1. Decision Support for Management Data+ Models+Intuition

2. Decision support systems –use data and models to support management decision making in different ways. Nowadays, what used to be called decision support systems often comes under the umbrella of business intelligence. Analytics describes the application of mathematical techniques to organisational operations.

3. Collecting Data Organisations need to collect data in order to be able to understand and improve their business. They need models (from analytics) to interpret this data. June 30, 20153

4. Example:Looking at data Harmful Algal Blooms www.marine.ie Click on the data tab Click on HABS search And tell me whether Donegal bay and Sligo are allowed to sell fish/shellfish or not. [in addition to automatic notification by e- mail, fax and SMS]

5. 5 Modeling and Data A model is a selective abstraction of reality. –Selective We choose which bits to put in the model. –Abstraction The model is not reality it is a simplification.

6. 6 Modelling A model is way of representing a part of the environment. Trade-off between the simplification and the representation of reality. –Advantages of simple models. –Disadvantages of simple models.

7. 7 Types of Models Mental Models Visual –Also called analogue. Physical/Scale –Also called iconic. Mathematical –Also called quantitative.

8. 8 Benefits of a Model Can compress time. Can manipulate easily. Can do trial and error calculations. Can model risk and uncertainty.

9. 9 Benefits of Producing Models Having a model to use is beneficial but the process of producing a model is equally if not more beneficial. Helps you to understand the problem. –Need to be explicit about your goals. –Need to quantify the variables which affect the goals. –Need to identify constraints and relationships between variables. –Facilitates communication and understanding.

10. Example: Simulation Models http://www.bized.co.uk/virtual/economy/ model/http://www.bized.co.uk/virtual/economy/ model/ http://www.xjtek.com/anylogic/demo_mo dels/?simulation_method=System++Dynam ics

11. Business Intelligence Overview BI involves acquiring data and information from a wide variety of sources and utilising them in decision-making.

12. in-depth analysis of company data for better decision-making. Models are used to analyse data.

13. Business Intelligence Business intelligence (BI) simplifies information discovery and analysis, making it possible for decision-makers at all levels of an organization to more easily access, understand, analyze, collaborate, and act on information, anytime and anywhere. (Microsoft)

14. The technology and processes that make this analysis possible take unwieldy collections of information and translate them into organized, readily-accessible, human-readable compilations of data.

15. What can companies do with BI? Track their own operations customers’ activity patterns industry trends. fact-based assessments help companies work toward specific goals with confidence.

16. Data is 1.Gathered from relevant sources 2.Filtered, and stored 3.Analysed and arranged into meaningful patterns using different tools. 4.Business intelligence is the knowledge gained from that data analysis.

17. Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation

18. .... From Turban, Aronson and Liang

19. Some Questions Where does the data come from? How can we decide what data is important? How can data from different sources be joined together (consolidated and integrated) securely? How can data be analysed? How can these analyses be viewed?

20. Where does the data come from? Data can be collected manually or automatically. –Transaction data e.g. supermarket checkout, bank withdrawal –Time studies, questionnaire, observation notes –Physical sensors e.g. temperature of a rooms in a house –Sensors, scanners, bar codes

21. How can we decide what data is important? Depends what our goals are, the functional area(e.g. Sales, HR, marketing..) and what processes we are looking at.. Balanced scorecard Critical success factors Key performance indicators

22. Sales and marketing products customers demographics promotions sales force order type Human resources employee organizational departmental measures Operations management assembly speed warehouse stock manufacturer and supplier cost shift productivity Finance currency standards account information industry trends Functional Areas

23. Data Quality is also important Garbage in..... Garbage out Contextual – relevance, value, timeliness completeness, amount Intrinsic – accuracy, objectivity, believability, reputation Accessibility DQ – ease of access,security Representation DQ – interpretability, ease of understanding, concise, consistent representation.

