1.
How to Make the
Most out of
Enterprise Data?
Dr. Stefan Tweraser

2.
The Big Data explosion has fundamentally
changed corporate decision making.
• Ever more data to create information
• Always more stakeholders with access
• Better and real-time situational awareness
• Leaders not able to keep up with these challenges

3.
To keep setting the pace, senior leaders
must deal with four Big Data challenges.
1. Have the infrastructure to treat your data as a utility
2. Keep up with your quants (© Thomas H. Davenport)
3. Learn how machines learn
4. Get your hands “dirty” with
• data,
• analytics and
• the right kind of leadership

4.
Your company needs to have the
infrastructure to treat data as a utility.
• Utility: useful, profitable, and beneficial
• Infrastructure is more than just computers
• Quality of data drives quality of decision making
• Example: Match-and-merge of customer data

5.
Even (or especially) senior leaders need to
learn the fundamentals of analytics.
Recognize
problem or
question
Act
Think
Review
previous
findings
Model solution
and select
variables
Present analysis
and act on
results
Analyze
data
Collect
data
Recognize
problem or
question

6.
No matter how much you trust your quants,
don’t stop asking them tough questions.
© Thomas H. Davenport
• What was the source of your data?
• How well do the sample data represent the population?
• Does your data distribution include outliers (and did they affect the results)?
• What assumptions are behind your analysis?
• Why did you decide on that particular analytical approach?
• What alternatives did you consider?
• How likely is it that the independent variables are actually causing the
changes in the dependent variable?
• Might other analyses establish causality more clearly?
• …

7.
Across your organization, establish a
culture of inquiry, not advocacy.
1. Never pressure your quants with comments like
“See if you can find some evidence in the data to
support my idea.”
2. Big Data biggest challenge is to convince people
not to trust their judgment – but to trust the data
and analysis.
3. Beware of both "Paralysis by Analysis" and
"Extinction by Instinct" © Ann Langley

8.
As strange as it sounds, senior leaders
need to learn how machines learn.
• Machine learning provides computers with the ability
to learn without being explicitly programmed
• Three steps towards “forward looking analytics”
• Understand what happened (descriptive analytics)
• Explore why it happened (diagnostic analytics)
• Predict what is likely to happen next (predictive analytics)

9.
A good CEO gets his hands „dirty“ with data,
analytics and the right kind of leadership.
1. Just adding a Chief Data Officer isn’t enough.
2. Analytics is harder than many executives expect:
2 out of 3 analytics initiatives fail .
3. Analytics can spark power shifts in the C-suite -
one of the most difficult challenges for CEOs.

10.
Big Data in hospitality industry needs work
but will drive success and guest satisfaction.
Treat your data
as a utility
Keep up with
your quants
Learn how
machines learn
Get your hands
“dirty”
• Too many spreadsheets
• Too many systems
• Not enough benefits
• Too few quants to start with
• Not enough focus
• Not enough knowledge
• Too much manual data
handling
• Not enough focus
• Very rarely top management
focus; little awareness
• Introduce smart BI as first
step, align with SOPs
• (Re)Take analytics 101
• Empower your smartest 10%
• Bring in ‘real” quants
• See what it takes to ‘prohibit’
manual interference w/ data
• Create leadership awareness
• Reward enquiry
• Make it a personal priority
TODAY TO DO

11.
Thank you.