BUS5PB Principles of Business Analytics : Prospects and Attributes

Theories of the contribution of the brain in decision making are conventionally derived from the economic theories of decision making criteria. In the expected utility theory options are singled and associated with a numerical value to define its utility before the decision maker goes for the option that maximises utility. From the theory, we can naturally conclude that the brain can estimate the optical choices by assigning calculations around the numerical utility values for avoidable choices.

Under the stable scale-based theories utility is defines as internal psychological value which is numerically measurable and optimisable, Since the ordinality revolution the modern economics have majorly been circumspect stating that people do make choices in a way that shows them to be consulting a stable utility scale within them but the contemporary theories are applying more of a psychological utility concept (Brown, 2008). Th only similarity between the two models being that they all consider the utility to be independent of other options.

Realistically computing the value of a complex choice is hard but should such values can be generated then decision making is easy. The stable stability models can explain why the choices may change systematically with deviation of states

This decision theories still assume the notion of scaled for utility. The models are build a round comparisons that allow context interference at the level of whole prospects and attributes. The models are therefore able to consider effect of context in different ways under economics. The Fehr and Schmidt’s model of inequity aversion assumes that a number related to a certain reward is reduced to the level that the rewards are unequally distributed on the other hand model of income satisfaction takes the assumption that value does not just come from an income but from the relationship of the income with the mean reference level, income or to the relative position in terms of rank within a whole distribution of income (Tversky, 1972). These accounts notably assume additional terms in the equation that computes utility. Most of the models under psychology preserve the notion that values of whole options are calculated even though they give room to context effects in many of ways.  Some models assign values to individual option even though the calculation of their utility from the features may be interfered with by attributes of other objects in the set of choices. Regret theory states that individuals first represent the utility of the result of prospect before modifying it by the anticipated feelings of regret they may be experiencing on the outcome in a similar way as per Tyersky and Simonson’s a popular componential-context model of context- dependent preference every object attribute has a number that only depends on its magnitude.

Another sub-class of models skips computation of objects value and instead choices are based on integrating only the relative quantities of various attributes. These comparisons only compare model like attribute across the choices hence the choice between an options is taken in relative rather than absolute figures, this gives way to explanation of several context effects (Vlaev, 2007).

In the comparison with scales model we retain the assumption that value of any of the attributes is computed and the outcomes inform decision. Now a third stronger option is the possibility of dispensing the idea of stable internal scale values so that comparison is the only operation undertake. Hence models under this type of theories have no scale and considers only ordinal comparison. The comparative technique doesn’t assume observable binary options are generated by consulting a stable internal value. Though the extent of comparison against similar context will cause stability.

The value-based theories only explain the contexts by assuming relativity of utilities in the other options in the decision. Dues to some researchers these outcomes result from inability of human to represent absolute scales.  The latest comparative theory assume that individuals decide with no consultation a utility scale based on the absolute magnitudes of stimuli this applies to even the key economic variables like time and probability. These models say that absolute differences between variable levels are not considers and various processing rules may be applicable to the direct comparison of objective numbers (Ivo Vlaev, 2011).

Business analytics is a wide term that entails capabilities and solutions that advantage several disciplines. Being that business analytics is applicable in all decision making it is therefore viewed as an important function of business. The need for cooperation between firms as well as effective supervisory measures have increased the demand for analytics in the business environment.

Analytics have been around for ages but its only 5-10 years ago that business decision making integrated its use. This largely attributable to the advancement of technology that have seen the rise of big data. This data is collected from everywhere and with the development of advance analytical tools professionals can now convert larger volumes of data into information that can be used to make business decision that are effective in running the operations of the firm (ComputerWorld, 2009).

With analytics firms can now make decisions and strategies based on data rather than feelings. With the quick means of analysing the big data firms can now keep information on consumer needs close to the reality in the market. With this effective decision can be made almost instantly and therefore reducing cost and growing the profits.

In the study conducted by Deloitte he stated that decision makers who can leverage daily data and information into actionable insights for developing their firms by, making dependable decisions have a higher probability of rising higher in terms of achievement of strategic growth.

There is excess information in the world today and mere feeling can no longer be effective in making crucial decisions for this purpose analytics will help cut out the clutter to enable organizations to make better decisions (Abhishek S., 2017).

