KIM WOODS ONLY Assignment 2: LASA 2—Business Analytics Implementation Plan Part 2

Business Analytics Implementation Plan

Student

Argosy University

Professor Audibert

Data Driven Decision Making

MGT334

May 25 2016

Table of contents

Introduction and business analytics summary………………………………… .3

Benefits and disadvantages of business analytics………………………………..4

Challenges the organization may face using business analytics…………………5

· How these challenges can be addressed proactively

Business analytic techniques …………………………………………………….7

Implementation plan to integrate business analytics into your organization…......8

Back up proposal…………………………………………………………………10

Conclusion ……………………………………………………………………….11

References ……………………………………………………………………….12

Business Analytics Implementation Plan

Dazzle Designs Inc. Business Analytics Implementation Plan.

Company introduction and summary of Business Analytics.

Dazzle Designs Inc. is a medium sized design firm in New York with a very busy work environment. The agency deals with a wide variety of designs including: graphic design, marketing communications, media management, environment design and digital marketing. Apart from these five major functions, the firm is also known to be a powerhouse in advertising consultancy in the state of New York. The agency has very renowned clients in New York as well as multiple states across the country.

Among its wide network of clients and customers is The New York Times which is a popular media publication agency both here in the United States and abroad. To facilitate its activities and operations, the firm has superior technology but the systems are not connected. Further, the agency’s databases are independent of each other while it uses server or client environment (Chen, Chiang, & Storey, 2012). Despite the use of technology in the running of daily operations (for instance data analysis) the firm does not use business analytics.

Business analytics (BA) refers to practices, skills and technologies used for continuous iterative investigation and exploration of the past performances of the business to accrue insight and drive formation of optimized predictive techniques that could be used in business planning. The techniques arrived at should be communicated to the customers, business shareholders and other partners for results preview. Operations at Dazzle is likely to be the biggest gainer after the implementation of the BA systems.

Application of business analytics in helps operation managers with adequate information to make smarter business decisions (Chen, Chiang, & Storey, 2012). For example, the operation department is likely to understand group or individual productivity and therefore allocate effectively designed headcounts of budgets. Secondly, since the firm intends to increase its presence in a second location, sales and marketing is another key area set to benefit from the implementation of business analytics. The business analytics will help the company in identifying sales trends, provide relevant metrics to gauge growth and loss, and quantify patterns in customer buying (Chen, Chiang, & Storey, 2012). All these aspects can be used by the agency in making smarter decisions in inventory management, adverts spending and usage of resources.

The benefits and disadvantages of business analytics

Several firms have cited the numerous benefits associated with business analytics. Firstly, it helps in faster time to insight (Provost, & Fawcett, 2013). The modern marketplace and business environment is very competitive. As such intelligent business decisions must be quickly made. Business analytics provides a platform to make quick, informed and intelligent business decisions, which can be used in seizing new market opportunities (Provost, & Fawcett, 2013). Secondly, business analytics plays a role in customer retention. Delivery of quality service and good relations is key in retaining customers. Since business analytics is able to identify relationship- threatening challenges, firms can therefore use it in predicting these challenges, address them and find solutions before their effects are felt. Hence, customers can be retained.

Business analytics intelligence aids organizations in improving business planning. According to FYI, more than 19 percent of mid-sized businesses mention that analytics increase business certainty, improve planning and reduce business risks (Provost, & Fawcett, 2013).

Operation managers are the biggest beneficiaries of business analytics since information gained from the system can be used to make smart decisions, which obviously improve efficiency and effectiveness of business (Provost, & Fawcett, 2013) for instance, a better understanding of group productivity could lead to better budgetary allocations.

However, business analytics may also have some disadvantages. First, identifying the correct data to be collected may be a real challenge. Secondly, the data collected and the analysed data can be so abstract and difficult to understand. Business analytics can be very costly for the organization in terms of resources, expertise and technology equipment (Provost, & Fawcett, 2013).

