<!–Uday Deb
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Businesses in virtually every industry are focusing on data exploitation to gain a competitive edge, thanks to the volume of data presently available. It has become more difficult to analyse large amounts of data manually or even with standard databases. Because of advances in computing power and widespread use of networking, it is now possible to conduct research of unprecedented depth and breadth using algorithms that connect databases. As a result of the convergence of these phenomena, data science is currently being applied in an increasing number of commercial applications.
Data-driven decision making (DDD), refers to the process of making decisions based on data analysis rather than intuition. A marketer, for example, could choose advertisements solely based on their extensive knowledge of the industry, and their intuition for what will be successful. Alternatively, they could base their decision on the findings of a data analysis of how customers respond to various advertisements. They may also use a combination of the strategies. DDD is not an all-or-nothing strategy; rather, different companies engage in it to varying degrees and intensities.
There are essential principles that drive the methodical extraction of information and knowledge from data using data science techniques. Data mining is perhaps the most closely related concept to data science since it involves the actual extraction of information from data using technologies that adhere to these principles. There are hundreds of distinct data-mining algorithms and a large degree of methodological depth in the subject. However, a far smaller and more compact set of fundamental principles lies behind all these specifics.
Data science encompasses the principles, procedures, and methods for comprehending phenomena through (automated) data analysis. Recently, economist Erik Brynjolfsson and his colleagues from MIT and Penn’s Wharton School did a study on the impact of DDD on corporate performance. They established a DDD metric that grades companies based on the extent to which they use data to make company-wide decisions.
Data-driven companies are more productive, even when accounting for a wide range of potential confounding circumstances. This is demonstrated statistically. This is shown statistically. As a result, a single standard variation higher on the DDD index is linked to a 4–6 percent increase in productivity. Additionally, DDD relates to higher asset utilisation, market value, return on equity, and return on assets, and the relationship appears to be causative.
Data scientists have identified a set of core concepts governing the pragmatic extraction of knowledge from data in relation to big data and data-driven decision-making.
- Data extraction methodology: A predefined method of data extraction.
- Data evaluation considering context: Data collected will be analysed based on the expected findings.
- Data buckets: Using the framework of analysing expected value, the relationship between the business issue and the analytics solution can frequently be deconstructed into workable sub problems.
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