Chapter 1 Exercises Review
Go to the TUN questions site. Look for BSI videos. Review the video of the "Case of Retail Tweeters." Prepare a onepage summary of the problem, proposed solution, and the reported results. You can also find associated slides on slideshare.net.
A fashion company called Brizio Fashions ran by the CEO Giorgio has had a problem with analyzing the market's trend. Brizio Fashion's competitors have been taking advantage of the use of social media to market their company, however, CEO Giorgio strongly believes that social media advertising is a waste of the companies' money and is just a trend. Martina, Chief Marketing officer has proposed a solution that Brizio Fashion should implement social media marketing strategies to increase revenue through ads and a new way to show case new products. CEO Giorgio agrees to this proposed solution by Chief Executive Martina and gave her 150k to experiment this marketing strategy. Martina then turns to BSI for help with this project. Martina states that in Fashion, product cycles go fast. In other words, trends come and go. Martina talks about "Hot buzz" or also known as viral marketing in which you determine what or who are the trend setters and influencers. The BSI team analyzes other competitors by determining what they have and what Brizio does not. They found that their key competitors have 5 Facebook pages and 2 twitter feeds. BSI team also uses sentiment analytics to hand pick Brizio Fashion's products that either are selling or not selling and had them change the prizes day by day. For example, Brizio's had an amazing success in their handbag which then increased the price and demand, however, their cosmetics lines did not sell great, so they had to split the price and discontinue the product. BSI then looks at social media to analyze and look for customers who have blogged and tweeted a lot, forwarded their emails, and most importantly has a large follower base. BSI approximately found 125,000 customers that tweet about the Brizio products, however, only 4,200 of them are seen as the influencers. Those 4,200 influencers make a huge difference in social media aspect of this case because when one of those influencers purchases a product of the company, 8 people in their network do the same through forwarded emails and retweets which cause a ripple effect. To increase more sales BSI offered a free perfume sample to the influencers of Brizio Fashion in which 120 out of the 4,200 influencers took advantage of the free product and then marketed in their social media which then averaged about 8-10 purchases from the influencer's followers. In conclusion, the price adjusting of the company's top of the line hand bag cosmetics generated about $72,000 in revenues in the second week. Also, by discontinuing saved Brizio about $27,000. In total the whole case made a financial impact of $200,000.
"Special Issue: The Future of Healthcare." Read the article "Predictive Analytics—Saving Lives and Lowering Medical Bills." Answer the following questions: What problem is being addressed by applying predictive analytics? What is the FICO Medication Adherence Score? How is a prediction model trained to predict the FICO Medication Adherence Score HoH? Did the prediction model classify the FICO Medication Adherence Score? Zoom in on Figure 4, and explain what kind of technique is applied on the generated results. List some of the actionable decisions that were based on the prediction results.
A. what problem is being addressed by applying predictive analytics? Existing strategies against non-adherence is too little and too late. When doctor figured out negative health consequences of patients, the retrospective approach does not work. It is important to predict patient before patient experience negative health outcomes. B. What is the FICO Medication Adherence Score? It is one of examples of analytic approach and based on models developed from publicly available data like age, gender, marital status. By combining these data, they can build score with range of 1-500 to indicate the probability of patients adhering to prescription for first year of therapy. C. How is a prediction model trained to predict the FICO Medication Adherence Score HoH? Did the prediction model classify the FICO Medication Adherence Score? The score is using to identify patients by pharmacy-benefits manager. The manager uses the five data sets, so analytics scientists are able to track the pattern of patients who filled and refilled prescriptions and those who didn't. The patients who record top decile on the scale actually stuck to their prescriptions for average of 129 more days. D. Zoom in on figure 4, and explain what kind of technique is applied on the generated results. By graphing the average days of adherence over first year by score, the author uses data of recording millions of patients for prediction analytics to point out how the score is related to the problem and forecast how patients of the certain amount of score will act based on the data. E. List some of the actionable decision that were based on the prediction results. Analytics scientists and doctors can develop strategy and tactic for patients who got risk score. One of example is that sending reminders through e-mail and text, simplifying drug regimens to avoid confusion, offering information of drug programs and other financial resources that can help patients to cover the cost of prescription, providing instruction in patient's native language and having some extra care such as sending nurse to patient's house to ensure that medications are taken properly.
