End-2-End Predictive Modeling Process (ModelOps/MLOps)

Predictive Analytics is essential for companies to harness data-driven insights, enabling informed decision-making. It helps in risk management, improving customer satisfaction, optimizing operations, and increasing revenue. Important aspects include: Enhancing decision-making processes Identifying future trends Optimizing resource allocation Improving customer experience Mitigating potential risks Predictive Analytics plays a crucial role in enhancing decision-making processes by providing insights into future trends and potential outcomes. Companies can anticipate market changes, adapt strategies, and allocate resources efficiently. It aids in identifying patterns from historical data, allowing organizations to forecast future demand, optimize inventory, and reduce costs.The importance of Predictive Analytics extends to customer experience. By analyzing customer data, companies can personalize interactions, predict customer needs, and improve satisfaction rates. This proactive approach fosters loyalty and drives sales. Additionally, Predictive Analytics helps in risk mitigation by predicting potential threats or outages, thus ensuring business continuity. Companies across industries leverage these capabilities to maintain a competitive edge and enhance operational efficiency.

OLAP is a precise method belonging to Data mining.  Data mining is a broader term covering a number of methods (one of them is OLAP). OLAP can be run: in software, you can ask for OLAP in a similar way you ask for correlations, regressions, and other statistics. OLAP is in a software menu (or it is a function in a language) (some packages will use “cube” or “pivot” keywords to get the same output). Instead, you cannot ask for a Data Mining execution because there is no output associated with “Data mining”, you have to invoke a method belonging to Data mining to get an output such as regression trees, association rules, clusters, support vector machines: each one has its execution in a software. In a book on Data mining, you can find a number of methods, OLAP is one of them. For instance, in the book “Data mining: concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. – 3rd ed. ISBN 978-0-12-381479-1 “, OLAP is just a section of the (Data mining) book. OLAP summarizes/aggregates by the group while other data mining methods will try to find hidden patterns from non-aggregated data (= the detail is king). OLAP would approach managerial information as the summary is welcome, other data mining methods would approach research as the detail is required.

Great question @Evgeny Belenky. Thank you @Xavier Suriol ​for the answer.  OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing in the online mode, whereas “Data Mining” is a broader topic to mine massive data to find meaningful patterns out of the data by using various analytical techniques or framework such as CRISP-DM, Statical Techniques, Predictive Modeling techniques, exploratory data analysis, etc.  Data Mining can be done both in the online and offline mode. Sometimes, in data mining, there would not be any predefined objective.