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Business Intelligence and Data Mining Session 14 Section 4 Instructor: Michael Sutton, PhD, CMC, AdmA, MIT EMBA 512 Fall 2015 Enterprise Performance Management (EPM) BVP of BI Presentation Layers for EPM (Key Characteristics and Examples) Agenda Dashboards Visual Analysis Tools Scorecards Reports Data Mining & Business Intelligence Conceptual Model & Life Cycle Classification Prediction Clustering Mining Association Rules Boise State Executive MBA Program Fall 2015 Data Mining Life Cycle SEMMA CRISP How Can Business Intelligence Drive Profits? Best Practices: BI Project Management Life Cycle: Best Practices: Integrate KPIs Future Opportunities for BI, DW and DMg 2 http://pelotongroup.com/what-we-do/enterprise-performance-management/ Enterprise Performance Management (EPM) [1] Boise State Executive MBA Program Fall 2015 3 Enterprise Performance Management (EPM) [2] EPM builds upon BI by leveraging information after implementing a BI framework—organizations move to automate processes that leverage this information. An EPM system integrates and analyzes data from many sources, including, but not limited to, e-commerce systems, front-office and back-office applications, data warehouses and external data sources. Advanced EPM systems can support many performance methodologies such as: dashboards visual analytical tools scorecards reports. Boise State Executive MBA Program Fall 2015 http://www.gartner.com/it-glossary/epm-enterprise-performance-management Enterprise Performance Management (EPM) is the process of monitoring performance across the enterprise with the goal of improving business performance. 4 BVP of BI Presentation Layers for EPM nothing but performance indicators behind GUIs (Graphical User Interface). effectiveness is due to a careful selection of the relevant measures, while highlighting data quality standards. not the primary goal of a BI/DW System. viewed as a sophisticated and effective add-ons to a BI/DW System. Primary goal of a BI/DW System should always be to properly define a process to transform data into actionable information (knowledge “between the ears” of the executives and managers). Boise State Executive MBA Program Fall 2015 Golfarelli, Rizzi, and Cella (2004), Beyond Data Warehousing: What’s Next in Business Intelligence? Presentation Layers: 5 https://www.geckoboard.com/blog/building-great-dashboards-6golden-rules-to-successful-dashboard-design/#.VjmIlrerRAk NOT a BI/BA Dashboard! Boise State Executive MBA Program Fall 2015 6 All the visualizations fit on a single computer screen — scrolling to see more violates the definition of a dashboard. 2. Displays the most important (key) performance indicators / performance measures to be monitored. 3. Interactivity such as filtering and drill-down can be used; however, those types of actions should not be required to see which performance indicators are under performing. 4. Not designed exclusively for executives, but rather should be used by the general workforce staff and managers, thus easy to understand and use. 5. Displayed data automatically updates without any assistance from the user. The frequency of the update will vary by organization and by purpose. The most effective dashboards have data updated at least on a daily basis. 6. Operational dashboards: (http://www.klipfolio.com/resources/dashboard-examples) manage intra-daily business processes – frequently changing and current performance metrics or key performance indicators 7. Analytical dashboards (http://www.klipfolio.com/resources/dashboard-examples) focus on gaining insights from a volume of data collected over time – often the past month or quarter – and use this to understand what happened, why, and what changes should be made in the future. Boise State Executive MBA Program Fall 2015 http://www.dashboardinsight.com/articles/digitaldashboards/fundamentals/what-is-a-dashboard.aspx#sthash.wry24hel.dpuf Key Characteristics of EPM Dashboards 1. 7 http://www.dashboardinsight.com/CMS/d7e568f7-ac5b-4a809150-54c017548473/business-dashboard-example.png Example of EPM Dashboards Boise State Executive MBA Program Fall 2015 8 Offer the ability to select various date ranges, pick different products, or drill down to more detailed data. 2. Fits on one screen, but there may be scroll bars for tables with too many rows or charts with too many data points. 3. Highly interactive and usually provides functionality like filtering and drill downs. 4. Primarily used to find correlations, trends, outliers (anomalies), patterns, and business conditions in data. 5. Generally historical data. However, there are some cases where real-time data is analyzed. 6. Identifies key performance indicators for use in dashboards. 