The path to using AI on your own business data
Many companies use generative AI tools like ChatGPT, Microsoft Copilot, and IBM WatsonX to analyze data and streamline their work. However, without integration into a company's specific data, generative AI lacks insight into particular processes, rules, and the current status. Without access to accurate data sources, AI can fabricate responses without any factual basis. A superior solution is to apply AI directly to your own business data.
By leveraging AI on your own business data, you can develop solutions tailored to your operations, unlike external AI tools. This means companies can benefit from all the data within their systems in ways previously unattainable, using it to make better, faster decisions and optimize operations.
Advantages of applying AI to your own business data
For business and IT leaders, this approach enables them to utilize their datasets in ways that strengthen competitiveness and improve decision-making.
With your own AI, you can:
- Gain new insights into your customers and their behaviors.
- Automate time-consuming processes and minimize human errors.
- Use AI to identify patterns, draw conclusions, create personalized communication, and optimize service.
- Analyze and understand large datasets to make quicker, more informed decisions.
How do you apply AI to your business data?
Applying AI to your own business data requires a well-thought-out strategy and organized data. Several internal preparations are essential. Below are some key steps businesses should take to get started:
- Data collection and integration
To apply AI to business data, extensive collection and integration of data from various systems, such as CRM, ERP, and external APIs, are necessary. This is achieved through APIs and ETL (Extract, Transform, Load) processes that centralize data from different sources into a unified platform. This foundational step ensures the collected data is accessible for analysis and AI applications.
- Data preparation
Once collected, the data must be cleaned and standardized to ensure consistency and accuracy. This involves removing incorrect or incomplete data and aligning data from different systems into a common format. Using techniques like schedule mapping ensures all data fields adhere to a unified structure, enabling efficient analysis and AI insights.
- Creating the data warehouse
A central database is built, often consisting of both a data warehouse for structured data and a data lake for unstructured data. This data platform serves as the central hub for deriving business insights, applying AI models, and conducting real-time analyses. This step is crucial for handling large datasets and providing a unified view of all relevant data.
- Data governance and security
When working with sensitive business data, robust governance and security systems are essential. Identity management and encryption protect data both at rest and in transit, while access controls (RBAC) ensure only authorized personnel can access specific parts of the data warehouse. It’s also important to ensure compliance with relevant regulations and standards for responsible and legal data management.
Epical helps you get your data AI-ready
How prepared is your data for AI? Do you have the structures and security in place to take the next step?
At Epical, we’ve developed processes to organize and optimize business data, enabling you to make better decisions faster. With our help, you can build a solid foundation for using AI on your business data and accelerate your operations.
Welcome to book a meeting with us to explore how you can take the next step toward becoming AI-ready.
Fredric Travaglia is a Business Architect in the Integration division at Epical, with over 25 years of experience in IT and business operations. He has helped companies enhance their integration solutions and maximize the value of their data. Feel free to book a meeting with Fredric via the link below or contact him directly by email fredric.travaglia@epicalgroup.com.