Go to the TUN site. Explore the Sports Analytics page, and summarize at least two applications of analytics in any sport of your choice
Basketball analytics: Using the TUN site I found an article called databall explaining how data analytics is helping to improve basketball players. Data analytics used in this scenario is EPV shortened for "expected progression value". EPV is to predict what will happen next depending on the circumstances of the game; it uses stats such as the players, past strategies, and formations. Gaining all of this new data doesn't mean Basketball will change completely until they fully understand how to use the new data. Currently, it will give more insight into the flaws of players and strategies. Football analytics: Just like in basketball, sports analytics is improving football games. The NFL uses all of this new facts to know exactly all the data involved in a play. Such as distance a player runs, the speed they run at, the speed of the ball in the air. The NFL is not only focusing on improving the players but to help the viewers have a better understanding of what is happening in each game. The NFL is committing to obtaining more data. They use a production truck outside of each game to collect the data and create stats so the broadcaster can use to emphasize the level of play. This ends up being a lot of data of each player so most sports use it to decide when to substitute people and who is better for each scenario. For each sport, data analytics is improving the game whether it is by improving player or giving audience members hard stats. All of the applications used are to enable the sport to become greater.
Review the Analytics Ecosystem section. Identify at least two additional companies in at least five of the industry clusters noted in the discussion
Data Generation Infrastructure Providers Dominion energy collects data on energy consumption to help them adjust price levels and maintain efficiency. They use radiation sensors to ensure that their employees are safe on nuclear powerplant sites and to learn if there has been a breach in safety. Another company that collects data on its users is Facebook. It is no secret that Facebook collects data on its users to target them for advertisements. Recently Facebook has come under fire for allegations towards the use of that data, that is, being sold to other companies for competitive business leverage. Data Management Infrastructure Providers One company that provides a tangible intrastate for data management that is not mentioned in the text would be SPARC. They provide physical servers for the basis of data storage and communication through the cloud. Another growing company that provides less physical storage but more open source solutions to mass storage would be PLEX. This is an at home solution for storage vast amounts of data while making it accessible remotely for personal use. Middleware Providers DataSelf Corp. DataSelf Analytics is an enterprise-level analytics platform designed for every business intelligence user at mid-sized companies from the CEO down. It's self-service business intelligence at an SMB price. Kronos Incorporated. Cloud-based Kronos human capital management and workforce management solutions help organizations of all sizes and industries attract, retain, and engage employees while improving efficiency and customer satisfaction. Data Service Providers DuPont Fabros Technology, Inc. was a real estate investment trust that invested in carrier-neutral data centers and provided colocation and peering services. They help create market and materialize physical warehouses big data storage and transfer. CenturyLink, Inc. is an American telecommunications company, headquartered in Monroe, Louisiana, that provides communications and data services to residential, business, governmental, and wholesale customers in 37 states. They are the 3rd largest telecommunications provider in the U.S. Analytics-Focused Software Developers Sisence is a business analytics software company that lets one create and manage complex data models from multiple sources in a simple drag and drop environment anyone can understand. It also enables data to come to life in interactive web dashboards with many visual options making it a great reporting tool. Domo solves brings business together a platform that makes it easy to see the information that you want it easily and makes the information readily available to present in many different fashions using an arsenal or premade templates and designs making it a great prescriptive analytic tool.
Go to microstrategy.com. Find information on the five styles of BI. Prepare a summary table for each style.
