12209508.ppt
- 1. BI – Preliminary Information on the course Prof. Paola Velardi Dipartimento di Informatica Sapienza Università di Roma
- 2. Why this course • Enterprises today are driven by data. "Business Intelligence allows people of all levels in organizations to access, interact with, and analyze data to manage the business, improve the performance, discover opportunities, and operate efficiently" (Cindi Howson, Successful BI, McGrawHill). • However, the degree to which BI solutions can be successfully adopted within organizations depend to a great extent on the degree to which business and IT experts can partner together. • The objective of this course is to form Advanced Business Users of BI applications, with a deep understanding of the business needs and a good understanding of technology. • The Advanced Business User understands the business and how to leverage technology to improve it, leads the interpretation of business requirements and strategic objectives, and helps designing reports to answer business questions.
- 3. Organization • The course is in two parts: – PART A: Business Intelligence and Social Analytics (Instructor: Prof. Paola Velardi velardi@di.uniroma1.it) 6CFU (MON-TUE) – PART B: Process modeling (Instructor: Prof. Paolo Bottoni bottoni@di.uniroma1.it) 3CFU (WED) • Course syllabus, material and slides available on the web page: http://twiki.di.uniroma1.it/twiki/view/BI/WebHo me • Subscribe to google group!
- 4. Business Intelligence What is and what is not
- 5. Example: supply chain management • Supply chain management is the integration of the activities that procure materials and services and deliver them through a distribution system • It typically implies the following actions/actors: 1. Transportation vendors 2. Credit and cash transfers 3. Suppliers & Distributors 4. Accounts payable and receivable 5. Inventory 6. Order fulfillment
- 6. The traditional Supply Chain workflow
- 7. The traditional ICT tools for handling SCM • ON-LINE TRANSACTION PROCESSING or OPERATIONAL TRANSACTION PROCESSING) • “On line transaction processing, or OLTP, is a class of information systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing. “ (wikipedia) • Operational, or online transaction processing (OLTP), workloads are characterized by small, interactive transactions that generally require sub-second response times.
- 8. Example of a typical OLT interface Shipment number, status, carrier name, shipping state, date, source, destination, package type..
- 9. What kind of info can you get from this database? • Shipments (transactions) • Destinations • Earnings.. • Everything according to time, geographic areas, etc. (e.g., “How many shipments to Roma in July?”) • Data come from inside the company and are mostly manually entered. Data processing is with traditional database systems (SQL). Interfaces are sometimes well designed , but usually not so exciting.
- 10. CAN BUSINESS INTELLIGENCE HELP SUPPLY CHAIN MANAGEMENT?
- 11. Study Case one: Magpie Vaccine supply chain (1) • Cold chain in healthcare is the temperature-controlled supply chain in trasporting and storing vaccines and drugs • Maintaining cold chain is extremely important for these products • A study of CDC (Center for Desease Control in US) revealed that ¾ of providers experienced serious cold chain violation
- 12. Study Case one: Magpie Vaccine supply chain (2) • Magpie sensing (a start-up project) is using cold chain data analytics and business intelligence to improve vaccine supply chain management • They gather location-tagged information about cold storage units using a shippable wireless temperature and humidity monitor system • Real-time, fixed-location monitoring device then safeguards products from their arrival at a lab or clinic until the moment of use
- 13. Study Case one: Magpie Vaccine supply chain (3) • At the core of Magpie Sensing's solutions are analytics algorithms which leverage data from monitoring devices to improve cold chain processes and predict cold storage problems before they occur.
- 14. Study Case one: Magpie Vaccine supply chain (4) • Three types of analytics are provided: • Descriptive: set point of the system's thermostat, typical range of temperature values in the system, the duty cycle of the system's compressor, etc. This informs the user as to whether their storage unit is properly configured to store a particular product. • Predictive. Predictive analytics detect cold chain problems (e.g. poor equipment configuration, human error, equipment failures) and send an alert before temperature bounds are violated. • Prescriptive: provide recommendations to improve cold storage processes and business decisions during normal operation (e.g., optimal thermostat setting for a particular product)
- 15. Example of analytic system: single system
- 16. What’s new? • Data come from several sources (in addition to company data, we have sensors , methereological and geographical data) • Data are aggregated to obtain additional information (e.g., times of day that are busiest and the periods where the system's door is opened the most. This can guide staff training and institutional policies) • Not simple queries (descriptive) but also predictive and prescriptive information. System helps efficient and timely decision making! • Interfaces (visualization) is a key to efficient use and understanding of data
- 17. Study Case two: Hokuriku Coca-Cola Bottling Company (1) • When you select a beverage on a vending machine, you never think that there might be a lot of hard work and careful planning by the bottling companies, who have to decide which drinks will sell best in their machines while at the same time eliminating out-of-stocks and reducing equipment failures. • Hokuriku used big data and data analytics to improve efficiency of vending machines. In 2005, they reported to increase 10% sales and decrease 46% associated costs.
