ROLES AND IMPORTANCE OF BUSINESS ANALYTICS IN INDUSTRIES

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    Management
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    Masters
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ROLES AND IMPORTANCE OF BUSINESS ANALYSTICS 8

ROLES AND IMPORTANCE OF BUSINESS ANALYTICS IN INDUSTRIES

ABSTRACT

Business analytics have been done for many years by different organizations to help them run their organizations effectively and with high expectations that their businesses will not collapse due to any reasons. Business analytics help businesses discover challenges that they might go through during the running of it businesses hence giving businesses time to plan for them and how to withstand this challenges.

Keywords: analytics, organizations, roles

Introduction

Different authors have different definitions of a business analytics, however; business analytics can be defined as the practices of iterative exploration of data in statistical analysis to gain insight and drive the business plans. It can therefore, be used to gain insights informing businesses decisions which automate the businesses processes. However, data-driven companies use these data as an asset and used for competitive advantage.

Business analytics steps

When developing an extensive review report of business analytics in industries we have to consider various steps. First, we clarify our roles as the business analyst. Hence, we create deliverables that meet your industry’s needs. This will help determine the primary stakeholders to engage in the industry’s business objective and scope. Furthermore, building more in the understanding of the industry’s history and avoiding inadvertently repeat work that’s done already. Hence understanding the existing systems in the industry so as to have an idea of the current state of the industry and what needs to be change.

Secondly, discovering the primary industry’s objectives should also be considered in the extensive report these is because discovering the primary industry’s objectives is helpful in defining the scope that ensures the industry end up with solutions thus solving the problem hence success to the industry.

Thirdly is to determine a scope that will help in determining solutions and the extent of technology to the industry’s ability to process change to make it active in implementing the solutions to the industry main objectives. Scope drafting statements and reviewing them with the industry’s business stakeholders will help prepare business analytics. Confirming the industry’s business case will ensure that it makes sense for the industries to do the business analytics review report? The scope is not only an implementation plan but a subsequent step of the business analysis process.

Formulating a business analytical plan will help bring clarity to the business analytic process hence, determining the detailed requirements for the industry’s analytics report. Also, choosing an appropriate type of business analytic with relevant deliverables having been given: the project scope, methodology, and other key aspects to be used in the industry’s report view analysis.

The detailed requirement will provide information needed by the industry to come up with correct information regarding the industry’s business analytics. When detailed requirements are provided regarding the industry eliciting information necessary to understand what the industry needs for specific feature or process change. After the information is discovered, it is used to create drafts that will be needed during the business analytics. However, reviewing and validating the deliverables with appropriate business and technology departments in the industry.

Supporting the implementation of the business analytic review report these are because it will help come up with relevant solutions facing the industry. The analytic report is expected to review the solution design to ensure fulfillment of all the requirements and look for opportunities that will help meet other business needs.

Implementing solutions in the business analysis report that theoretically meets the businesses objectives. However, all the changes may not be implemented due to various reasons that articulate to the changes needed to be done in the business process. After the implementation training users will be necessary to ensure that they understand all the processes and procedural changes hence, collaborating with the training staff so as to provide appropriate training materials delivering during training.

Finally, at the end of business analytic, it is important to assess value created by the solution. The outcomes are to be discussed. Details have been worked through and problems solved building a relationship to manage change hence; technology implemented creating a need to train users on how to use the current technology at the workplace. However, coming up with follow-up projects and initiative objectives would help discover and evaluate the impact of this project.

Descriptive statistics

Itdescribes the features of data in study thus providing summaries between samples and measures graphical analytics formed from basis of the virtual quantitative analysis of data. It also describes what the data is showing. However, inferential statistics help to reach conclusions which can extend beyond data. They present quantitative descriptions in forms that are manageable. Thus helps simplify large amounts of data in various ways. Thus this statistics help reduces data to a simpler summary.

It also can be done to help calculate the frequency of an individual’s range of values. Example, a way to describe the distribution of students in a university doing their Ph.D. within the years can be done when we list the number and percentage of students at each year of study.

Category percentage

Under 35 9

Frequency distribution table

A common way to describe variation is by use of a frequency distribution table. However, depending on the variables data values may be grouped in different categories. they can be depicted in different ways either by using a tables or graphs.

The central tendency therefore estimates the center value in the frequency distribution table. Major estimates for central tendency are the mean, median and mode. The mean however is commonly used to describe the central tendency. Therefore, to calculate mean we add all the values and divide with the total number of values. Let’s consider the following scores values:

16, 21,22,21,37,16,24,16 hence the mean can be calculating the total score value which is divided by the total test scores which is 8 therefore the mean is 173÷8 is 21.625

The median is the value found at the middle of the values. To compute the medium we list all the values in a numerical order from the least to the greatest and then pick the value at the center. However, if the middle value is more than one score like in even total scores we add the two numbers then divide by two to get the median. Using the example above the median of the numbers can be calculated.

16, 16,16,21,21,22,26,37 the median values are two we add them as explained above i.e. 21 therefore 21+21 is 42 divide by two 21 hence 21 is the value which can be said to be the median score.

Finally mode is the most frequent value occurring in the values. To compute the mode we check the score appearing most. Using our example, 16 is our median since it appears three times.

The dispersion can also be calculated which is said to be the values around the central tendency. However, the common measure of dispersion is said to be the range of the standard deviation. The range can be computed when we take the highest value less lowest value in the set of values as in our example the highest been 37 and lowest 16 therefore, 37-16=21 is the value of the range.