Enterprise Reporting This style is typically used when an enterprise wishes to give financial reports to its stakeholders in the organization. This is the most widespread style of BI. These reports are deployed in a pixel-perfect report format Cube Analysis This style is ideal for basic analysis which can be predicted in advance. This style is formed in an Online Analytical Processing (OLAP) slice-and-dice analysis. It is targeted at managers and other people who require a simple environment for data exploration within a restricted range of data. Ad Hoc Query and Analysis The Ad Hoc Query style allows full investigation of analysis of enterprise data. Because a buyer cannot determine what is going on based on pre-defined comparisons in an analysis cube alone, he needs the Ad Hoc Query to examine more areas of the database to determine what is going on. Typically targeted at information explorers. Statistical Analysis and Data Mining This style is used to reveal subtle relationships (e.g. price elasticity) and predict projections like sale trends. Statistical methods and advanced math functions are used to do this. Alerting and Report Delivery Alerting and Report Delivery enables enterprises to give out a vast number of reports on an active and centralized basis. It also lets users self-subscribe to report distributions. The report distributions can be initiated on a scheduled basis or an event-triggered basis. This style is targeted to very large user populations.
Go to oracle.com, and click the Hyperion link under Applications. Determine what the company's major products are. Relate these to the support technologies cited in this chapter.
Hyperion is a division under Oracle that focuses in Business Intelligence products. Acquired in 2007, Hyperion's product line was merged with Oracle to create Oracle Business Intelligence suite. This application allows its users to work with big data via visual analytics, operational analysis, predictive analysis, reporting, dashboards and various other tools. It contains the 4 major components of a BI system according to the text: a data warehouse, business analytics, a collection of tools for manipulating and analyzing the data, and a dashboard. As a module of the Business Intelligence suite, Hyperion also offers Hyperion Interactive Reporting. This reporting tool allows users to combine data from multiple sources and create charts, pivot tables, and reports. Its dashboard technology utilizes descriptive analysis to give its users a clear and visual representation of the data. Oracle Essbase is another product offering from Hyperion. Essbase is an industry leading online analytical processing (OLAP) server. It is designed to help its users forecast business scenarios and complete "what-if" analysis with various conditions. Essbase other functions include forecasting, variation analysis, and reporting. Aside from Hyperion specific products, Oracle offers a wide range of software solutions catered to big data and data analytics. Autonomous Data Warehouse is a solution offered by Oracle which delivers a fully managed cloud data warehouse. This product solution automates various management operations that are required of data warehouses. Oracle Big Data SQL is another service provided by Oracle. It allows users to query data across various sources and databases such as Hadoop, Oracle Database, and NoSQL.
Explore the public areas of dssresources.com. Prepare a list of its major available resources. You might want to refer to this site as you work through the book.
List of major resources for DSS resources Framework Data-Driven A type of DSS that focuses access and modification of internal and external data Sites for Information on Data Driven Examples of Data Driven Data Warehouse Online Analytical Processing Software Executive Information Systems Geographic Information System or Spatial DSS Spreadsheet-based Model Driven and Data Driven built with using spreadsheet Resources Articles Instruction Materials General Sites and Product Sites Development Tips for Spreadsheet Web-Based A DSS that delivers decision support information or decision support tools to business analyst using a web browser accessing the internet or intranet. Examples of Web based DSS Microsoft Carpoint TCBWorks (discussions and decision making) PCS Health Systems (Prescription management System) Model-Driven A type of DSS that focuses on the access and modification of a statistical, financial, optimization and/or simulation model Example of Model Driven DSS Computationally Oriented DSS Model Driven DSS can be used for breakeven analysis, Budget Financial models, Pro formas , and Many more Knowledge-Driven A DSS that can suggest or recommend actions to managers. These DSS are built to be problem solving experts in specific domain Example of Knowledge Driven DSS TAXAdvisor (assist attorney with tax and estate planning for client with large estates.) XCON (help configure computer systems-based customer orders) Tomakomai Paper Mill (Customer Support System at Compaq Computer) Insurance Plan Selection System for Meiji mutual Life Insurance Document-Driven A type of DSS focuses on the recovery and management of unstructured oral, written, and video documents Examples of Information Retrieval systems Lexis-Nexis, Infosys, and Uncover Web Search engines such as WebCrawler, Alta Visita, and Lycos Sites for Information on Document- driven Communications-Driven A type of DSS that focuses on communication, collaboration and shared decision-making support for a company Characteristics Enables communication between groups Enables the sharing of information in groups Supports collaboration Supports group decision tasks Key Terms Group decision supports situation Interactive Videos Channels (provide information for specific groups who are looking for information of decision support systems) Consultants Decision Support Readiness Audit Questionnaire Press Releases Archive DSS glossary Interviews Developers Guide for developing Web and spreadsheet HTML/Programming Links JavaScript Decision Aids Tools Manager Ask Dan!