Empowering Strategic Decision-Making: Exploring the Role of Business Analytics in the Era of Internet of Things

Introduction

In the context of the contemporary business environment, the approach of business analytics facilitates different tools and tactics to examine a variety of information within the strategic planning process, including business dissertation help. Moreover, the demand for data analytics is significantly enhanced because companies consider different interest-based technologies to manage the distinct business operation (Yerpude and Singhal, 2017). It influences management to apply different measures of statistical analysis and data mining for examining a variety of information. Therefore, this report investigates the role of business analytics to promote the Internet of Things. It also determines the piratical implications of different methods of business analytics in the forecasting, profit estimation, demand assessment and selection of optimum pricing approach.

Task One- Business Analytics Concepts

a. Critically analyzing and explain the Internet of Things and the role of business analytics in IoT.

Internet of Things

The Internet of Things (IoT) is an important element of data sciences. It seems a great platform to manage the integration of several devices, networks, technologies and operations of human resources to achieve a common goal. For improving the operational efficiency, companies consider a variety of IoT-based application to establish better coordination among different business operations (Jernigan, Ransbotham and Kiron, 2016). Lee and Lee (2015) stated that the effectiveness of IoT technologies is significantly influenced by the optimum management of information and sharing of data. Therefore, IoT and data remain intrinsically linked together so as there has significant increment identified in the growth rate of data consumption. Joseph (2018) asserted that the influx of data is encouraged by the widespread adoption of IoT, so the total number of IoT connected devices would be reached by 30.73 billion until 2020. Furthermore, there is a significant increment identified in internet traffic due to the increase in the number of intelligent devices.

The Role of Business Analytics in IoT

The approach of Business Analytics (BA) is defined as a process that is applied by companies to examine big and small data sets that are having distinct properties to extract meaningful conclusions so as companies can plan long-short term business plans and managerial strategies. The assessment of information is mainly carried out in the form of trends, patterns along with statistics (Marjani and et al., 2017). This system supports business organizations to implement effective decision-making. It leaves a direct impact on the efficiency of business operations and the integration of IoT. This is because the effectiveness of IoT applications is significantly influenced by the reliability of different concepts of business analytics. In the context, the approach of data analytics assists the manager to examine huge clusters of data sets that are applied in IoT.

The investigation of Kotu and Deshpande (2014) has found that the application of business analytics in IoT investments will allow companies to assess detailed insights about the change in customer preference. Therefore, organizations would be able to develop different kinds of customized services to attain expectations of customers that could enhance the profit earning capabilities of a different firm. Verma and et al. (2017) asserted that IoT devices are worked on different sensors, networks, and software technologies. Therefore, the performance of IoT devices is highly dependent on the optimum management of information and the evaluation of data trends.

The utilization of data analytics would be found very useful to improve the efficiency of IoT applications that would offer several benefits to companies in the form of gain improved revenues, competitive gain, and customer engagement. It also assists business entities to assess new opportunities related to technological advancement by establishing a partnership with other companies (Yerpude and Singhal, 2017). IoT is playing a critical role in reducing the reliance of the information management process from humans to machines so as companies can improve the effectiveness of information processing tools that are playing an important to maximize the efficiency of different integrated devices. IoT is going to bring a revolutionary change in Business Analytics through the integration of different information processing approaches that could leave a positive impact on overall organizational efficiency (Jernigan, Ransbotham and Kiron, 2016).

b. Critically analyze and explain the Text Mining concepts, applications, and tools. Also, Text Mining steps should be described properly

Text Mining concepts, applications, and tools

The text mining concept is mainly applied to transform the unstructured data and information within meaning numeric indices. It is mainly carried out by applying different algorithms. By applying the approach of text and data mining, companies consider different sources of information to generate reliable information to attain business planning goals (Rangra and Bansal, 2014. However, Shmueli and et al. (2017) argued that text mining requires significant expertise identification of appropriate data sets and selection of different variables to establish an appropriate relationship in different data sets to generate reliable outcomes.

Torgo (2016) stated that the concept of test mining had gained huge popularity in recent years. This is because various companies associated with different industries apply different tools and measures to manage different tasks of information management. In the context of the communication industry, it offers great support to companies to predict customer behaviour. The study of He and et al. (2017) has examined the application of text mining in different industries. This assessment has found that service-sector industries like banking, insurance, finance, and others use text mining to investigate the current market trends related to expectations of the consumer, needs of people, impact on seasonality on the spending of people and many more. Therefore, the approach of texting mining would be identified very useful to different companies in the selection of marketing channels, promotional mix, product portfolio and others with reference to contemporary industry trends (Yerpude and Singhal, 2017). Furthermore, the approach of text mining is all about explaining the past and predicting the future corporate trends.

In the context of text mining, companies consider different tools of information management such as R-Language and Oracle Data Mining. R language is identified as an open-source tool for statistical computing, and it offers great support in statistical tests, time-series analysis, classification of data and the application of different graphical techniques (Rangra and Bansal, 2014). On the other hand, Oracle Data Mining is also termed as ODM that is termed an important element of the Oracle Advanced Analytics Database. This tool offers great support to data analysts for assessing detailed insights and makes predictions so as an organisation can easily predict customer behaviour and can identify new cross-selling opportunities (Shmueli and et al., 2017).

Text Mining Steps

The first stage of testing is Test Pre-Processing, that is applied to remove unwanted information from particular data sets.

The second step is Text Transformation in which a text document is represented by the words with two different approaches, such as Bag of Words and Vector Space. The third section is known as Feature Selection.

