Business analytics implementation plan for a design firm

Running head: BUSINESS ANALYTICS 1

BUSINESS ANALYTICS 9

Business Analytics and Decision Making

Hakim Callahan

Argosy University

Contents Introduction and Company Summary 3 Summary of Business Analytics 3 Benefits and Shortcomings of Business Analytics 4 Challenges of Applying Business Analytics 5 Business Analytics Techniques 6 a) Predictive Analytics 6 b) Decision Analytics 7 c) Descriptive Analytics 7 Implementation Plan 8 Backup Implementation Plan 9 Conclusion 9 References 10

Business Analytics and Decision Making

Introduction and Company Summary
Business analytics is a platform for integrating technology, skills, and practices in exploring previous business events. BA is mainly important in forging the future through gathered insights and formulated business plans. In this context, business analytics will be applied to a design firm that has the resources of technology but does not engage in data analysis. Additionally, the design firm has not interconnected its technology systems, and as a result, the databases are independent. Looking forward, the company is aiming at opening a second branch of business. In rectifying the operational performance of the company in context, it is necessary that the company utilizes business analytics in the functions of decision making, description of historical data and predicting. The primary goal of this paper is to identify the role of business analytics in decision making.

Summary of Business Analytics
The business in context has been defined above, and this section will work towards providing a summary of business analytics required in decision making. Before engaging in business analytics, it is important that the firm integrates its technology platform into a single system. Integrating technological components will help the firm to centralize its data for purposes of business analytics (Evans & Lindner, 2012). Primary, this firm can apply business analytics in analyzing historical data for purposes of developing trends that will help in the decision-making process. The multiple instances that the business analytics can be applied include understanding resource allocation, identifying the optimal number of employees and identifying the appropriate marketing mix. These scenarios will be critical in the opening of a new business branch that will yield optimal positive outcomes. Therefore business analytics will be applied towards predicting and simulating business conditions that will provide the optimal set of conditions.

Benefits and Shortcomings of Business Analytics
Considering that the main goal of businesses is to provide valuable services and products to customers, business analytics provide a competitive advantage when appropriately utilized. The competitive advantage is provided through the alignment of business functions towards achieving consistent performance metrics. Secondly, the business analytics also help in making business information more understandable and usable towards decision making (Evans & Lindner, 2012). Concerning the company in context, it could be having a great of information and data, but it can hardly draw any meaningful information for decision making. A better understanding is created through the visualization of data in the form of graphs and simulations. Thirdly, business analytics will also enable the company to become more agile and adaptable to the current dynamic business environments (Evans & Lindner, 2012). Agility and adaptability are created through the ability of analytics to develop forecasts. Subsequently, both agility and ease of understanding information also help in optimizing the competitive advantage of a company.

However, business analytics is also accompanied by disadvantages that arise during their utilization and maintenance. One of the disadvantages of applying business analytics is the complexity of models and data. A certain level of complexity might hamper rather than assist in decision making. This disadvantage can be related to the over expectation of analytical models and software. In coping with this disadvantage, a business can use a top-down approach that aims at maximizing output while saving time. The top-down approach enables easier workflows, unlike the general approach. Secondly, there is also the disadvantage of huge piles of historical data. Since business analytics thrive in the presence of historical data, companies might encounter the drawback of having to store large amounts of data (Holsapple, Lee-post & Pakath, 2014). In proactively handling this problem of mass data accumulation, it is important that the firm increases its storage spaces and even deleting some of the old historical data. The latter solution could effectively work under a policy that defines the relativity of historical information (Holsapple, Lee-post & Pakath, 2014). Thirdly, there is also the disadvantage of high installation and maintenance costs for the business analytic systems. Companies should ensure that it trains its employees on the use of the business analytic systems. As a proactive measure against costs, business will ensure that the systems are operated optimally to avoid damages and ineffective utilities.

Challenges of Applying Business Analytics
During the utilization of business analytics, it is possible that companies may encounter implementation challenges. One of the challenges is the element of information maturity. Information maturity relates to the quality of data used in the analytical systems. Poor-quality data subsequently lead to poor decision making. In proactively coping with this challenge, it is important that mapped data sources are verified in ensuring the inflow of data is quality. Additionally, it is important to integrate data collection for purposes of obtaining more meaningful information (Holsapple, Lee-post & Pakath, 2014). Secondly, there is the challenge of model usability which is caused by employees who do not have adequate information about the operations of business analytic models. In handling the challenge of an ignorant workforce, it is important that training is comprehensively conducted for purposes of empowering the employees with skills and knowledge on business analytics models. Thirdly, there is the challenge of applying inappropriate analytical models and systems. In some cases, the applied models are either too complex or too simplified for the data available for analysis (Holsapple, Lee-post & Pakath, 2014). It is therefore recommended that companies identify their analytical needs and the data available to them for purposes of applying the most appropriate model for business analytics.

