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How do smart algorithms lead us?

1.How do the smart algorithms lead us?.

2.How does Minimize Myopia help to improve accuracy?.

3.What could be the most effective input data for algorithm?.

4.What is the importance of identifying the limitations of Algorithm?.

1. Business Management of the bigger organisation needs different step by step operations to identify the most valuable employees, the most effective market and to predict future market trends that help the company to take beneficial decisions. However, from HRM system to setting-up effective logistics this logical step by step operation is very time and energy consuming. Therefore, in order to make this system more efficient, less time consuming and more accurate in advance  business management environment numerous business organisations are using algorithms to execute the step b step operation to predict most beneficial decision for the organisations (Buckingham 2012). The term ‘smart algorithm’ means making situational alteration within the algorithm and its input data set. In diverse and independent business operation use of algorithm make the overall business management system smart and productive.

The behaviours of this algorithmic process are very different from the general pattern of thinking and logical analysis done by human mind. An algorithmic operation like automated voice call, market analysis and other automated tasks makes the business operation much easier for numerous business organisations. However, in spite of being mathematically extremely correct, the algorithmic predictions ware failed entirely to predict consumer behaviour in many cases. The main reason behind this condition is lack of knowledge and operating capabilities of the business organisation that can make the prediction and analysis of these algorithms more accurate and useful (Krolzig 2013). The existing algorisms are only capable of monitoring and calculating literal fact, it cannot predict human emotions and psychological fluctuation. Therefore, this advancement of the business operation can lead either to ultimate success or complete failure depending on how the organizations are pursuing their ability of prediction.

2. The term ‘Myopia’ is also known as nearsightedness that means lack of capability to visualize the objects properly which is situated very far from the observer. One of the essential limitations of the algorithm is lack of ability to predict long-term future. The algorithm is a variable dependent process that relies only on initial variable and constants. Any situational changes during the execution of the algorithm do not have any impact on the potential outcomes (Margaris, Georgiadis and Vassilakis 2015).  The algorithms are able to predict the near future which can project only primary results for many situational variables. The logical integrity of these algorithms is suitable to predict short-term outcomes, like a market prediction for a situational product or service. The reason behind this disability is the initial variables and auto-generated variable of any predicting algorithm that store temporary information is not the optimum resource for predicting long-term outcomes. The major problem is, the algorithms are not capable of analysing anything but their pre-programmed logic.

How does Minimize Myopia help improve accuracy?


The term ‘Minimize Algorithm' refers the activities or intention to reduce the operational failure in future prediction of the algorithms. Minimizing the Myopia of any algorithm requires more several and auto-generated variables that can minimize the nearsightedness and maximize the boundaries of future prediction (Williamson et al. 2015). However, the algorithm can easily predict the primary reaction of potential consumer segment as outcomes of a specific promotional strategy or advertisement. Trends of website visitors are a short-term consumer behaviour which can be easily predicted by the algorithm. Despite that, the algorithm is constructive for comparing different consumer feedbacks and reviews and identifying the most beneficial product for a market segment. Additionally, this phenomenon of the algorithm can be used to determine the most appropriate user for conducting pay-per-click based e-promotions for clients (Hair et al. 2014).

3. An algorithm has a significant disadvantage concerning their lack of informing power about different logical outcomes. An algorithm can predict or generate only single output for the most optimised decision, while it cannot show the internal processing of the information and the logical interaction of cause and effects. Therefore the selection of proper inputs is very crucial and regulatory part to run any algorithm efficiently for gaining more accurate analysis or future predictions. The lack of objective analysis and data prioritising power of the algorithm often cause loss of valuable information or interpretations during the logical operation within it. However, the algorithm can aim towards particular set objectives having a precise philosophy of observation and description. Lack of clarity and diversity of inputs variable and constants can cause a vivid and distorted prediction about most straightforward outcomes (Wenand and Zhou 2012).

In some cases, the algorithm is only capable of priorities different constants as per their utilisation in previous cases. These prioritizations of different constants can become more logical instead of being more practical that causes ultimate failure of future prediction or identification within different constants. However, the algorithm is constructive for comparing different consumer feedbacks and reviews and identifying the most beneficial product for a market segment. The underlying reason behind this is, in this analysis, the algorithm has to process the data and information based on only the constant data and fixed variables. Therefore it is obvious that choosing the right data as input noticeably differentiate the potential outcomes of algorithm-based social prediction like prediction consumer behaviour for a particular product which is going to be launched.