In the third part, the data examiner selects different variables to establish an appropriate model for information management (Yerpude and Singhal, 2017).

Data mining is termed as the fourth stage of Text mining process in which text mining merges with the traditional process of information with the help of a structured database.

The fifth stage is called Evaluate in which data is examined by using different tactics of statistical analysis (Jernigan, Ransbotham and Kiron, 2016).

The last step is associated with the Applications in which an organization uses the final results to attain different business goals.

Task Two- Forecasting

a. Use the data set in table 2 to compute the four-period moving average and enter your values in the appropriate columns.

Presented in excel sheet.

b. Plot the data and describe the main features of the series

As per the above chart, the plotting of forecasted and actual sales figures determines that the organization has addressed stable growth in sales per the forecast. It shows a stable proportion of sales growth. However, the actual sales figures determine significant up-downs in the total sales of the company.

c. Calculate the Centered Moving Average (CMA)/Baseline. Interpret it.

As per the above chart, the baseline can be considered as an important indicator of the average sale. In the present case, the baseline remains fixed at 900, and significant variations are identified in actual sales. However, actual sale is floated near to baseline with stable growth.

d. Calculate the Trend and interpret the trend

As per the above chart, the assessment of sales figures between the period of 2012-2019 has recorded positive trends because the company has maintained stable growth in profit every year. It would lead to a positive impact on overall business efficiency and profitability.

e. Determine the Seasonality (St) and interpret it properly

In the context of time series analysis, the approach of seasonality determines seasonal variation that would be occurred in a systematic cycle. It is also termed as repeated patterns that are identified in a certain period of time-frame. The assessment of sales data in the present case has determined that the business entity has recorded seasonal variation in the 3rd quarter of every year in which the organisation generates maximum revenue. It is significantly influenced by market trends and seasonal variation.

f. Forecast the revenue for the year 2020

The assessment sales trends of the company determine that the company would maintain positive growth in revenue in 2020. According to growth rate, the sales of the company in 1st, 2nd, 3rd and 4th quarter would be reached respectively 1109.44, 1120.71, 1131.99 and 1143.26.

g. Calculate the Error, mean absolute deviation (MAD) error, Mean Square Error (MSE) and Mean Absolute percentage error (MAPE).

Present in Excel.

h. Writing a brief report to explain and evaluate and make comments on the error variables, the forecasted and actual revenues.

In the context of forecasting, error variables are used to determine the difference between the forecasted sales and actual sales. The positive error indicates that the actual sales figures are higher than forecasted sales. However, the negative error that the actual sales figures are lower than forecasted sales. In the present case, the maximum value of positive error is 129, and the maximum value of negative error is near to -106. Therefore, there is significant variation identified in the actual and forecasted sales figures.

Task Three- Marketing Analysis

a) Determination of the estimated equation function of demand according to the price in a quadratic form

Equation Y(Demand)= 0.27x2-69.68x+4720

b) Finding the optimal price which maximizes company profit

According to Excel Solver, the Optimum Price of product is £71 per unit that would maximise the company’s prfit.

c) Determine the optimal demand.

The Excel Solver identifies the Optimal Demand of 1130 Units.

d) Compute the optimal profit.

As per the optimal price of £71, optimal demand of 1130 units and the unit cost of £35, the value of optima profit is £40817.

Profit = (Price-Cost)* Demand

= (71-35)*1130

Profit = £40817

e) Interpret the results.

In the context of present case, business entity would maximise its profit when company sells the product at £71 per unit to attain the demand of 1130 units. It plays an important role to enhance overall business profitability.

f) If the supply cost of each new product is 10, 15, 20, 25, determine the optimal price and analyze it properly.

As per the able table, if business entity fixes the profit £40817 with the demand of 1130 units then the unit price of product will be changed with reference to cost. It shows the positive relationship between price and cost to attain optimum profit.

Conclusion

As per the above assessment, this report concludes that IoT has gained significant popularity in the contemporary business environment. This study has found that companies use different tactics of business analytics to examine the data and manage the flow of information for ensuring the optimum integration of IoT devices. Therefore, it has concluded that the approach of business analytic plays an important to increase the effectiveness of business operations with the help of high-tech tools. The report also examines different aspects of text mining and found that it assists companies in the assessment of future business trends, forecasting of sales figures and evaluation of the change in customer’s interest. Therefore, it has concluded that business analytics has been emerged as a great tool to improve the effectiveness of the decision-making process.

Reference

He, Z. and et.al. (2017). Conditional discriminative pattern mining: Concepts and algorithms. Information Sciences, 375, 1-15.

Jernigan, S., Ransbotham, S., and Kiron, D. (2016). Data sharing and analytics drive success with IOT. MIT Sloan Management Review, 58(1), 1-17.

Kotu, V., and Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer. Morgan Kaufmann.

Lee, I., and Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440.

Marjani, M. and et.al. (2017). Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access, 5, 5247-5261.

Rangra, K., and Bansal, K. L. (2014). Comparative study of data mining tools. International journal of advanced research in computer science and software engineering, 4(6).

Shmueli, G. and et.al. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley and Sons.

Torgo, L. (2016). Data mining with R: learning with case studies. CRC press.

Verma, S. and et.al. (2017). A survey on network methodologies for real-time analytics of massive IoT data and open research issues. IEEE Communications Surveys and Tutorials, 19(3), 1457-1477.

Yerpude, S., and Singhal, T. K. (2017). Internet of Things and its impact on Business Analytics. Indian Journal of Science and Technology, 10(5), 1-6.

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