The Business Value of Analytics

A New Way of Working

The nature of business is changing, bringing with it a new way to compete. Keeping up with the demands of today’s technical workforce requires a method to create value and move quickly. Deliver speed and simplicity to your users while maintaining optimal levels of data quality and security. A centralized analytics platform, where IT plays a pivotal role, should be a cornerstone of your enterprise analytics strategy. Balancing business-driven and IT-focused initiatives is the ideal formula for fostering innovation.

Advances in analytics technology are unlocking new opportunities to leverage your data. Modern analytics is predictive, self-learning, and adaptive, enabling you to uncover hidden data patterns. It is also intuitive, featuring exceptional visualizations that help you interpret millions of rows and columns of data in an instant. Modern business analytics is mobile and easy to use, delivering the right data at the right time with little to no training required.

You want to spot data signals before your competitors do. Analytics gives you a high-definition view of your business landscape. By combining personal, corporate, and big data, you can quickly grasp their value and share insights with your team—all within minutes.

IoT + Analytics

1. Selection: The first stage of the data analysis process is the selection of relevant data. In this phase, data sources are identified and collected to be used for analysis, ensuring that the data is suitable and aligned with the project objectives. The quality and relevance of the data at this stage are crucial for the success of the analysis.

2. Preprocessing: In this phase, the selected data is prepared for analysis. Data preprocessing includes cleaning, error correction, and the removal of irrelevant or redundant information. It may also involve imputing missing values and normalizing or scaling the data to ensure that it is ready to be effectively transformed and analyzed.

3. Transformation: In the transformation layer, the preprocessed data is modified or converted into a format suitable for data mining and subsequent analysis. This includes aggregation, generalization, or dimensionality reduction, which facilitates the extraction of useful patterns and reduces data complexity without losing valuable information.

4. Data Mining: Data mining is the core of data analysis. Here, advanced algorithms and techniques are used to uncover hidden patterns, relationships, or trends in the data. Through methods such as machine learning, clustering, classification, and regression, valuable information is extracted that can be used to generate insights or make informed decisions.

5. Evaluation: After extracting patterns in the data mining phase, it is crucial to evaluate them to determine their validity and usefulness. In this evaluation stage, the results obtained are checked, and their relevance with respect to the initial objectives is analyzed. This may involve measuring accuracy, interpreting the results, and comparing them with previous models or hypotheses.

6. Knowledge: Finally, the knowledge layer refers to the interpretation of the results obtained in the analytical process. In this stage, the processed and validated information is transformed into useful knowledge, which can be used for strategic decision-making, process optimization, or creating new business opportunities. It is here that data is transformed into tangible value for the organization.