Data analysis and business intelligence

Data analysis and business intelligence

Modern companies generate vast amounts of data, yet a significant portion remains unused. According to some estimates, up to 55% of corporate data goes unutilised, even though analysing this information can be crucial for informed decision-making and business intelligence. This is why well-executed data analysis is becoming increasingly important in corporate operations.

Data analysis in 7 steps

Data analysis is a structured process that consists of seven main steps. 

1. Goal setting
2. Data collection
3. Data organisation
4. Data cleaning
5. Analysis
6. Data visualization
7. Conclusion

Now, let's take a closer look at the steps of data analysis.

1. Goal setting

The first step is to clearly define the objectives. This determines what data is needed and the criteria by which it will be analyzed. It is important to formulate a hypothesis that will be either confirmed or rejected during the analysis. 

2. Data collection 

Data collection can come from internal sources such as databases, surveys, or emails, as well as external sources like social media, reviews, or reports. In the era of Big Data, the volume and diversity of data are constantly increasing, making data collection increasingly complex. 

3. Data organisation

The collected data must be organized for better transparency. It is advisable to create documentation and remove unnecessary or redundant data to make the subsequent analysis easier. 

4. Data cleaning

Data cleaning (along with the previous phase of data collection) is one of the most time-consuming steps. For textual data, typos and irrelevant responses need to be filtered out, while for numerical data, outliers must be identified and missing values addressed. 

5. Analysis

During the analysis, different methods can be applied depending on the objectives. These include descriptive statistics, regression analysis, time series analysis, classification, and clustering. With the help of these techniques, we can examine whether the formulated hypothesis is valid. 

6. Data visualization

Presenting the results is essential for decision-makers. Data visualization tools such as Power BI or Tableau help display data in a clear and comprehensible manner. For more specific charts, programming languages like Python or R can also be used. 

7. Conclusion

Based on the analysis results, we draw conclusions that support better decision-making and contribute to the advancement of business intelligence.

Data analysis and artificial intelligence

Artificial Intelligence (AI) significantly enhances data analysis through the following methodologies: 

• Machine Learning: They can recognize patterns and make predictions. Classification, regression, and clustering are fundamental techniques. 

• Neural Networks:They are capable of recognizing complex patterns, particularly in image recognition and time series analysis. 

• Deep Learning: A method used for processing large datasets (a key technology in big data analysis), which is also applied in the development of Large Language Models (LLMs) in Generative Artificial Intelligence. It is not typically used for everyday analytical tasks but is essential for creating analysis-supporting tools.  

The future of data analysis

The Artificial Intelligence (AI) already plays a significant role in data analysis today. AI-driven algorithms can quickly process large amounts of data and recognize complex patterns, enabling real-time analysis and more accurate predictions. By applying machine learning, the accuracy of forecasts and classifications can be further enhanced. 

In the future, the emergence of quantum computers could further revolutionize data analysis. These machines will be capable of performing extremely complex calculations in a short time, opening new horizons in data analysis and business intelligence. 

Final thoughts

In summary, the data analysis process is crucial for unlocking the value within data and making well-informed business decisions. The continuous advancement of technology, particularly in artificial intelligence and quantum computing, offers new opportunities to enhance the efficiency and accuracy of data analysis.