Challenges the organization may face using business analytics

Midsized companies face several challenges in implementing business analytics in their operations. Dazzle Designs Agency is not an exception. The firm cites the cost of implementing the integration of business analytics as a real challenge. Recently, FYI solutions conducted a research study which revealed that more than 30 percent of mid-sized business cite cost as the greatest challenge of adoption of business analytics. The economic conditions have put several technology projects on hold. Therefore, implementing the plan has been avoided for several years because of the capital expenditures involved (Provost, & Fawcett, 2013).

Another factor is inadequate staffing. More than 15 percent of mid-sized companies interviewed by FYI Solutions in the survey explain that they do not have the required in-house expertise to implement business analytics (Provost, & Fawcett, 2013). If additional staff with Business Analytics skills were to be employed, the agency would incur additional compensation costs, such as benefits, salaries and other rewards associated.

Lastly the complexity involved with Business Analytics deployment discourages its implementation. Citing again the survey conducted by FYI, about 14 percent of the respondents found the business analytics systems very complex to implement, maintain and upgrade. Implementing these systems would require the IT department to be completely overhauled, and a lot of time spent on individual projects (Provost, & Fawcett, 2013). The firm also fears that implementation of the business analytics systems would consume valuable resources from the IT department, which would have been used in other departments. Connected with the complexity in BA deployment is the huge amounts of raw data which needs to be processed to get useful results for decision making.

So how can these challenges be addressed proactively?

Almost every organization faces financial constraints when implementing a new project. However, Dazzle can make earlier preparations in their budgetary allocation to include business analytics deployment in the organization (Provost, & Fawcett, 2013). Setting aside special funds for the implementation of this plan can be a solution. For instance, the other budgetary allocations in their annual budget can be slashed to accommodate the project.

Secondly, employee expenses can be cut by using internal expertise. How? Instead of employing new staff with business analytics skills, the human resource department can undertake to train the existing employees in the IT department specifically for the deployment of the project (Saaty, & Vargas, 2013). For instance, if a special IT group can be trained exclusively for the project, then their services can be used in the deployment of the project. This way, it reduces the expenses that could have been used in outsourcing for outside experts.

Complexity and huge amounts of raw data require the services of experienced IT professionals trained on business analytics. Training a special IT group from the firm will not only solve the problem of staffing but also address the system complexity issue. After training, the professionals will be able to handle the data with top notch professionalism. Lastly, the firm can begin to process small amounts of data, then progress to huge data with time as it grows.

Business analytic techniques

The first technique is regression modelling and analysis which can be used in predicting customer patterns and increasing the client base. Suppose the operations manager intends to attract new customers, a model can be built using new customers, and several other variables related to the new potentials (Saaty, & Vargas, 2013). These variables could be; profession, income group and the age of the customers and used as predictor variables, while the customer base could be the response variable. Using consumer data and statistical techniques, a model can be used to develop this relationship. Advantages of this technique is its expansive application and the high accuracy levels that it presents when well used (Saaty, & Vargas, 2013). However, the technique is limited when it comes to handling a huge amount of data. Secondly, its use requires a vast experience and knowledge in statistical applications, which can be complex.

Time series forecasting is another technique. There are several applications of this technique. Very popular is weather analysis, but stock market, profit and loss analysis and sales are areas where the technique can be used (Saaty, & Vargas, 2013). Compared to regression modelling and analysis, time series forecasting is a rather simple form of predicting technique. Secondly, the technique time series forecasting can predict seasonality in business performance which lacks in multiple regression and analysis of models. One of its popular applications is sales forecasting. However, the technique requires historical data to predict future trends. Historical data could sometimes be misleading since economic times and the business environment is changing. Secondly, sometimes the predicted patterns may not yield as a result of unprecedented market conditions such as inflation, or hostile business environment.

Lastly, conjoint analysis can be used in conducting market research for the company, to determine features, products or pricing most likely to be attractive to their old and new customers and therefore make appropriate business decisions (Saaty, & Vargas, 2013). Compared to the two techniques discussed above, conjoint analysis is more reliable since the research team from the firm has to target the respondents with the new products and services exhibiting different features and price levels. Statistical techniques are then applied in determining the contribution of the additional features on the product, and how they are able to influence the customers to buy. From these analyses, a model is then built to estimate market share. Potential revenue and profitability.