(DSS q&a answered by Dr Dan Power Case Studies DSS Book Store DSS Hyperbook Professors Resources for Decision support /MIS course for MBA and Undergrads Resources for DSS Course Resources for Decision Support Technologies Course Researchers DSS Books DSS Questionnaires DSS Journals/Newsgroups DSS Professional Associations DSS Research Centers DSS Web Page Directory DSS Research Topics Students Case studies Course List and Materials DSS Book Reference List DSS Hyperbook Review Questions Lecture Slides Tutorials Library Articles DSS Q&A Case Studies Glossary Hyperbook Interview Newsletter Reflections Other Vendor List Consulting/Training
The discussion for the analytics ecosystem also included several typical job titles for graduates of analytics and data science programs. Research Web sites such datasciencecentral.com and tdwi.org to locate at least three additional similar job titles that you may find interesting for your career.
Network technician - A lot of network technicians earn their CompTIA Network + certification for industry recognition. They primarily build and troubleshoot network issues as well as focus on setup, repair, and troubleshooting of both hardware and software products incorporated in business operations. They lie on the outer six petals of analytics ecosystem, allowing organizations to employ technologies in the most effective and efficient manner. Amazon Solutions Developer - classified as an industry specific application developer. In this case Amazon has developed its own certification to ensure its developers have the industry knowledge, analytical expertise, and solutions available to work with the Amazon software. This cert is called Amazon Web Services (AWS) certification. Salesforce Developer - Personally, I myself have worked with Salesforce through one of my internships. This specific position requires being involved in agile development projects focusing on enhancing the current Salesforce platform. In addition it requires some administration and high level support, which I believe makes this position an analytics accelerator in the analytics ecosystem.
"Work Social." Read the article "Big Data, Analytics and Elections," and answer the following questions: What kinds of Big Data were analyzed in the article Coo? Comment on some of the sources of Big Data. Explain the term integrated system. What is the other technical term that suits an integrated system? What kinds of data analysis techniques are employed in the project? Comment on some initiatives that resulted from data analysis. What are the different prediction problems answered by the models? List some of the actionable decisions taken that were based on the prediction results. Identify two applications of Big Data analytics that are not listed in the article.
What kinds of Big Data were analyzed in the article Coo? Comment on some of the sources of Big Data. State economic, Social media, demographic, voter sentiment, behavioral analysis and political data Explain the term Integrated systems. What is the other technical term that suits an integrated system? True definition: a system that has combined different functions together in order to work as one entity. Relation to the article: " The advantage of the integrated system is that analytics could be performed effectively across multiple datasets from multiple channels- the ability to connect the digital dots." Integrated systems evolved the way data was once executed; data could now be used and shared simultaneously. Another term that suits integrated data is Data Integration Process; "They first went through a data integration process to consolidate many disparate databases and create a single, massive system that merged information collected from pollsters, fundraisers, field workers, and customer databases as well as social-media and mobile contacts with the Democratic voter files in the swing states. " What kinds of data analysis techniques are employed in the project? Comment on some initiatives that resulted from data analysis. Majority of the techniques were quantitative which means they had little room for variation in the ways data was collected. Polls were taken, advisers ran experimental campaigns, data from previous elections was evaluated, and after collect the data models were created. The Obama campaigns used combined analytic techniques that were not used in the past and it lead to triumph. What are the different prediction problems answered by the models? How many states Obama would win? Which states would Obama lose? The grumpy voter effect Voter sentiment Who to target and how to target? How to approach tailored messaging? List some of the actionable decisions taken that were based on the prediction results. Fundraising: text messaging, e-mail, George Clooney and Sarah Jessica Parker Ads: Ad-buying and Ad-timing (where and when to run ads) Gaining the swing state votes Identify two applications of Big Data analytics that are not listed in the article. They used demographic data but what if in addition to that they assessed the correlation between Demographic data and geographic data. That information could be used to target a specific areas beliefs and values during Rallies. Employment status data could also help in targeting potential voters.