- 18. Study Case two: Hokuriku Coca-Cola Bottling Company (2) • The first step was to provide vending machines with wireless connections to transmit data • Data from vending machines and other traditional sources are collected in a data wharehouse (will se what this is later: for now, like “a large heterogeneous database of structured and unstructured data”) • Wharehouse collects data like: time location and date of each sale, when a product sells out, or is short-changed, and when machine is malfunctioning. They also collect sentiment data from social networks. • Big data technologies (SAP HANA, Hadoop, and Spark ) have been used to manage and intersect these data providing descriptive, predictive and prescriptive analyticts
- 20. Example of analytic system: brands
- 21. Example of analytic system: audience behaviour
- 22. What’s new? • Data come from several sources (in addition to company data, we have near real-time data from single vending machines and social data) • Data are aggregated to obtain additional information (e.g., best seller by time and location, opinions of users, user’s preferences by profile, date, location... This can guide stocking policies and marketing campaigns) • Not simple queries (descriptive) but also predictive (a product will shortly sold-out, fine-tuning policies for refilling: not only if close- to-sold-out, but also based on historical data, consumers’ opinions..) and prescriptive information (targeting users for specific campaigns). System helps efficient and timely decision making! • Interfaces (visualization) is a key to efficient use and understanding of data.
- 23. Case Study 3 • Lennox International is a $3B manufacturer and distributor of heating, air conditioning and refrigeration products. • Lennox describes how it doubled the size of its footprint with – Manhattan’s integrated Warehouse Management System (WMS), – Transportation Management System (TMS) and – Labor Management System (LMS). • See more at: http://www.manh.com/resources/videos/lennox- internationals-supply-chain-integration#sthash.1kjODMBd.dpuf
- 24. Case Study 3: Lennox CHALLENGES ICT SOLUTIONS RESULTS
- 25. • As for Magpie and Coca Cola, use telemetry data (wireless transmission of data), e.g. for tank volumes. • Use also POS (Point Of Sale) data rather than shipment data only. • POS data allows to see the real time data driving the supply chain. Statistical methods can then be applied to bin and cluster the data for further analysis. Case Study 3: Lennox (2)
- 26. Why POS data are helpful? • They enable us to balance either too much optimism or pessimism that can come from shipment trends alone. • They provide more stable and consistent patterns that can be easily used, as well as helps to identify changes in the trend over time. Shipment data are not true indicators of a change in the trend because their data bounce around. • They allow companies to do a “sanity check” on the final forecast numbers by using trade inventory (inventory by store) in the data stream.
- 27. • Determine which distribution centers should service which stores for which specific products, especially in large networks, in order to optimize time, fuel and resources. • Create the most productive delivery schedule for fleets, taking into account factors such as opened and closed stores, route disruptions and seasonal issues • Factor in delivery window optimization, determining the best time for product delivery based on store hours, traffic and other variables. • NOTE: integrating real time data from POS, metereology, and local sensors is crucial here. Case Study 3: Lennox (3)
- 28. Case Study 3: Lennox (4) • Lennox almost tripled the percentage of orders that can be delivered the next morning, yet reduced inventory by almost 20%, even with a 250% increase in physical locations. • They improved service levels by about 20%. They also reduced distribution costs as a percentage of sales by over 15%, increased inventory turnover by more than 20%, and reduced response time.
- 29. Analytic system example: view seasonal demand Seasonal demand at the item level is identified in specific locations within regions (system identifyied over 200 micro-climates).
- 30. What’s new? • Data come from several sources (in addition to company data, we use POS data and telemetry) • Data are aggregated to obtain additional information (e.g., high- demand products and parts that must be available immediately; lower-demand items need to be delivered the same day or next morning. This can guide staff training and institutional policies, such as accurate stock replenishment at the order-line level) • Not simple queries (descriptive) but also predictive and prescriptive information. Advanced analytical techniques to accurately model and forecast complex and unpredictable demand, e.g., according to micro-climates . System helps efficient and timely decision making! • Interfaces (visualization) is a key to efficient use and understanding of data
- 31. SO, WHAT IS BUSINESS INTELLIGENCE?
