crucial to set up access control rules based on the nature of the data. For example, you might want to restrict access to payroll data only to the HR and administrative department. By defining rules based on the typology of the data and defining user profiles, you ensure that only authorized people can access the relevant information.
3.2 Data Security in the Cloud
Storing data in the Cloud offers many advantages, but it is essential to choose a secure and reliable platform.
“Especially since we all use Word, Excel, etc., being on Microsoft Sharepoint to save your data in the cloud is ideal, because it allows a connection between applications, files and Drive,” adds Arthur.
Thus, experts recommend Microsoft Azure as an ideal Cloud solution, offering high compliance with the GDPR and seamless integration with common productivity tools. By using Microsoft SharePoint to store your data in the Cloud, you benefit from seamless connectivity between applications, files and Drive, which simplifies data management and security. In addition, enabling two-factor authentication (2FA) adds an additional layer of security by requiring additional verification when logging in.
4. Sustain the system
Once your data system is up and running, it’s crucial to ensure its long-term sustainability. Here’s how you can ensure the continuity and efficiency of your system:
4.1 Continuous integration and automation
Your business generates data on a continuous basis, making the process of integrating this new information into your existing system essential. Artificial intelligence can play a key role in this process. By using previously established AI models, you can automate the classification and processing of new data, ensuring that it is properly placed and organized in your data infrastructure. This involves setting up automated processes to collect and integrate data from various sources such as emails, collaboration platforms like Teams or Slack, and other channels. This automation ensures that your database is constantly fed with relevant and up-to-date information.
4.2 Change management and training
System sustainability also requires a constant effort of change management and ongoing training. Make sure to regularly educate your employees on data management best practices and changes to your system. By training them on new features and processes that are put in place, you ensure that the entire team is aligned with the objectives and standards of your data system.
Conclusion
Your company probably has a goldmine of information. Artificial intelligence offers exceptional opportunities to improve the efficiency and performance of your business. However, to fully exploit its benefits, it is imperative to prepare your data properly. By making it accessible, well-structured and ready to be exploited, you create the optimal conditions for a successful integration of AI. That’s it, your company is ready to thrive in an increasingly AI-driven world!
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