7. Typically relied on by technically savvy users like data analysts and researchers. Boise State Executive MBA Program Fall 2015 See more at: http://www.dashboardinsight.com/articles/digitaldashboards/fundamentals/what-is-a-dashboard.aspx#sthash.wry24hel.dpuf Key Characteristics of EPM Visual Analysis Tools 1. 9 http://www.dashboardinsight.com/CMS/d7e568f7-ac5b-4a80-915054c017548473/public-technology-performance-dashboard.png Example of EPM Visual Analysis Tools Boise State Executive MBA Program Fall 2015 10 Scorecards and dashboards are often used interchangeably, but can be differentiated. 2. Tabular visualization of measures and their respective targets with visual indicators to see how each measure is performing against their targets at a glance. 3. Contains at least a measure, its value, its target, and a visual indication of the status (e.g. a circular traffic light that is green for good, yellow for warning, and red for bad) on each row. 4. Scorecard should not be interactive nor contain scroll bars. 5. It may contain columns that show trends in sparklines. Boise State Executive MBA Program Fall 2015 See more at: http://www.dashboardinsight.com/articles/digitaldashboards/fundamentals/what-is-a-dashboard.aspx#sthash.wry24hel.dpuf Key Characteristics of EPM Scorecards 1. 11 http://www.dashboardinsight.com/CMS/d7e568f7-ac5b-4a809150-54c017548473/dashboard-design-scorecard-example.png Example of EPM Scorecards Boise State Executive MBA Program Fall 2015 12 Reports contain detailed data in a tabular format and typically display numbers and text only, but they can use visualizations to highlight key data. 2. Presents numbers and text in a table. 3. May contain visualizations but only used to highlight findings in the data. 4. Optimized for printing and exporting to a digital document format such as Word or PDF. 5. Geared towards people who prefer to read data, for example, lawyers, who would rather read text over interpreting visualizations, and accountants, who are comfortable working with raw numbers. Boise State Executive MBA Program Fall 2015 See more at: http://www.dashboardinsight.com/articles/digitaldashboards/fundamentals/what-is-a-dashboard.aspx#sthash.wry24hel.dpuf Key Characteristics of EPM Reports 1. 13 http://www.mrexcel.com/forum/powerbi/788960-profit-loss-powerpivot.html Example of EPM Reports Boise State Executive MBA Program Fall 2015 14 Exercise # 4: 20 min. EPM Presentation Layers [1] 12 Standard Screen Patterns http://designingwebinterfaces .com/designing-webinterfaces-12-screen-patterns 30 Essential Controls http://designingwebinterfaces .com/essential_controls 15 Common Component Patterns https://www.pinterest.com/pin/69524387974322158/ Designing Web Interfaces Principles and Patterns for Rich Interaction by Bill Scott & Theresa Neil http://designingwebinterfaces .com/15-common-components Boise State Executive MBA Program Fall 2015 15 Break into Groups of 4-6 Move to Breakout Rooms Exercise # 4: 20 min. EPM Presentation Layers [2] Business Problem: Each Team is provided with 4 different examples of BI Presentation Layers Using what you have just learned about Dashboards, Visual Analysis Tools, Scorecards, and Reports Critique each presentation layer Suggest improvements Identify 1 tool that can be used to create each Presentation Layer. Report out Teams: Select a spokesperson Present and defend results. Boise State Executive MBA Program Fall 2015 16 Data mining is “non-trivial extraction of implicit, previously unknown, and potentially useful information from data in databases” [including DWs]. —Frawley, Piatetsky-Shapiro, Matheus (1991), Knowledge Discovery in Databases: An Overview http://www.sas.com/en_us/insights/analytics/datamining.html?gclid=CIer3O_q9MgCFVJlfgodxa0ORA Data Mining— Advanced Business Analytics Alternative synonyms: Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, data forensics. Boise State Executive MBA Program Fall 2015 17 http://www.slideshare.net/salahecom/introduction-to-data-mining-tutorial Data Mining and Business Intelligence [1] Boise State Executive MBA Program Fall 2015 18 http://www.saedsayad.com/data_mining_map.htm Data Mining: Conceptual Model & Life Cycle [Part 1] Boise State Executive MBA Program Fall 2015 19 http://www.saedsayad.com/data_mining_map.htm Data Mining: Conceptual Model & Life Cycle [Part 2] Boise State Executive MBA Program Fall 2015 20 Classification [1] Classifies data by constructing a model, based upon a training set and the values within a classifying attribute, using the value to classify new data Terrorist profiling Target marking Credit Approval Medical diagnosis Fraud detection Boise State Executive MBA Program Fall 2015 Han and Kamber, (2006). Data Mining: Concepts and Techniques Predefined set of groups or models based on predicted values. 