Implementation plan to integrate business analytics into your organization.

An effective integration of business analytics in the organization comes with increased profitability, creation of new business opportunities, decreased company risks and enhanced client experience among several other benefits. Before the implementation, testing and piloting the relevant technologies required for the process is important. The very first important step in the implementation of the plan is choosing the right technology that will be required for the entire process (Saaty, & Vargas, 2013). For instance, the right computers, computer software, communication and internet connectivity is must be initially set up to effectively handle the project. Once the firm identifies the right analytics tools and database software, with the right technology infrastructure in place, it will be able to move to the next stage of developing a real strategy for its business analytics integration.

Firstly, the plan will choose which data to be included and what to be left out. Business analytics data can be very large and voluminous. However, not all the company data will be relevant for business analytics. Therefore, the firm will have to identify the relevant strategic data intrinsically important for analytical insights. For instance, what set of data is important for sales prediction patterns? Basically, this stage of the implementation will largely depend on the firm’s goals and objectives (Sharma, Mithas, & Kankanhalli, 2014).

Secondly the plan will build effective business rules and create a platform through which it will be able to work through the complexities created. Business analytics deals with large data sets and therefore can be very complex. Therefore, the ability to cope with these complexities is key in gathering the right analytical tools essential for integrating technology with the business dynamics. The business rules enable the firm to stick to the complexities despite the frustrations they pose.

Thirdly, the business rules are translated into workable analytics in a collaborative environment. The analytic professionals and the IT people draft analytical questions, and algorithms which will be used in generating desired results. During the development of the algorithms, the teams should have free collaboration and communication in the analytics development process (Sharma, Mithas, & Kankanhalli, 2014). The next component of the plan is maintenance. The business analytics systems need to be regularly attention and updating. Maintenance should be done regularly, during which new technologies are integrated in the system. During the maintenance, the team must evaluate the software and hardware additions necessary for supporting changes in the dynamic business environment.

Lastly, ongoing training is an important part of the implementation strategy. New knowledge is available every day, and therefore the plan must have an ongoing training program for development of a robust IT department, and the system development team (Sharma, Mithas, & Kankanhalli, 2014). The business and technical aspects must be taken into account to ensure that firms get the desirable outcomes are achieved from the business analytics program.

Back up proposal.

The back-up proposal for the implementation plan should the first one fail, is based on four aspects: plan, discover, act and embed. At the planning stage, the details of the project are drafted. The relevant resources and expertise for the project are gathered. For instance, here, the firm will have to assemble the required team of experts, including IT professionals to effect the project (Sharma, Mithas, & Kankanhalli, 2014). Additionally, at this stage, relevant executive involvement is required. The role of the executive at this stage is to provide financial sponsorship. Effective communication channels must be created at this stage to ensure that the goals and objectives of the project are well articulated to the team.

The second stage of the plan is discovering. Here, an analytics development environment is built to create (develop), test and fine-tune the initial analytics set. The initial results allow the team to come up with a realistic business impact and make improvements based on the recommendations (Sharma, Mithas, & Kankanhalli, 2014). Suppose there are signs of early success, the plan should be pursued further to give better results. The success signs also motivates the team to improve on the initial analytics.

Thirdly, the firm then rolls out the execution of the plan. After the first and second stages, the firm is now ready to execute the entire plan using the big data sets (Sharma, Mithas, & Kankanhalli, 2014). The last step is to embed the integration plan and the analytics in the firm. embedding the analytics capability into the company include far reaching impacts to people, processes, data, technology, applications and the firm.

References

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.

Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. “O’Reilly Media, Inc.".

Saaty, T. L., & Vargas, L. G. (2013). Decision making with the analytic network process: economic, political, social and technological applications with benefits, opportunities, costs and risks (Vol. 195). Springer Science & Business Media.

Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433-441.