Question:
Ski Team is a ski tourism business that organises ski trips in various locations around the world andmarkets these to its existing clients. The Ski Team database contains information relating to the coretourism business. It contains details of the camps run, the athletes who attended those camps, and campaigns associated with sourcing athletes. In addition the database contains financial information related to the amount invoiced and received for each client taking part in ski trips.
The data in this database will assist managers of Ski Team to understand the profitability of each of their programs, identify who their most profitable clients are and better manage their best performing camps. Information on how to access Yellowfin and documentation to assist you with using the BI tool is provided for you in Resources in the Yellowfin User Guide folder.
Task requirements:
Using the Ski Team data provided in Yellowfin, create a dashboard, with at least 3 reports, for the managers of Ski Team that will assist in the evaluation of their sales performance between 2011 and 2016. Due to the erratic performance of the Europe Region over the past 6 years, the management is wondering what the key drivers of buyer behaviour are in regards to customer demographics and camp locations. Choose suitable KPIs for your dashboard that will inform management about the sales performance of Regions.
Provide a 1,000 word summary, in business report format, that enables management to understand the story behind the data. Within your report explain to management the impact that BI technology can have on accounting and business decisions. Please include your dashboard as part of your report summary.
Answer:
The need for governed reporting using an agile centralized BI provisioning model to run businesses remains. However, there is a significant change in how companies are satisfying their governed reporting requirements: Companies are asking if the data discovery tool can be used to fulfill the full spectrum of BI and analytic requirements. They would like to leverage the higher ease of use, higher business benefits, and lower cost of ownership to deliver enterprise reporting requirements. Here we have seen vendors who started out as point solutions for individual analysts in a decentralized use case evolve their capabilities to handle enterprise governance features with better report distribution and KPI alerting
Strengths
- Self-service data preparation: Ski Team allows power users, such as citizen data scientists, to combine data from multiple data sources while also transforming and cleansing data. Surveyed customers report using an average of nine data sources per application, putting the product in the top third of the vendors included in this research for this metric. It provides connectivity to a broad range of data sources, including JSON, XML, direct HDFS, Spark, Impala, Google Big Query, and a broad range of relational databases. For data scalability, Ski Team supports push-down processing to a number of leading databases. Ski Team is in the top quartile for fastest time to create complex reports, and customers rate the ease of use for authoring complex reports as the easiest.
Key Operating Information
- Advanced and location analytics: Ski Team embedded advanced analytics are rated outstanding overall. It supports forecasting and clustering via a menu-driven interface, along with more than 60 R-based functions, allowing these to be used either in the data preparation process or as output columns for an application. Models can also be output to PMML or R for refinement in other data science platforms. Ski Team has its origins with the U.S. Census Bureau, and supports spatial analytics using a range of maps down to street level for a number of world regions. It also supports drive time and radius geospatial calculations.
- Scheduled reports: Ski Team allows formatted reports to be distributed in a variety of formats such as PDF, PowerPoint and Excel on a scheduled basis, with email notification. While this capability is typical in traditional BI platforms, it is lacking in many of the modern BI and analytic products. These schedules can be set based on system or business events, such as low inventory.
Areas of Improvement
- Visual exploration for consumers: Ski Team rates only fair for its visual exploration capabilities. The ability to manipulate data or author content is only supported in the desktop interface, not via the browser. In this regard, information consumers mainly interact with a highly parameterized dashboard, as opposed to performing more free-form exploration. Ski Team lacks the ability to automatically display numeric values as percentages, link multiple visualizations on a page, or create groups via a point-and-click interface. Particular chart types must be specified at design time, with no support for trellis or histogram charts.
Business Operating Metrics
- No native mobile: Ski Team does not offer native mobile apps, nor specific support for mobility outside of generic, browser-based access. While there may not be high demand for mobile support for the development of data blending workflows, the Ski Team Analytics Gallery could benefit from improvements to its content-consumption experience through support for responsive design when creating content, or through the addition of native mobile apps.
Conclusion
Ski Team does not natively support dashboard layouts. Scores for this capability are still rated as Fair to Good because of its support for sub criteria related to mapping. Cloud capabilities are limited to AWS deployment for the Analytics Gallery, with lack of support for hybrid connectivity to on-premises data sources and no additional software security certifications. Within the group, share and collaborate capability, Ski Team lacks discussion threads, storytelling and the ability to rate content.
References:
- George, J., Kumar, V., & Kumar, S. (2015). Data Warehouse Design Considerations for a Healthcare Business Intelligence System. InWorld Congress on Engineering
- Kimball, R., Ross, M., Becker, B., Thornthwaite, W., & Mundy, J. (2015).The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence Remastered Collection. John Wiley & Sons
- Wixom, B., Ariyachandra, T., Douglas, D., Goul, M., Gupta, B., Iyer, L., ... & Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data.Communications of the Association for Information Systems, 34(1), 1
- Chang, V. (2014). The business intelligence as a service in the cloud.Future Generation Computer Systems, 37, 512-534.
