A brief introduction to business intelligence (BI) and its importance.

1. Business Intelligence
Michael Lamont, ’12
lamont@post.harvard.edu

2. Decisions Strategic Tactical

3. Decisions

4. Decisions

5. Decisions
Possible to make a good decision without data technology
Looking at the right data can help you make better decisions
Decisions aren’t judged on a binary “good” or “bad” scale.

6. Decisions
Decision quality is measured on a gradient scale
Disastrous
Excellent

7. Decisions
Decisions drive companies
Better decisions lead to:
More efficient operation
Higher profitability
Greater customer satisfaction
Companies that make better decisions are more successful

8. Business Intelligence
Business Intelligence (BI): Using data about yesterday and today to make better decisions about tomorrow
BI makes companies smarter:
The right criteria to judge success
Locating and transforming the right data
Arranging information
Lets management see things more clearly, and glimpse the future

9. Limited Resources, Unlimited Decisions
Every organization has to make do with only what they have, all the time
You can’t hire only the brightest minds and spend unlimited money on efficiency
Time is the most precious resource your company has – it has to move quickly, not just correctly

10. Limited Resources, Unlimited Decisions
BI is a powerful ally when a decision is required
Flexible resource that can be used by any level of the organization

11. Limited Resources, Unlimited Decisions
Examples of simultaneous BI tasks:
VP of Sales deciding which markets & accounts to target to meet sales targets
Product developers deciding which fragrances to use in future products
Gulf Coast marketing team deciding on holiday weekend promotions

13. BI Defined
Lots of very different definitions of business intelligence exist
BI used to be a marketing buzzword that got tied to not strictly related technologies
Every vendor invents a definition that skews toward their products
Researchers, authors, & consultants all have their own pet definitions

16. BI Defined
Business Intelligence is timely, accurate, high-value, and actionable business insights, and the work processes and technologies used to obtain them.
There’s no “magic list” of processes or software that constitute BI
BI is high tech, and thus always evolving
Every company’s situation is different

17. Insights
Insights should flow out of successful BI projects
An insight is:
A new way to look at things
A moment of clarity
A path forward
Something that you didn’t already know about your company

18. Business Intelligence
Accurate
Valuable Timely
Actionable

19. Accurate Answers
Decisions should be:
Informed by data
Made by subject matter experts
Based on hard information
For BI to be valuable, it has to:
Reflect objective reality
Adhere to strict standards of correctness
Accuracy is a core attribute of BI insights

20. Accurate Answers
 BI is subject to “Garbage In, Garbage
Out” (GIGO) rule
Business Intelligence Process

21. Inaccuracy Example
Sales exec sees a region lagging behind
Senior executives adjust sales process (and personnel) in that region
What if the insight was wrong?
Some sales offices were incorrectly allocated to neighboring region
Sales volume wasn’t correctly allocated
Actions taken were less than helpful – may have made things worse

22. Accurate Answers
Accuracy is also important from a political perspective
BI can’t have a real impact unless people trust it
BI insights can be
Surprising
Counterintuitive
Threatening to some groups/managers

23. Accurate Answers
Any error, no matter how small, is going to be used to call into doubt every conclusion pulled from the data
BI has to be as accurate as possible to protect its reputation from skeptics
Inaccurate BI insights are worse than useless – they’re damaging
One bad BI experience will keep it from ever being trusted again

24. Valuable Insights
Not all insights are created equal
Reporting that people who buy peanut butter also buy jelly isn’t much of an insight
BI should produce information that can have a real impact on a business

25. Valuable Insights
Impact of valuable BI insights can be:
Reduced costs
Increased sales
Operational efficiency
Other positive factors
High-value insights aren’t usually deducible
Insights aren’t always obvious, but can have huge impact

26. Valuable Insight Example
Walmart analysis of most popular products after severe hurricane damage

27. Valuable Insight Example

28. Timely Information
Die Geist der Treppe – the “Spirit of the Staircase”
Delivering facts late in a debate keeps them from mattering
Information delays in business can have the same level of impact

29. Timely Information
Information delays come in many forms:
Workflow (not refreshing data frequently enough)
Technology (lack of computational power and efficiency)
Unexpected logistics issues
The time taken for each step in a BI process, added together, has to be short enough to make results useful

30. Timely Information
Timeliness is a required part of useful insights
High quality BI processes need current information
Analysis products need to be provided to decision makers in time for them to consider all courses of action

31. Actionable Conclusions
Insights are worthless if they can’t be acted upon
Non-actionable insights:
Major competitors should instantly cease operating
Factories should be 20 years newer
Decision support tools will happily find non-actionable insights if you let them

32. Actionable Conclusions
An insight is actionable if there’s a reasonable way to take advantage of the situation Conclusion Action Result

33. The Value of BI
Links information with action
What’s the real value returned from an investment in BI tools and processes?
Promoting and supporting better decision making habits

34. The BI Cycle Raw Operational Data Business Insights Take Action Measure Results

35. The BI Cycle
Companies that follow the cycle have a rational decision making process
Business Intelligence supports the cycle
Obtain insights from operational data
Good insights can be applied to decision making process
Decisions lead to actions, and improved operational results
Cycle repeats and decisions are refined

36. Trends
BI continues to increase in importance to both large enterprises and SMBs
BI is flexible, and responsive to technological advances

37. Trends
BI projects are originating outside IT departments
IT used to be the only group that knew what data and analyses were available
Executives and other decision makers have gotten comfortable with BI

38. Trends
Delivery of analytics to desktop (and mobile devices)
Premier vendors can round-trip data to standard Office applications
Excel includes advanced analytical tools

39. Trends
Data access is becoming more dynamic and approaching near-real-time
BI systems of the future will be able to directly pull data from operational systems
Computational efficiency of BI systems is constantly increasing

40. Conclusions
Business Intelligence isn’t just about computing
Requires a corporate culture that supports data-driven decision making
Business managers must promote data- based decision making
IT has to support the tech behind BI at all levels of the company

41. Conclusions
BI gives you new tools and perspectives
Lets you ponder what-if questions
Decision makers have to know how to ask the right questions
No set rules for determining the “right” reports and analytics for a particular company

42. Conclusions
The right people have to be in the right positions for BI to work
BI is a commitment to rational decision making processes
Must be supported at all levels of the company, by both managers and IT

43. Michael Lamont, ’12
lamont@post.harvard.edu