The Importance of Data Quality and Business Intelligence
In the current business environment, Artificial Intelligence (AI) is one of the main technological trends. Companies have shown a strong interest in developing internal models and connections with AI technologies, enabling growth and innovation in organizations.
Machine learning (ML) is not new, it has existed since 1950~. However, before moving towards implementing AI, companies must understand the need to have a solid data foundation and have a properly structured Business Intelligence (BI) and data analysis team.
1. Data Structuring and Quality: The First Step Towards AI
AI initiatives depend directly on the quality and availability of data. Before wanting to implement AI models, it is essential that companies carry out careful work of structuring and knowledge of this information.
This involves organizing, cleaning, and standardizing data in an appropriate format for analysis. By establishing a solid structure for their data, companies will be building a robust and reliable pillar to start thinking about the application of AI.
2. It’s Not Just About Tools, GPT Chat, and Colorful Dashboards
Data analysis is not just about using Power BI, Tableau, Excel, and Python, it is possible to achieve the same result in all of them. It is much more about human intelligence and knowledge applied to the interpretation and understanding of data. Although tools facilitate the process, it is the ability to extract relevant insights and make strategic decisions that make the difference.
This requires advanced analytical skills, the ability to identify patterns and trends, as well as knowledge of the business context. Not only knowing how to create a bar chart or avoid the pie chart. It is necessary to know how to formulate the right questions, select the appropriate metrics, and interpret the results to generate value.
3. BI and Analytics Team: Empowering Artificial Intelligence
A qualified and well-structured Business Intelligence team is a fundamental asset for the success of artificial intelligence initiatives. This team is responsible for analyzing data, validating the knowledge, and translating this information into strategic insights for the organization.
These professionals will work side by side with data scientists and engineers, ensuring that the analyses are accurate and relevant, and provide accurate data to start train and test of ML models.
With this well-defined structure, companies will be well-positioned to achieve the benefits of artificial intelligence:
- Greater efficiency and accuracy in data analysis and utilization, ensuring the reliability and quality of information before applying AI;
- Ability to extract valuable insights, identify emerging trends, and produce a solid and reliable foundation for training and testing AI models;
- Simplification of the AI deployment process, reducing risks and optimizing resources, with the potential to develop more advanced and sophisticated models, leveraging the full potential of technology.
If you are considering implementing Artificial Intelligence in your organization, make sure to establish a solid foundation through proper data structuring and a qualified team.
These elements are essential to obtain reliable, relevant, and innovative results through AI. It may even be that you don’t need it, just a well-done analysis can be your goal.