The standard deviation is said to be the most accurate measure of dispersion because an outlier may exaggerate the range. It also shows the relation of values to the mean of the sample. Using our previous example

16, 21,22,21,37,16,24,16

To calculate standard deviation first we find the difference between the value and the mean. Our mean is 21.625. Therefore, the difference from the mean is:

16 — 21.625 = -5.625

21 — 21.625 = -0.625

22 — 21.625 = +0.375

21 – 21.625= -0.625

37 – 21.625 = 15.375

16 – 21.625 = -5.625

24 – 21.625 = +2.375

16 – 21.625 = -5.625

It’s noted that the values below the mean have negative discrepancies while as the values above the mean are positive. Lastly we square each value of discrepancy:

-5.625 × -5.625 = 31.641

-0.625 × -0.625 = 0.3906

+0.375 × +0.375 = 0.1406

-0.625 × -0.625 = 0.3906

15.375 × 15.375 = 236.3906

-5.625 × -5.6.25 = 31.6406

+2.375 × +2.375 = 5.6406

-5.625 × -5.625 = 31.6406

We then take the sum of the squares and add them to get the sum which is 337.8749. Then divide by the sum of values less 1. As for our case 337.8749 ÷ 8 = 42.2344 this value is said to be the variance. Hence, to calculate the standard variation we find the square root of the variance which is the square root of (42.2344) = 6.4988

Prescriptive analytic

These analytic reveal actions to be taken. It is the most valuable kind of analysis and results in rules and recommendations for the next action but largely not used. Since organizations use predictive analytic but only a few use prescriptive analysis.

Its roles and functions include; advice on possible outcomes, helping users to prescribe different possible actions which help them towards finding a solution. Prescriptive analytic provides advice. It tends to quantify the effects of decisions hence, advising on possible outcomes before making the decisions. However, it predicts not only what will happen but also answers the question why it might happens to provide recommendations on actions that will help take advantage of the predictions. Prescriptive analytic goes beyond predictive analytic since it recommends more than one possible courses of the action.

Assumptions include: in the case of any outcome the information they gave as their prediction will apply in the particular case. They also assume that in case a risk occurs the next step as indicated in the prescriptive analysis will not affect the businesses performance whatever.

In the prescriptive analytic method, validation is done if the solutions given for the next step can be confirmed to be suitable for its use. These results can be used to judge the quality, reliability, and consistency hence can be said the procedure was successful.

Predictive analytics

We define predictive analytics as a method of using past patterns to predict the future. However, predictive analytics is based on probabilities. In predictive analytic it about understanding the future hence companies are able to come up with actionable insights on data.

The statistics also tries to keep data that is available thus filling the missing data. They also combine historical data identifying various patterns in data thus applying statistical models, therefore, capturing the relationships between data sets. It is used by companies whenever they want to predict the future. Furthermore, this analytic can also be used throughout organizations, from forecasting purchasing patterns and customer behavior to identifying various trends in sales activities thus helping in forecasting demand for inputs from the various departments example: supply chain hence operations and inventories run smoothly.

The roles and functions include producing a credit score which is useful to financial services to determine certain probabilities example: determining probability of customer making payments on time. Other uses include: predicting how sales will close at the end of the year, predicting items customers will purchase or forecasting inventory levels based on variables.

Assumptions of this analytic include: the predictions made will be the exact figures at the end of the predicted time. All factors held constant will not affect the predictions done by the organization example: the number of customers expected during the year by an organization.

The predictive method of validation can be proven if the organization finds that the predictions made are figures either which were predicted or close to the figures predicted at the beginning of the year.

Organizations samples can be used when performing a predictive analytic to your organization this will help see that the sample cases used can be a correct information which can be applied in current organizations.

Descriptive analytics

This analytic describes raw data making it interpretable by humans hence describing the past. They are useful since they help us learn from past behaviors and make us understand how they can influence future outcomes. Descriptive analytic are therefore provide historical insights regarding company’s production, operations, sales, finance inventory and customers.

The roles and functions of Descriptive analytic are used to show, the total inventory stock, average spendings per customer and yearly changes in sales. The descriptive analysis also helps insight into the past of the organization. Descriptive analytic assumptions include: any past behaviors will not affect the organization since these behaviors helped them learn from them. Also, data interpreted will be useful to the organization.

Diagnostic analytics

They are used for discovery or determining why something happened. It is characterized by techniques such as data discovery, data mining, correlations, and drill-down. However, diagnostic analytic is a very laborious work which has limited ability to give actionable insights hence providing a very good understanding of limited problems to be solved.

Roles and functions of Diagnostic analytics include: understanding data fast helping answer critical workforce questions. Providing the fastest and simplest way organizations ca use gaining insight to their employees and solve workforce issues. Diagnostic analytics assumptions include: the cause of the poor performance of the organization is the only reason that caused poor performance of the organization. The reason must have been predicted by the predictive analytic, and its solution must have overseen in the prescriptive analysis.

Diagnostic analytic can be validated if the reason that caused poor performance was discovered during the diagnostic analytics thus this analytic method is effective and can be used in organizations to come with business analytics. A sample case can be done if previous researchers show that information obtained from diagnostic analytic answers the question why something happened to the organization and if the cause was predicted by relative business analytic.

Business analytics have played a major role in making sure those businesses can run smoothly without the fear of their collapse or incurring losses that may cause the closer of the business. Business analytic also helps businesses come up analytic styles that help them run businesses smoothly.

References

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Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly36(4), 1165-1188.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan management review52(2), 21.

Sahay, B. S., & Ranjan, J. (2008). Real-time business intelligence in supply chain analytics. Information Management & Computer Security16(1), 28-48.

Trkman, P., McCormack, K., De Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems49(3), 318- 327.