Business Analytics Techniques
Regarding the current scenario, the three business techniques that can be employed by the design company are predictive analytics, decision analytics, and descriptive analytics.

a) Predictive Analytics
Beginning with the predictive type of business analytics, they are an approach used in leveraging the historical data for purposes of making scientific inferences of future possibilities (LaValle et al., 2011). Under the predictive business analytics, it is possible to use either output or a combination of both the model and output.

Advantages

· Predictive analytics enable a business to conduct risk management.

· Predictive analytics also build causal relationships between the business performance and variables in the operating environments.

Disadvantages

· Low-quality data might yield poor predictive abilities.

· Misinterpretations and wrong data could lead to counterproductive decision making (LaValle et al., 2011).

b) Decision Analytics
Secondly, there is decision analytics which in most cases are intertwined with the prescriptive analytics. Decision and prescriptive analytics enable visualizations of analytics (LaValle et al., 2011). Again, these set of analytics target making decisions for future engagements.

Advantages

· They provide optimal sets of resource combinations for best performances.

· Company managements are also able to picture and visualize their ideas in future. This function is enabled through simulations (LaValle et al., 2011).

Disadvantages

· It is almost impossible to bring together all causal factors together. This challenge is due to the dynamic nature of the business environment which continually introduces new affecting factors.

· Larger action sets yield to poor optimization (LaValle et al., 2011).

c) Descriptive Analytics
Thirdly, descriptive analytics play the role of analyzing historical data and creating trend-visuals such as charts and graphs (Schläfke, Silvi, & Möller, 2012). Descriptive analytics occur in the forms of either exploratory or focused.

Advantages

· Descriptive analytics provide data consistency for purposes of developing a relevant business narrative.

· Descriptive analytics also simplify historical data analysis through visualized trends.

Disadvantages

· It can only study a maximum of three variables in a given time.

· Descriptive analytics are not sufficient on their own in the presence of many variables leading to complexity of data analysis (Schläfke, Silvi, & Möller, 2012).

Implementation Plan
Towards integrating the business analytics into the business operations, it is critical that the design company utilizes the most optimal model of integration. Based on the purpose of optimizing the positives of a business analytics, the following implementation plan will be used:

· Step One: Identification of the issue to be solved. Concerning the design firm, the business problem is the wise decision of opening an operations branch.

· Step two: Establishing the performance metrics to use. Some of the metrics to apply include customer satisfaction, and company profitability.

· Step three: Integrating the technology platforms including the databases for a unitary data pipeline.

· Step four: Conducting simulations of the expected opening of the second location.

· Step five: Analyzing and comparing both historical data and predicted data.

· Step six: Compiling a report on the business analytic outcomes.

Backup Implementation Plan
If the above implementation plan is not approved by management, the following backup implementation plan can be applied:

· Step one: Identifying the purpose of the business analytics.

· Step two: Integrating the technological platform including the databases in creating a unitary data pipeline.

· Step three: Installing business analytic software for use by different employees.

· Step four: Training company staff on how to use the installed software to reap optimal results from its utility.

· Step five: Running simulations of sample business scenarios in establishing its effectiveness.

· Step Six: Evaluating the implementation results and making amends where necessary.

Conclusion
In general, the above analysis and proposal on the implementation of business analytics are resourceful in enabling objective decision making. Heading into the future, the design firm management will be able to apply purpose to its predictions and forecasts rather than solely depending on intuitions. Regarding the second business location of the design firm, it will be possible to apply the appropriate resource combination in making the project viable. Eventually, the integration of business analytics into business operations enables companies to develop business narratives based on data consistency that even help in making decisions.

References
Evans, J. R., & Lindner, C. H. (2012). Business analytics: The next frontier for decision sciences. Decision Line, 43(2), 4-6.

Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130-141.

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 review, 52(2), 21.

Schläfke, M., Silvi, R., & Möller, K. (2012). A framework for business analytics in performance management. International Journal of Productivity and Performance Management, 62(1), 110-122.