The purpose of descriptive, inferential techniques and probability theory in formulating decisions

Majority of daily business decisions made are related to some probabilistic aspects. Even though the focus is concentrated around formulas and statistical calculations used in the definition of probability on the background these we have the general ideologies that determine the magnitude of effect which determine the probability. Combined statistics and probabilistic concepts are vital for decision making in uncertain conditions.

Probability ideas are abstract concepts used in the definition of magnitude of risk that involve business decisions. Probability calculations can be made on a random sample or full data. Be it designing new products, streamlining the manufacturing process or analysing current vs potential customers in the contemporary business world the managers faces more complexities than ever experienced. Statistics therefore boost the confidence of the managers in dealing with the uncertain situations amidst the flood of data presence therefore enabling him to make faster decisions which are effective and hence provision of stable leadership to his staff.

Statistical analysis of sample of consumers can be a source of reasonable correct and cheaper way of viewing the market with faster and cost effective statistics than when an overall census is carried out for all the consumers a company is intact with. Using statistics, the manager can formulate strategies via unbiased approach (Ungemach, 2011).

In negotiations statistics is essential in supporting your decisions. For this purpose, a manger may find himself relying on statistics in situations such as convincing the shareholders to take a certain risk. Statistic identifies relationships, careful review of a sample of data can be a way of getting a link between two situations. This link might prove to be essential is satisfying certain situations which brought complexity in the decision making.

Quality control and consistency in production are necessary for continuation of a business, to determine the scale for measuring these statistics is the key. This role of statistics make it vital when it comes to standardisation of products quality as well as when we want to make uniform quantitative decisions. This saves the organization the expenses associated with surplus production and manufacturing products which are below per.

Expectation of performance management

Performance management is a term used to define processes set by the managers to make all the employees aware of the standard expected of them. For the performance management to produce favourable results it need to focus the employees of the on the overall organizations objectives as well as individual targets. The process entails a feedback system that enable the managers to give response immediately to an employee performance and that way performance can be corrected at the right time.  This close working relationship is necessary for the employee’s development to the set standards by the firm. The system may involve a remuneration and reward system for purpose of motivation and encouraging continuous improvement in performance. For this purpose, the management target of motivated employees who are dedicated towards the achievement of the firm’s goals is achieved. Also, employee retention is achieved.

The performance management is put in place to raise an organisation with well-defined targets and standards that are integrated within each employee’s system. For this purpose, the employees find is easy to make decisions regarding their operations simply by following the guidelines. The use of performance management is targeted towards increasing the competitiveness of the firm. To achieve this the activities of the employees, need to be aligned so that there exists a scale through which the managers may measure performeance and so in an easy situation when it comes to standardization of output.

Conclusion

Human brain has a unique way of making decisions. Even though scientists and economics are yet to come up with exact procedure by which it makes choices several theories have been modelled to try to explain the process. Though they vary in approaches one feature which tend to b uniform in them is the comparison nature of the decision making by the brain. Based on these findings we can integrate the use of analytics in this decision process. For the brain to formulate the utilities it will need a basis for comparing, analytics provides the data necessary for the computation of the utility number. By use of big data and modern analysing techniques the human brain can be assisted to make sense from the magnitude of data available in the business world and so improves the decision making from mere feeling based to fact supported. This is an efficient way of improving performance of the organisation as well as gives the managers the backup for instilling their decision on employees as well as evidence in case of negotiations.

References

Abhishek S., B., 2017. What is the role of business analytics in today's business scenario?. [Online] Available at: https://www.quora.com/What-is-the-role-of-business-analytics-in-todays-business-scenario[Accessed 02 June 2017].

Brown, G., 2008. Does wage rank affect employees’ well being?. Ind. Relat. , Volume 47, pp. 47, 355–389.

ComputerWorld, 2009. Defining Business Analytics and Its Impact On Organizational Decision-Making, s.l.: MarketVibe.

Ivo Vlaev, N. C. N. S. a. G. D. B., 2011. Does the brain calculate value?, London: Centre for Health Policy, Imperial College London.

Tversky, A., 1972. Elimination by aspects: a theory of choice. Psycho Rev, Volume 79, p. 271–299.

Ungemach, C., 2011. How incidental values from our environment affect decisions about money, risk, and delay.. Psychol. Sci. , Volume 22, p. 243–260.

Vlaev, I., 2007. Financial prospect relativity: context effects in financial decision making under risk.. J. Behav. Decis. Making , Volume 20, p. 263–324.