The book super crunchers by Ian Ayres informs us of the application of simple statistical models to large data sets. These models have been gaining popularity in improving decision making in various applications including business and professional orientation. The increased use of these models is an indication of their supremacy over the rationality expressed by knowledgeable experts, in duties such as diagnosing and prescribing treatment for diseases and coming up with methods of pricing the airline seats (Ayres 12).
Other common fields that make use of these models include the rating of the creditworthiness of a person and matching partners for relationships. According to the second chapter, the naming of the book was aimed at getting more views by top listing in search engines for searches of words like number crunching and data mining. The two words refer to the statistical study of huge datasets (Ayres 29).
The difference between this method of data analysis from the traditional one is its ability work on large datasets, therefore making the models suitable for arriving at crucial decisions, regarding businesses and governments (Ayres 213). The second chapter also introduces us to the association of experimental design with data mining in various organizations, using illustrations such as the use of experimental design in credit card companies and the issue of compensating passengers of a particular airline. The sample used in chapter three, which discusses unemployment insurance experiments, depicts more of randomization in drawing conclusions as opposed to data mining (Ayres 44).
Research conducted by IDC
A study was conducted by IDC on decision making by large organizations around the world, and the influence of business analytics in the process. The research showed that a significant number of organizations were keen in obtaining varied information, though just about half of it was useful in decision making. It was also observed that well over 50% of organizations used business analytics regularly in their decision making, which required them to have real-time access of information (Gantz, Morris and Vesset 2).
This information is in the form of images, documents and emails, and is mostly unstructured, making it difficult to understand its context based on its location. The information was manually obtained in about sixty percent of the organizations studied. The primary use of business analytics was in the support of client-related duties, whereby they occupied multiple departments and applications (Gantz, Morris and Vesset 1).
The research indicated that less than 20% of the organizations had managers who were well informed on the importance of intelligence technology in business. About 30% of organizations made decisions based on intuition without basing their actions on any data. From the data obtained, it can be deduced that many organizations are increasingly becoming reliant on business intelligence, with much fear on the continued good performance of their organizations if the systems failed for one day, or just one hour (Gantz, Morris and Vesset 3).
Information technology revolution
The interaction between businesses and clients has been greatly influenced by E-commerce and the information technology revolution. The advancements in information technology have made it possible to involve the customers in business operations, making it easy for them to access company information. This implies that businesses have become more client oriented, focusing more on the needs of the patrons in order to provide them with in-depth service. Ayres discussed the arrival of decisions using simple statistical models based on three incidences (Aronson 50).
Circumstances allowing for the access and processing of data
In some instances, the data may be available for the manipulation and interpretation of information systems, though inhibitors limit the degree to which such information can be used. For successful and appropriate models, the information used should be relevant. Another important factor in the access of information is the freedom provided by information laws. The laws in place may protect confidentiality and consider that the release of some information may result in exposing particular individuals to insecurities, by sharing their information. Otherwise, with good fortune, appropriate database and access to data, suitable models can be obtained, relevant enough to meet the needs (Aronson 50).
Generation of data from planned experiments
When the data used in simple statistical modeling is obtained from regulated tests and research, the models obtained can be valid and significant. In chapter four, Ayres tells us of evidence based medicine. The diagnosis and treatments were obtained from collecting information from drug-company trials and the results on patients. Other examples of models that have emerged from research are evidence-based teaching, whose source of information came from the study of the accomplishment of children under differing learning systems (Ayres 157), and the credit company Capone, which obtains its business data by comparing the responses of individuals who obtain offers of new products and contract terms from them, and those who do not receive the offers (Ayres 41).
Modeling the arrival of decisions by experts
This method does not involve the direct construction of models, but structures the path used by experts to arrive at their decisions. This method focuses on finding out the most appropriate and significant forecasts of professional judgments. These models that are based on the path of decision arrival by experts have been found to surpass the opinions of experts. The experts have been found to be inconsistent and forgetful, unlike the computers (Ayres 121).
Implications of increased use of quantitative modeling
Decision making and marketing is largely based on quantitative modeling, which implies that commercial firms and government agencies frequently gather and distribute information regarding many people. The sharing of personal information increases chances of cybercrimes including identity theft, due to reduced personal privacy. Another implication of the continued use of machine-generated instructions is the reduction of social status of the experts.
An example is the unwillingness of doctors in adhering to the rules generated, in spite of their superior results for patients (Aronson 51). The same behavior has been identified in teachers who are aware of the improved teaching patterns, as outlined by numerous researches, but remain fixed to old teaching ways. Research in teaching has showed increased student performance when instructions are issued directly, but the technique denies the teachers creativity. The use of super crunching is unlikely to diminish, since people are in need of the best and most reliable advice and insights. The biggest challenge to the administrators of various institutions and organizations is to encourage their people to adapt to the instructions given by the machines (Aronson 51).
Aronson, Jason. “Clinical versus statistical prediction.” A theoretical theoretical (1996): 10(1), 50-52. Print.
Ayres, Ian. Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart. New York City: Bantam Books, 2008. Print.
Gantz, John, et al. Taming Information Chaos. A State-of-the-Art Report on the Use of Business Intelligence for Decision Making. New York: IDC, 2007. Print.