Business Intelligence intro Matti Rossi, Professor Aalto University School of Economics Information Systems

Transcription

1 Business Intelligence intro Matti Rossi, Professor Aalto University School of Economics Information Systems

2 BI definition Business Intelligence (BI) refers to skills, technologies, applications and practices used to help a business acquire a better understanding of its commercial context. Source: WikiPedia,

3 Business Intelligence (BI) BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies BI's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis BI helps transform data, to information (and knowledge), to decisions and finally to action

4 A Brief History of BI The term BI was coined by the Gartner Group in the mid-1990s However, the concept is much older 1970s - MIS reporting - static/periodic reports 1980s - Executive Information Systems (EIS) 1990s - OLAP, dynamic, multidimensional, ad-hoc reporting -> coining of the term BI Inclusion of AI and Data/Text Mining capabilities; Webbased Portals/Dashboards 2010s Mobile, real-time marketing campaigns

5 Business Pressures Responses Support Model

6 CRISP-DM = Cross-Industry Standard Process for Data Mining The purpose of CRISP-DM is to present an industry, tool and application neutral description of data mining / knowledge discovery process (non-linear, repeatedly backtracking). Source:

7 The Evolution of BI Capabilities

8 Competitive intelligence the action of defining, gathering, analyzing, and distributing intelligence about products, customers, competitors and any aspect of the environment needed to support executives and managers in making strategic decisions for an organization BI has internal focus and CI external focus

9 Business Intelligence Done Right Turns Data Into Information Information Into Knowledge Knowledge Into Decisions Decisions Into Profits

10 Business Intelligence is used for Understanding business through collected data Answering questions such as: Which Of Our Customers Are Most Profitable? Where Are Our Most Profitable Customers? Who Are Our Customers? Which Products Cost The Most To Maintain? Where Can We Cut Costs?

11 BI users 80% are casual users relying on production reports Senior executives - Use monitoring functionalities Middle managers and analysts - Ad-hoc analysis Operational employees - Prepackaged reports - E.g. sales forecasts, customer satisfaction, loyalty and attrition, supply chain backlog, employee productivity Turban 2011

12 BI and Business Strategy To be successful, BI must be aligned with the company s business strategy. BI cannot/should not be a technical exercise for the information systems department. BI changes the way a company conducts business by improving business processes, and transforming decision making to a more data/fact/information driven activity. BI should help execute the business strategy and not be an impediment for it!

13 Real-time, On-demand BI The demand for real-time BI is growing! Automated, faster data collection (RFID, sensors, ) Database and other software technologies (agent, SOA, ) are advancing Telecommunication infrastructure is improving Computational power is increasing while the cost for these technologies is decreasing Trend -> Business Activity Management

14 Data and information in organizations Source: cullen/index.html

15 The Architecture of BI A BI system has four major components a data warehouse, with its source data business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse; business performance management (BPM) for monitoring and analyzing performance a user interface (e.g., dashboard)

16 A High-Level Architecture of BI

17 What we need to get there?

18 Transaction data Data produced in the day to day operations of a company Usually stored in relational databases Optimized for storing and retrieving individual data items in real time

19 Data warehouse A decision support database that is maintained separately from operational databases A consistent database source that bring together information from multiple sources for decision support Support information processing by providing a platform of consolidated, historical data for analysis Data warehousing: The process of constructing and using data warehouses A data warehouse is based on a multidimensional data model which views data in the form of a data cube

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21 Examples of BI applications Predictive analytics - Use patterns in data to predict future behavior - E.g. Credit card companies use predictive analytics to determine customers at risk for leaving Data visualization - Help users see patterns and relationships that would be difficult to see in text lists Geographic information systems (GIS) - Ties location-related data to maps Turban 2011

22 Data Warehousing E-Business Infrastructure for Finance E-Business Accounting Operations Analytic World Industryspecific / Legacy Systems ERP Systems Billing & Payment Systems CRM- Systems SCM Systems E-Commerce Systems Business Partner Systems / ASP Enterprise Application Integration (Middleware) Accounting Engine SHARED MASTER DATA AND META DATA Analytics data transfer / transactions trigger OLAP / Data Warehousing BW Acc. Views SHARED BUSINESS FUNCTIONS Data Mart Data Mart Data Mart Analytics / Reporting Strategy & Enterprise Management Business Analytics Reporting Source SAP ag

23 Advanced example: Source: Henrikki Hervonen

24 Simple Visualisation of Complex Data Extremely attractive High interactivity Feather light Highly scalable Easy to build and maintain

25 Performance Dashboards Facebook report examples: facebook-reporting-tools-in-depth-analysis-fan-pages/

26 Geospatial data 1 Own sensors Cell phone. Water metering Energy consumption Connecting and visualizing data from different sources Display on map, Windshield display, or dashboards Sources: Open Data, Commercial databases, Crowd sourced measuring devices Weather Lake water temperatures Water quality Traffic Air quality Statistics Energy production Factory pollution Syncron Tech Oy Source: Syncron Tech Oy white paper

27 Geospatial data 2 Source: Deloitte LLC

28 Target case Target build predictive models of trying to identify when women become pregnant They analyzed the known pregnant customers against all customers and found 21 variables that predict pregnancy: buying large quantities of unscented lotions extra large bags of cotton balls scent-free soaps Etc. The model was very accurate, but risky to use Why important and why risky?

29 High velocity automated decision making Made possible through computer algorithms precisely defining steps for a highly structured decision Humans taken out of decision E.g. High-speed computer trading programs - Trades executed in 30 milliseconds - Responsible for Flash Crash of 2010 Require safeguards to ensure proper operation and regulation Turban 2011

30 Summary Business intelligence is based on the use of data resources of a company for better decision making Key is the aggregation, transformation and presentation of the data in ways useful for decision making

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32 Further reading Watson, Hugh J. (2009) "Tutorial: Business Intelligence Past, Present, and Future," Communications of the Association for Information Systems: Vol. 25, Article 39. Available at:

33 BI Resources Teradata University Network A great and free academic resource for BI (the available resources include cases, articles, tools including Microstrategy, datasets, exercises, etc. The Data Warehousing Institute (tdwi.org) The OLAP Report (olapreport.com) DSS Resources (dssresources.com) Business Intelligence Network (b-eye-network.com) AIS World (isworld.org) Microsoft Enterprise Consortium (enterprise.waltoncollege.uark.edu/mec)