21 http://www.slideshare.net/salahecom/08-classbasic Classification [2] Boise State Executive MBA Program Fall 2015 22 Relates to time series; but not time bound. Prediction Used to predict value based on past data and current data. Water flow of a river will be calculated by various monitors at different levels and different time intervals. Information use to predict the future water flow. Boise State Executive MBA Program Fall 2015 https://www.siggraph.org/education/materials/H Han and Kamber, (2006). Data Mining: Concepts and Techniques yperVis/applicat/data_mining/data_mining.html Similar to Classification 23 similar (or related) to one another within the same group dissimilar (or unrelated) to the objects in other groups Clustering (cluster analysis or, data segmentation, …) Clustering [1] Finding similarities between data according to the characteristics found in the data and grouping similar data objects into clusters Similar to classification, except clustering won’t rely upon any predefined groups. Instead the data itself defines the group. City-planning: Identifying groups of houses according to their house type, value, and geographical location Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults Land use: Identification of areas of similar land use in an earth observation database Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs Boise State Executive MBA Program Fall 2015 Han and Kamber, (2006). Data Mining: Concepts and Techniques Cluster: a collection of data objects 24 http://userwww.sfsu.edu/art511_h/ acmaster/Project1/project1.html Clustering [2] http://today.slac.stanford.edu/feature/2009/data-mining.asp Boise State Executive MBA Program Fall 2015 http://datamining.xmu.edu.cn/main/~cloud/cbddm.html 25 Amazon “People bought this also bought this” model Boise State Executive MBA Program Fall 2015 Han and Kamber, (2006). Data Mining: Concepts and Techniques Mining Association Rules Uncovering relationship among data. 26 http://timkienthuc.blogspot.com/2012_04_01_archive.html Data Mining Life Cycle – SEMMA [1] // SAS // Boise State Executive MBA Program Fall 2015 27 http://timkienthuc.blogspot.com/2012_04_01_archive.html Data Mining Life Cycle – SEMMA [2] // SAS // Boise State Executive MBA Program Fall 2015 28 https://the-modeling-agency.com/crisp-dm.pdf Data Mining Life Cycle CRISP Boise State Executive MBA Program Fall 2015 29 https://the-modeling-agency.com/crisp-dm.pdf Data Mining Life Cycle – CRISP Details Boise State Executive MBA Program Fall 2015 30 Best Practices: BI Project Management Life Cycle: NOT THIS! Boise State Executive MBA Program Fall 2015 31 Foundation | SP (2013), What considerations should you have when starting a BI project? Best Practices: BI Project Management Life Cycle: NOT THIS EITHER! Boise State Executive MBA Program Fall 2015 32 Quartech Systems Ltd. (2013), Business Intelligence... performance from information: Focus your Organization Best Practices: BI Project Management Life Cycle: MORE LIKE THIS! Boise State Executive MBA Program Fall 2015 33 Business Value Proposition of Business Intelligence/ Data Mining Including Important Best Practices Boise State Executive MBA Program Fall 2015 34 http://www.youtube.com/watch?v=THLpOPYj-YY Complexity reduction Companies on the leading edge of BI are spending 45% less on business analysis and enjoy 2.4 X on their ROE Numerous information silos are being consolidated into a central DW with Data Marts Rensselaer Polytechnic Institute selected Oracle and Hyperion and noted a payback of 2.5 years, saving them $1.2M. Increase competitive advantage Now relying upon a single DW providing more timely and accurate information sethcurry.ga How Can Business Intelligence Drive Profits [1] The Hackett Group studied 200 large companies in 2007 Enterprise-wide model construction and minimal number of BI tools, not desperate siloes and 5-10 tools Boise State Executive MBA Program Fall 2015 35 Redefining the role that information plays in the organization. Changing the way that information requirements are defined. Changing behaviors in using information. Increase Accessibility Focus on Accountability Enhance Accuracy Timeliness of Information Improve Decision-Making Boost Communication Boise State Executive MBA Program Fall 2015 http://dionhinchcliffe.com/2015/01/16/how-leaders-canaddress-the-challenges-of-digital-transformation/ How Can Business Intelligence Drive Profits [2] AAATIC (“attic” thread) Through transformation of the enterprise culture by: 36 business strategy :::: BI strategy business infrastructure :::: BI infrastructure business processes :::: BI processes How Can Business Intelligence Drive Profits [3] Establish a BI Competency Center Address new marketplace opportunities: 1. Extending current opportunities. How can firm’s extend opportunities that are the focus of its current strategy? 