1. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Competing on Analytics Robert Monroe March 20, 2008

2. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Key Ideas Competing on analytics requires much more than just a few stats geeks with powerful computers Commitment to aggressively apply analytic results to decision making is needed at all levels of the business You need to carefully select where to apply your analytical prowess to create competitive advantage You need a lot of high quality data to do this well

3. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques What Are Analytics? By analytics we mean the extensive use of data, statistical and quantiative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. - [DH07] p. 7.

4. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques What Does It Mean to Compete On Analytics? Analytics can support almost any business process. Yet organizations that want to be competitive must have some attribute at which they are better than anyone else in their industy – a distinctive capability. … Analytics themselves don’t constitute a strategy, but using them to optimize a distinctive business capability certainly constitutes a strategy - [DH07] p. 9.

5. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Common Characteristics Of Analytic Competitors

6. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Support A Strategic, Distinctive Capability Having a distinctive capability means that the organization views this aspect of its business as what sets it apart from competitors and as what makes it successful in the marketplace. - [DH07] p. 24

7. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Enterprise-Level Approach To And Mgmt Of Analytics Data spans the enterprise Analytic approach pervades the enterprise Local analysis, enterprise-level data –Single version of the truth

8. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Senior Management Commitment … as with so many other initiatives

9. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Large-Scale Ambition Think big and reap economies of scale

10. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Gaining Competitive Advantage… Requires analytical capabilities that are: –Hard to duplicate –Unique –Adaptable to many situations –Better than the competition –Renewable [DH07] pp 48-49 Examples?

11. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Where Is An Analytic-Driven Strategy Appropriate? Some industries [and processes] are clearly more amenable to analytics than others - [DH07] p. 10 What types of industries and processes are particularly well suited to analytics-based strategies? What types of industries and processes are particularly ill-suited to analytics-based strategies?

12. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques From Data To Knowledge

13. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Definitions What is the difference between data, information, and knowledge? Data is a collection of raw value elements or facts used for calculating, reasoning, or measuring. Data may be collected, stored, or processed but not put into a context from which any meaning can be inferred. [Los03] Information is the result of collecting and organizing data in a way that establishes relationships between data items, which thereby provides context and meaning. [Los03] Knowledge is information to which experience, interpretation, and reflection are added by individuals so that it becomes a high value form of information –The OR Society http://www.orsoc.org.uk/about/topic/projects/kmwebfiles/knowledge.htm

14. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Exercise 3/21/05$27.743/22/05 $27.01 3/21/05$19.783/22/05 $19.72 3/21/05$21.413/22/05 $21.50 3/21/05$83.813/22/05 $84.24 MSFT INTC CSCO IBM 3/21/05 3/22/05 3/22/05 3/21/05 3/22/05 3/22/05 3/21/05 3/21/05 $27.74 $19.78 $21.41 $83.81 $27.01 $19.72 $21.50 $84.24 CSCO MSFT INTC Closing Stock Prices Knowledge: Buy GOOG!

15. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Goal: Convert Data to (Actionable) Knowledge Data Info Knowledge Increasing Value Why is this so hard to do in practice?

16. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Challenge: What To Capture and Store? The amount of data that can be captured is enormous –Storing data is relatively cheap (  free @ the margin) –Structuring and retrieving data is relatively expensive –Converting large data sets to actionable knowledge tends to be relatively challenging and expensive Rules of thumb for deciding what to capture and store –Start with what you want to get out and work backwards –Evaluate what is already available –Insure that you capture high-quality data –Analyze fundamental data requirements for the enterprise, independent of the specific project at hand

17. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Exercise: What To Capture And Store Scenario 1: You are a marketing VP for a national (USA) toy retailer. You need to figure out how to properly price and promote a specific brand of toy dolls over the next year What questions do you need to ask? What analyses would you like to do to answer them? What data will you need to do these analyses? Where will you get that data? –Is your organization likely to already have all the data that you need? –Are there other data sources that you should try to take advantage of and incorporate into your analyses?

18. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Exercise: What To Capture And Store Scenario 2: You are an executive at Ferrari who needs to decide how to allocate the latest and greatest sports car your company is introducing in six months to maximize your company’s profits long-term What questions do you need to ask? What analyses would you like to do to answer them? What data will you need to do these analyses? Where will you get that data? –Is your organization likely to already have all the data that you need? –Are there other data sources that you should try to take advantage of and incorporate into your analyses?

19. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Exercise: What To Capture And Store Scenario 3: You are a HR executive responsible for recruiting salespeople. Your bonus each year is directly tied to how well the salespeople you bring in do in their first three years at your company. You’ve read Moneyball and Competing on Analytics, and you want to take a more analytic approach to your job What questions do you need to ask? What analyses would you like to do to answer them? What data will you need to do these analyses? Where will you get that data? –Is your organization likely to already have all the data that you need? –Are there other data sources that you should try to take advantage of and incorporate into your analyses?

20. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques The Role Of Data For Analytic Competitors In the future, software availability will not be an issue in analytical competition, although the ability to use analytical software well won’t ever be a commodity. - [DH07] p. 15. Question: if powerful analytical software becomes a commodity (perhaps it already is) what role will the ability to acquire huge quantities of unique, high- quality data play in the ability of organizations to compete on analytics?

21. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques A Quick Look At Some Analytics Competitors

22. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Companies Posted To Class Wiki As Examples … none posted by Wednesday

23. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques In-Class Exercise Form groups of 2-4 people You will be given an organization to evaluate Select a business process or functional area of that organization Identify multiple ways that the organization could (or does) use analytics to improve its business for that process or functional area –What types of analyses could (or do) they do? –What are the business benefits of doing the analysis effectively? What are the costs of doing the analysis? –Could these analyses form the core of a competitive advantage? Are they aligned with the businesses overall strategies? Why or why not? –What data do they need to collect to conduct these analyses? –How might they need to adjust their organization or business processes to take advantage of the results of these analyses?

24. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Wrap Up

25. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Key Ideas Competing on analytics requires much more than just a few stats geeks with powerful computers Commitment to aggressively apply analytic results to decision making is needed at all levels of the business You need to carefully select where to apply your analytical prowess to create competitive advantage You need a lot of high quality data to do this well

26. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques Homework #1 Posted to class wiki Teams of 2-4 people Two options. Pick one: –Managerial option: write two memos, one making the business case for a data warehousing project, the other responding with an analysis of how you should proceed with the project. Due Thursday, April 3 –Technical option: Create a small data mart and do the ETL to fill the data mart with data from a transactional data store. Due Tuesday, April 8

27. Carnegie Mellon University ©2006 - 2008 Robert T. Monroe 45-875 BI Tools and Techniques For Tuesday 3/25 Tuesday we begin the data warehousing module Preparation for class: –Required reading: [Los03] chapters 4, 6 –Suggested reading: [Los03] chapters 1-3