Use the following format to write your case study analysis:

1. Provide a brief summary listing the key facts in the case.
2. Identify key issues and identify one problem you wish to resolve in your analysis. The problem statement should be clear and concise. A problem statement is typically no more than one sentence in length.
3. List recommended solutions or strategies to resolve the problem. Be sure to include the who, what, why, when, where and how of each solution or strategy you propose.
4. Analyze each recommended solution or strategy by linking information from course materials and outside research sources. List the pros and cons of each solution. Choose the best solution or strategy and list an explanation why it is the best choice. The reasons for your choice should be clearly linked to your analysis.
5. Write a conclusion to your analysis. Use the questions posed at the end of your case study written in your text to guide your conclusion.

Case Two : Sunny Delight Improves Profitability with a Self-Service BI Solution

When implementing a self-service analytics program, information systems staff and end users across an organization often must be willing to give up some control and autonomy in exchange for a cohesive data management strategy. Companies that effectively implement self-service analytics, however, usually find those trade-offs are outweighed by the competitive advantages gained for the organization as a whole.

For Sunny Delight Beverages, a Cincinnati-based producer of juice-based drinks, the payoff from using selfservice analytics software has been significant. The company, which generates more than $550 million in annual revenue through sales of its SunnyD, Fruit20, and VeryFine brands, estimates that its newly implemented, self-service analytics program has resulted in a $195,000 annual reduction in staffing costs and a $2 million annual increase in profits.

Getting to these results, however, has not been easy for Sunny Delight. Like many companies, it had developed a patchwork of departmental business analytics applications over the years. Sunny Delights infrastructure was particularly complex as the company has been bought and sold multiple times since it was founded in 1963. At one point, Sunny Delights 480 employees were working with eight different legacy BI applications, resulting in some departments spending up to a week each month producing data that was often not in agreement with the data generated by other departments. Reconciling and rolling up the data was time consuming and left little time for in-depth analysis, much less strategy development and execution.

The data silos also meant that Sunny Delight had no real visibility into its business, which lead to revenue unpredictably, higher-than-necessary inventory levels, and lower margins. The companys sales efforts were hampered because the sales team did not have a true understanding of the effectiveness and profitability of specific sales promotions. For example, the sales department was unable to correlate the impact of a promotional discount with order volumea key metric for judging the effectiveness of a promotional program. The company was also unable to tie shipping costs directly to specific promotions, which was significant since the timing of many promotions required shipping products to stores on weekends, when shipping and warehouse labor costs were higher.

When the company made the decision to revamp its analytics efforts, the companys CIO and CFO pulled together a cross-functional team of managers from sales, marketing, logistics, warehousing, and accounting who were responsible for developing a comprehensive picture of the required BI functionalitywhich ranged from simple, canned reports to complex, ad hoc data analysis tools. Working to understand each departments needs built credibility for the project team and helped them choose the solution that would be most effective across the company, which they did after evaluating 17 different options.

The team selected Birst, a cloud-based, self-service BI solution that offers an end to data silos with what it refers to as local execution with global governance. Because the project team understood that a centrally managed data source was critical to ensuring consistent user-generated data and analysis across the company, they also opted to implement a data warehouse at the same time Birst was rolled out to employees.

According to John Gordos, Sunny Delights associate director of application development, Birst provides Sunny Delight with a single, networked source of data, which employees at all levels can access quickly and easily, regardless of where they work. Birsts data governance features mean that Sunny Delights IS team maintains final control over all data, while the user-friendly interface, which is the same whether users are accessing data on a PC, laptop, or smartphone, makes it easy for nontechnical users to access and customize the systems departmental dashboards.

With the data from the new system, Sunny Delight was able to create a more efficient production schedule that allowed it to cut back on production, decrease inventory levels, and reduce plant overtime costs by 90 percentall without impacting order fulfillment. And with a clearer picture of overall costs, the sales and distribution teams worked together to revise shipping schedules, resulting in a 7 percent drop in the transportation costs tied to promotions.

According to Gordos, Birst helps [Sunny Delight] employees to think fast because they no longer have to worry about building and aggregating the data. They just get the data, and then they think about itinstead of accumulating it.

Critical Thinking Questions:

Is it surprising to you that a relatively small company like Sunny Delight could end up with so many different analytics tools? How might the fact that Sunny Delight has changed ownership multiple times have impacted the number and variety of BI tools being used?

What are some of the trade-offs of a move to an enterprise-level analytics solution for individual end users who might have grown accustomed to working with their own customized solutions for generating data?

According to a recent report by Gartner, most business users will have access to some sort of selfservice BI tool within the next few years; however, Gartner estimates that less than 10 percent of companies will have sufficient data governance practices in place to prevent data inconsistencies across the organization. Why do you think so many companies continue to invest in new analytics tools without implementing governance programs that ensure data consistency?