Search the Internet for material regarding the work of managers and the role analytics plays. What kinds of references to consulting firms, academic departments, and programs do you find? What major areas are represented? Select five sites that cover one area, and report your findings.
When searching for "analytics in management", many of the first links that come up are those regarding numerous institutions that offer "Masters in Analytics" programs and continually refer to it as a rapidly growing field necessary in today's workforce. MIT Sloan School's website refers to the field as one that will provide essential insight for executives and managers in order to better facilitate their process and more importantly, their decisions . CIO magazine reports that the ever-growing field of Big Data and Analytics is helping companies not only focus on understanding the customer but is ultimately assisting companies in attracting and retaining talent that will effectively ensure continued success. The modern day job force is quite different from that in the past; record number of employees voluntarily quit and are actively seeking the next best opportunity, additionally, unemployment is at historic lows. According to Visier, nearly 78% of employers find employee retention to be important or urgent, and thus now is a time for companies to increase their usage of workforce analytics in order to attract and retain the best talent possible. SAP is a frontrunner in developing workforce analytics and has created the SuccessFactors Workforce Analytics, this uses different metrics such as salary and makes great attempts as to understanding "flight risk" and what has happened in the past and what kind of informed decisions companies can make in the future to reduce employee flight.; it refers to this as predictive power. Deloitte, a management consulting firm, has additionally worked on implementing analytics in order to reduce employee turnover for one of its clients. One of the main metrics it used was a "retention score" based on a data model regarding employee motivation to stay with the company. This retention score helped sales managers determine which employees were at risk of leaving, and what the company could do in order to appease them.
Then select the case study "Harrah's High Payoff from Customer Information." Answer the following questions about this case: What information does the data mining generate? How is this information helpful to management in decision making? (Be specific.) List the types of data that are mined. Is this a DSS or BI application? Why?
a. What information does the data mining generate? Data mining takes observations collected and generates data that could be used to implement better business strategies to (in this case) build customer loyalty, provide great customer service, and increase overall sales/revenue. In this specific situation, the collection of this customer data lead to: Doubled response rate of offers to clients Consistent customer reward programs/recognition methods across Harrah's different properties A brand identity for Harrah's casinos; An increase in customer loyalty A 72% increase in customers who play at more than one of Harrah's casinos (profitability increased by over than $50 million) A 62% IRR on the information technology investments b. How is this information helpful to management in decision making? (Be specific.) Gives management a better understanding of which business strategy or approach they should go forward with next, based on their client's preferences. Harrah Co. took on a more customer relationship management initiative to obviously push their rewards program and customer service, leading to the results listed in the previous question. c. List the types of data that are mined. Harrah Co. conducted proper and reliable observations and viewed current documents and records to understand the current customer patterns. They used these methods to be able to: Maintain/gain customer relationships Predict customer habits/patterns Introduce their new IT systems to compete with their competitors Prepare for growth d. Is this a DSS or BI application? Why? Harrah's Company IT systems and methods of data mining are considered a BI (Business Intelligence) application. The reason behind it is that they spent a solid amount of time observing, experimenting, analyzing, and planning their client's needs, habits, and preferences, which lead to several conclusions and information addressing the overall customer experience. With this information, management is able to plan and prepare for future program implementation.
