Business intelligence (BI) uses data about past and present to help companies make better decisions for the future. BI provides timely, accurate insights that are valuable and can be acted upon. It helps companies operate more efficiently and profitably by supporting better strategic and tactical decision making. As BI systems evolve to deliver analytics to mobile devices in near real-time, more companies are using BI to promote a data-driven culture and rational decision making processes.

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