Introduction to Data-Analytic Thinking

Introduction to Data-Analytic Thinking

1. Different enterprises have invested in the last years in business infrastructure, which have improved the ability to collect data and are focused on exploiting data for competitive advantage. Nowadays, computers can make a deeper analysis than any other person could make.

2. Applications of data mining techniques: -In marketing to analyze customer behavior. -In finance for credit scoring and trading, and in operations via fraud detection. -In major retailers, form marketing to supply-chain management.

3. Data Science

   • Is a set of fundamental principles that guide the extraction of knowledge from data. It involves different principles, processes and techniques for understanding phenomena via the analysis of data.
     • One of these principles is: data, and the capability to extract useful knowledge from data, should be regarded as key strategic assets.
       • Some of the fundamental concepts are: *Extracting useful knowledge from data to solve business problems can be treated systematically by following a process with reasonably well-defined stages. *From a large mass of data, information technology can be used to find informative descriptive attributes of entities of interest. *If you look too hard at a set of data, you will find something-but it might not generalize beyond the data you’re looking at. *Formulating data mining solutions and evaluating the results involves thinking carefully about the context in which they will be used.
         • Data engineering and processing are critical to support data science. Data science needs access to data and it often benefits from sophisticated data engineering that data processing technologies may facilitate.
     • Data mining is practically the same, but data mining techniques provide some of the clearest illustrations of the principles of data science.

4. Data-driver decision-making (DDD)

   • It refers to the practice of basing decisions on the analysis of data, rather than purely on intuition. Statically, the more data-driven a firm is, the more productive it is. DDD is also correlated with higher return on assets, return on equity, asset utilization, and market value.

5. Big Data

   • It refers to datasets that are too large for traditional data processing systems, and therefore require new processing technologies.
     • Big data technologies are used for data engineering, implementing data mining techniques or for data processing in support of the data mining techniques and other data science activities. Using this technology is associated with significant additional productivity growth.