2. Potential new marketplace opportunities. What opportunities beyond the reach of the current strategy should the firm be considering? What opportunities may be lurking but not yet fully evident in marketplace change? Boise State Executive MBA Program Fall 2015 https://whittblog.wordpress.com/2011/04/24/mckinsey-7smodel-a-strategic-assessment-and-alignment-model/ Establish strategic alignment between 37 Questioning Competitors How Can Business Intelligence Drive Profits [4] How might competitors most adversely affect the firm’s current strategy? Which competitors are most likely to do so? How might the firm best ‘‘handle’’ these threats? Questioning Risks What competitive risks does the firm’s current strategy face? What competitive risks might the firm face in the future? How can the firm best manage these risks? Questioning the Marketplace Assumptions What assumptions about marketplace change underpin the firm’s current strategy? What assumptions should the firm make about emerging and potential marketplace changes? If the firm needs to change its assumptions, what are the implications for the firm’s strategy change? Boise State Executive MBA Program Fall 2015 http://leadingstrategicinitiatives.com/2012/04/26/how-to-identify-strategic-assumptions/ When successful, BI can drive Competitive Intelligence (CI): 38 BI and DMg Critically Support Significant Enterprise Questions Potential Value of BI what happened ? reporting and dashboards trigger corrective actions, if unfavorable variances are observed from expectations why did something happen? ad hoc queries, user-selected queries, and OLAP are the foundation for o deeper analysis o creating customer segments for behavioral analysis o clustering products that often sell together for marketing purposes o decomposing enterprise performance information by business unit, product, customer, or other relevant dimension why is our enterprise performance effective/ ineffective? performance management analytics are intended to enable strategic, tactical, and operational control over performance and to enable more cost-effective planning what critical issues/events are happening right now? real-time analytics are intended for keeping a tighter rein on minute-by-minute performance of some key business process monitoring sales or customer service to enhance such processes, such as by using BI to suggest book titles to a potential customer who has been looking at other titles what is likely to happen and the likely economic results? predictive analytics are sophisticated statistical and analytical techniques used for forecasting Boise State Executive MBA Program Fall 2015 http://leadingstrategicinitiatives.com/2012/11/23/strategicinitiatives-what-are-the-metrics-that-matter/ Questions 39 Profit margin per transaction Margin by customer Customer average days to pay Boise State Executive MBA Program Fall 2015 Returns count, quantity, and value Contribution to profit by product Late benefits enrollment IBM (2005), Business Performance Management Meets Business Intelligence Best Practices: Integrate KPIs Expiring purchasing contracts 40 http://datamining.typepad.com/data_mining/2008/12/twitter-venn-diagrams.html Future Opportunities for BI, DW and DMg [1] Boise State Executive MBA Program Fall 2015 41 http://www.propublica.org/article/world-of-spycraftintelligence-agencies-spied-in-online-games http://www.entropiaplanets.com/wiki/File:Journal_of_Virtual_Wo rlds_Research__Payback_of_Mining_Activities_Within_Entropia_Universe.pdf Future Opportunities for BI, DW and DMg [2] http://www.cryptocoinsinsider.com /mining-cryptocurrency/ Boise State Executive MBA Program Fall 2015 42 http://www.worldclassminers.com.au/news/safety/ns w-miners-build-world-class-virtual-reality-train/ http://livinglabs.mit.edu/index.php?option=com_con tent&view=article&id=90:original-reality-miningstudy&catid=53:responsive-technology&Itemid=113 Future Opportunities for BI, DW and DMg [3] http://ostic.wp.tem-tsp.eu/2014/06/17/realitymining-the-technology-which-will-change-our-lives/ Boise State Executive MBA Program Fall 2015 http://news.mit.edu/2013/how-hardit-de-anonymize-cellphone-data 43 http://yellowhammernews.com/nationalpolitics/4th-amend-protectus-warrantless-cell-phone-data-mining-al-06-candidates-sound/ Future Opportunities for BI, DW and DMg [4] Boise State Executive MBA Program Fall 2015 44 http://www.businessinsider.com/picturesof-the-nsas-utah-data-center-2013-6 Future Opportunities for BI, DW and DMg [5] --BIG Challenges http://blogs.reuters.com/great-debate/2014/01/02/willsnowdens-disclosures-finally-rein-in-the-nsa/ Boise State Executive MBA Program Fall 2015 45 http://www.rippdemup.com/justice/nsa-data-mining-prism-why-myblack-ass-isnt-afraid/ Future Opportunities for BI, DW and DMg [6] Boise State Executive MBA Program Fall 2015 46 http://timoelliott.com/blog/more-analytics-cartoons Future Opportunities for BI, DW and DMg [7] Boise State Executive MBA Program Fall 2015 47 Section 4 Recap Boise State Executive MBA Program Fall 2015 48