24. Example : ecological intelligence? What information can we access on www.goodguide.com www.goodguide.com What do you think the goals of someone using this website might be? What type of data do you think has been analysed to give this information? Where did the data come from? How reliable is the data? Can you find out how it was analysed?

25. Example: What is ecological intelligence? a vast, shared network of detailed information regarding the full social and ecological impact of products. Consumers will be able to use an array of new wireless and web-based technologies to instantly tap into this network to find product information, even at the point of purchase.

26. Example: Ecological Intelligence How is the data analysed? Industrial ecologists and engineers deconstruct the ingredients and processes that go into any manufactured object and do a Life Cycle Assessment, or LCA. This allows them to track a product’s precise social, health and ecological effects from production to final disposal.

27. Example Data Warehouse Technology : Microsoft How can data from different sources be joined together (consolidated and integrated) securely? SQL Server provides a comprehensive and scalable data warehouse platform organizations build large-scale enterprise data warehouses that can consolidate data from multiple disparate systems into a single, secure, manageable solution.

28. 28Data 3 What is a Data Warehouse? A data repository that makes operational and other data accessible in a form that is readily acceptable for decision support and other user applications. Note: A data warehouse is not another word for a database. The specific purpose of a data warehouse is to support decisions not operations.

29. Data warehouses vs operational databases an operational database is normalised. Each data item is only held once. databases have very fast insert/update performance because only a small amount of data in those tables is affected each time a transaction is processed. Older data may be periodically purged from operational systems to improve performance. Data warehouses are optimized for speed of data retrieval. data in data warehouses may be stored using a dimension-based model. To speed data retrieval, data warehouse data are often stored multiple times. Data may be held in the data warehouse even after the data has been removed from the operational systems.

30. How is the data analysed? Analytics techniques – types of model –Simulation –Decision analysis –Statistics : averages, correlations, –Linear programming: optimisation –Queuing theory: “waiting line”analysis –Network analysis: Maximise flow through a network e.g. A supply chain –Multi-criteria decision making: scoring models

31. Example – Microsoft OLAP How can data be analysed? Microsoft Online Analytical Processing (OLAP) makes it quick and easy to perform ad-hoc queries and analysis of large amounts of complex data across all aspects of your business.

32. Example – Microsoft OLAP Microsoft OLAP is used to report on... sales marketing management issues business process management budgeting and forecasting, financial issues etc..

33. What is OLAP? OLAP enables you to look at and access data in different ways (3-d data cubes), drill down, view summarised data, make calculations on the fly etc. http://spatialolap.scg.ulaval.ca/Examples.a sphttp://spatialolap.scg.ulaval.ca/Examples.a sp http://www.census.gov

34. What is Data Mining? Data mining is a capability to support the recognition of previously unknown but potentially useful relationships within large databases/ data warehouses. Basically software to analyse data and spot patterns.

35. Visualising Data Digital images- These can be still or animated. Maps e.g. Geographic Information Systems Multidimensions - (OLAP) Tables and graphs Virtual reality Dashboards

36. A Table

37. A Chart

38. Dashboards Taken from http://gbr.pepperdine.edu/034/bis.html

39. multiple, synchronized chart types

40. A visualization with multiple displays showing a Supplier scorecard in conjunction with a geographical display. Return to Document

41. Summary Decision support involves data and models BI involves acquiring data and information from a wide variety of sources and utilising them in decision-making. Data is –Gathered, selected –Consolidated and integrated -> data warehouse –Analysed in different ways (analytic techniques) –Results are Visualised

42. We need to Understand Data issues – data quality Where data comes from How data is stored: data warehouses How data is analysed Tools to do this. Limitations of the computer Our own blind spots (if this is possible)!

43. References Advanced Analytics- Information Week 2010 (analytics.informationweek.com) Competing on Analytics - Thomas Davenport Harvard Business Review Jan 2006 In search of Clarity - Economist intelligence unit 2007 (available from sap) What is Business Intelligence (sap)