Transcript
Page 1: IMEX Frankfurt - BIG DATA session - Human Equation

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

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The GapHistory of data

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Last centuryThe birth and raise of the Information Technology

DATA

TIME

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Last centuryThe value of data

DATA

TIME

$$$

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Next decadeThe Gap

DATA

TIME

THE GAP

$$$

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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.

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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

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Outsourcing changes

41

%

59

% 100%

250h 360h 610hSpent on the project

Client Human Equation Total project

+ =

You can’t buy your way out of this one…

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5 sources of dataFor a typical organization

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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

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What is Open Data?Changing the discussion

• Free

• Structured

• Automatically updated

• Organic

• Real-Time

• Universal

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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

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Case Study 01From Champion to Sponsored

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Available data

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Case Study 02From Visitors to Clients

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Next steps

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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

from organic sources

Do you have a Data Governance and Performance plan ?

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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

from organic sources

Do you have a Data Governance and Performance plan ?

Page 41: IMEX Frankfurt - BIG DATA session - Human Equation

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

from organic sources

Do you have a Data Governance and Performance plan ?

Page 42: IMEX Frankfurt - BIG DATA session - Human Equation

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

from organic sources

Do you have a Data Governance and Performance plan ?

Page 43: IMEX Frankfurt - BIG DATA session - Human Equation

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

from organic sources

Do you have a Data Governance and Performance plan ?

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Merci!

e. [email protected]. @david_brochul. linkedin.com/in/davidbrochu


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