Bid data session given at IMEX Frankfurt, on May 2014. Human Equation has been invited by ICCA for this training on how to use big data for associations, convention bureau, tourism office and venues. Open data, big data, data governance & performance.

1. BIG DATAWhy is this Issue a Big Deal for International Associations?

2. The GapHistory of data

3. Last centuryThe birth and raise of the Information Technology
DATA
TIME

4. Last centuryThe value of data
DATA
TIME
$$$

5. Next decadeThe Gap
DATA
TIME
THE GAP
$$$

6. Why you should careWorking the GAP
1
3
2
Your data value is decreasing rapidly
Cost to keep data up-to-date is increasing as much as the required time
to ensure quality and consistency.
Your data is fragmenting and leaking
Processing data is getting cheaper. Your data (clients, ambassadors, etc…) is leaving traces
in other databases. With enough sources, someone could be able to figure out your
internal data.
You don’t want to be the last man standing
More and more value is being generated by opening up your data rather than sinking with it.
Open Data and Big Data enable you to extract business intelligence at a never available before
level.

7. Truth is: The GAP is getting hugeWorking the GAP
“During 2008, the number of things
connected to the Internet exceeded
the number of people on earth.
By 2020, there will be 50 billion.”
- CISCO

8. Outsourcing changes
41
%
59
% 100%
250h 360h 610h
Spent on the project
Client Human Equation Total project
+ =
You can’t buy your way out of this one…

9. 5 sources of dataFor a typical organization

10. The 5 DATA SOURCESYou should be looking at
1. Internal Data
2. Semi-Structured Data
3. Social Media Data
4. Paid Data
1. Open Data

11. What is Open Data?Changing the discussion
• Free
• Structured
• Automatically updated
• Organic
• Real-Time
• Universal

12. What is Open Data?Change in the discussion
“Adopted by 41 governments, Open Data
has now reach a critical mass of more than
10 million datasets.”
- Wikipedia

13. Case Study 01From Champion to Sponsored

29. Available data

30. Case Study 02From Visitors to Clients

38. Next steps

39. CMM – Capacity Maturity ModelData Governance and Performance Plan
1. Initial - data is not centralized and managed by a committee.
Extraction of business intelligence is chaotic, ad hoc, individual
heroics.
2. Repeatable - the data is centralized and at least documented
sufficiently such that stakeholders can get some business intelligence
3. Defined - the plan is defined/confirmed as a standard business
process, and connect various data to stakeholders
4. Managed - the plan is now quantitatively in accordance with agreed-
upon performance and metrics. Instructions.
5. Optimizing – the data governance plan is growing, challenging
stakeholders with new, unrequested business intelligence coming
Do you have a Data Governance and Performance plan ?

40. CMM – Capacity Maturity ModelData Governance and Performance Plan
1. Initial - data is not centralized and managed by a committee.
Extraction of business intelligence is chaotic, ad hoc, individual
heroics.
2. Repeatable - the data is centralized and at least documented
sufficiently such that stakeholders can get some business intelligence
3. Defined - the plan is defined/confirmed as a standard business
process, and connect various data to stakeholders
4. Managed - the plan is now quantitatively in accordance with agreed-
upon performance and metrics. Instructions.
5. Optimizing – the data governance plan is growing, challenging
stakeholders with new, unrequested business intelligence coming
Do you have a Data Governance and Performance plan ?

41. CMM – Capacity Maturity ModelData Governance and Performance Plan
1. Initial - data is not centralized and managed by a committee.
Extraction of business intelligence is chaotic, ad hoc, individual
heroics.
2. Repeatable - the data is centralized and at least documented
sufficiently such that stakeholders can get some business intelligence
3. Defined - the plan is defined/confirmed as a standard business
process, and connect various data to stakeholders
4. Managed - the plan is now quantitatively in accordance with agreed-
upon performance and metrics. Instructions.
5. Optimizing – the data governance plan is growing, challenging
stakeholders with new, unrequested business intelligence coming
Do you have a Data Governance and Performance plan ?

42. CMM – Capacity Maturity ModelData Governance and Performance Plan
1. Initial - data is not centralized and managed by a committee.
Extraction of business intelligence is chaotic, ad hoc, individual
heroics.
2. Repeatable - the data is centralized and at least documented
sufficiently such that stakeholders can get some business intelligence
3. Defined - the plan is defined/confirmed as a standard business
process, and connect various data to stakeholders
4. Managed - the plan is now quantitatively in accordance with agreed-
upon performance and metrics. Instructions.
5. Optimizing – the data governance plan is growing, challenging
stakeholders with new, unrequested business intelligence coming
Do you have a Data Governance and Performance plan ?

43. CMM – Capacity Maturity ModelData Governance and Performance Plan
1. Initial - data is not centralized and managed by a committee.
Extraction of business intelligence is chaotic, ad hoc, individual
heroics.
2. Repeatable - the data is centralized and at least documented
sufficiently such that stakeholders can get some business intelligence
3. Defined - the plan is defined/confirmed as a standard business
process, and connect various data to stakeholders
4. Managed - the plan is now quantitatively in accordance with agreed-
upon performance and metrics. Instructions.
5. Optimizing – the data governance plan is growing, challenging
stakeholders with new, unrequested business intelligence coming
Do you have a Data Governance and Performance plan ?

44. Merci!
e. dbrochu@humanequation.com
t. @david_brochu
l. linkedin.com/in/davidbrochu