What could be the most effective input data for algorithms?

4. As per various reports of business operations, it is clear that algorithm needs precise and accurate inputs and optional variables from the real time operational field to calculate the most accurate prediction. Similarly, an algorithm is also capable of making a correct prediction without eliminating any connection between different cause and effect relations. Proper accessibility of managerial activities within the initial and secondary state logical operation is adequate for predicting any outcomes with the appropriate set of objectives and executable cardinalities through a proper understanding of the limitation of the existing algorithm (Chen, Chiang and Storey 2012). The algorithm needs precise and accurate inputs and optional variables from the real time operational field to calculate the most accurate prediction. Each data set has unique perspective and mode of operation. Therefore management should analyse the effectiveness of the selected inputs to generate most appropriate analysis or prediction from an algorithm.

From various failure cases, it can be stated that using the algorithm in the broader subject requires lots of data and information with appropriate synchronisation and interconnection. However, in these situations, the results generated by a particular algorithm are more like free choices rather than some confident prediction. Therefore management has the significant responsibility to handle the data set including both variable and constants in order to gain more accurate analysis form the systematic execution of the operational algorithm (Gomez-Uribe and Hunt 2016). Moreover, limitation of an algorithm can be identified by analysing the difference between its previous functional components and the current set of various data and information. Henceforth, the identification of operational limitation and boundaries of the individual algorithm can help to find the most valuable dataset that to run the algorithm efficiently.

References:

Basso, C., Vaidhyanathan, N., Verrilli, C.B., Walk, B.M. and Wind, D., International Business Machines Corp, 2016. Flow distribution algorithm for aggregated links in an ethernet switch. U.S. Patent 9,438,447.

Brandle, R.T., International Business Machines Corp, 2013. Hash algorithm using randomization function. U.S. Patent 8,595,273.

Buckingham, M., 2012. Leadership Development in the Age of the Algorithm. Harvard Business Review, 90(6), pp.86-94.

Cao, B., Wang, J., Fan, J., Dong, T. and Yin, J., 2015, June. Mapping elements with the hungarian algorithm: an efficient method for querying business process models. In Web Services (ICWS), 2015 IEEE International Conference on (pp. 129-136). IEEE.

Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: from big data to big impact. MIS quarterly, pp.1165-1188.

Chen, P.W., Lin, W.Y., Huang, T.H. and Pan, W.T., 2013. Using fruit fly optimization algorithm optimized grey model neural network to perform satisfaction analysis for e-business service. Applied mathematics & information sciences, 7(2L), pp.459-465.

Hair Jr, J., Sarstedt, M., Hopkins, L. and G. Kuppelwieser, V., 2014. Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European Business Review, 26(2), pp.106-121.

Gomez-Uribe, C.A. and Hunt, N., 2016. The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), p.13.

Krolzig, H.M., 2013. Markov-switching vector autoregressions: Modelling, statistical inference, and application to business cycle analysis (Vol. 454). Springer Science & Business Media.

Luca, M., Kleinberg, J. and Mullainathan, S., 2016. Algorithms need managers, too. Harvard business review, 94(1), p.20.

Margaris, D., Georgiadis, P. and Vassilakis, C., 2015, May. A collaborative filtering algorithm with clustering for personalized web service selection in business processes. In Research Challenges in Information Science (RCIS), 2015 IEEE 9th International Conference on (pp. 169-180). IEEE.

Martens, A., Fettke, P. and Loos, P., 2014. A genetic algorithm for the inductive derivation of reference models using minimal graph-edit distance applied to real-world business process data. Tagungsband Multikonferenz Wirtschaftsinformatik, pp.1613-1626.

Thrun, S. and Pratt, L. eds., 2012. Learning to learn. Springer Science & Business Media.

Tomita, M. ed., 2012. Generalized LR parsing. Springer Science & Business Media.

Wen, J. and Zhou, W., 2012. An improved item-based collaborative filtering algorithm based on clustering method. Journal of Computational Information Systems, 8(2), pp.571-578.

Williamson, D., Cooke, P., Jenkins, W. and Moreton, K.M., 2013. Strategic management and business analysis. Routledge.

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