- 32. © 2008 Accenture. All Rights Reserved. Improving organizations by providing business insights to all employees leading to better, faster, more relevant decisions
- 33. Why do companies need BI? Tactical / Strategic BI (PREDICTIVE) What’s the best that can happen? What will happen next? What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Sophistication of Intelligence Operational BI (PRESCRIPTIVE, DESCRIPTIVE) Optimization Predictive Modeling Forecasting/extrapolation Statistical analysis Alerts Query/drill down Ad hoc reports Standard reports Competitive Advantage © 2008 Accenture. All Rights Reserved.
- 34. Image from Alok Srivastava Where can we apply Business Intelligence? Tactical Apps. Strategic Apps. Suppliers Customers ERP Applications SCM Applications CRM Applications Materials/Component s Consumers Business Intelligence Supply Chain Enterprise Resource planning Customer Relations
- 35. Layers in a BI system Social and sensor data
- 36. Architecture of a BI system OLAP: on-line analytical processing Data selection, extraction, transformation and load Data storage, in relational or multi-dimensional tables Data visualization and analytics: descriptive, predictive prescriptive
- 37. Information Technology challenges in a Business Intelligence System • Data Sourcing: extracting information from heterogeneous data such as texts, databases, images, sensors, media files and web pages • Data Analysis: Synthesizing useful knowledge from collected data using IT techniques such as data mining, text understanding, image analysis and signal processing • Knowledge extraction: Extracting useful (= both relevant and frequent) facts and rules and filtering out irrelevant information; • Decision Support: employ semi-interactive software to identify good decisions and strategies
- 38. Benefits of Business Intelligence for Business Users • Improve Management Processes – planning, controlling, measuring and/or changing resulting in increased revenues and reduced costs – Improve Operational Processes – fraud detection, order processing, purchasing.. resulting in increased revenues and reduced costs • Predict the Future (sales, out-of-stock,…)
- 40. Yes, but why should you take this course? • Because business users are closest to the data going into BI systems, their involvement is paramount to successful business intelligence management efforts • Companies should NOT let the IT department control of BI projects, particularly if they're setting up a centralized team to take charge of BI management enterprise-wide. • While IT's strengths in data preparation, management and governance are critical for effective BI initiatives, treating a BI program as yet another IT project doesn't bode well for its long-term success.
- 41. Role of Business users in managing BI projects • Define needs: Business users sometimes have a hard time to adequately translating their requirements for BI and IT developers: so task 1 for BI users is learn accurately identify the BI goals; • Be knowledgeable: Managing a BI program takes a special breed of employee, one with a combination of technical, people and process management skills. Don’t need to design algorithms nor to be a programmer, but BE AWARE of the technology (and stay up to date..) • Help creating reports and dashboards: Interfaces and dashboards for BI do not answer only pre-defined queries as “old” OLTP systems, but even though they are much more flexible, they cannot manage data they have not been designed for. Business Users should learn defining the objectives of the analytics: which data and “what for” (in cooperation with IT people).
- 42. Business people should NOT be data consumers! Easy to lose focus of the real vision and milestones to arrive at the final goal. Efforts can easily be deemed a success by IT measurements (good infrastructure, performance, coding, change management, etc.) but a failure to the business users!
- 43. A balanced view Who is an IT person? Who is a Business person?
- 44. What are the typical roles in a BI project team (and who could YOU be) • Advanced Business User • Professional Report Author • Modeler • Administrator • Analyst • Business User ABU: This person has a deep understanding of the business needs and a good understanding of technology. The Advanced Business User leads the interpretation of business requirements and creates reports to answer business questions.
- 45. SO WHAT IS THIS COURSE ABOUT?
- 46. Course Syllabus (6 CFU) 1. Data Wharehousing: describe the process used in developing and managing a data wharehouses 2. Decision Support Systems: how to extract knowledge from data (with some “contamination” with Machine Learning students from MD in Computer Science) 3. Social networks and Social Analytics: social media and web as new source data to better engage clients 4. Business Reporting and Visual Analytics: why visual analytics is crucial for BU, and what are the new visualization startegies 5. Lab Application: using IBM Cognos (with IBM tutors)
- 47. Final Exam • Homeworks (case studies, tests and practical exercises) 10-15% • Written exam (50%) • Project (35-40%)
- 48. Homework 1 • Select one BI use cases. Find material on the web http://www.teradata.com/Resources?AssetType=Case+ Studies • Answer the following (for each case): – What was the key problem for the company, when deciding to adopt BI? – Which information is provided to the BI analytics tool? – Give examples of descriptive, predictive and prescriptive analytics supported by the BI systems. – Which business performance features are highlighted as success indicators? – Any free comment you may want to add.
- 49. Next monday • Teams will describe their business case first! • All must handle two pages report via email • Submit on shared folder
- 50. TERADATA case studies To search for interesting use cases, select appropriate filters