Strategy and implementation of a data Modernization Journey: strategy Kjeld Stipsen

1. Accenture – Data Modernization Journey
September 2017
Martijn van der Meijden
Kjeld Stipsen

3. INTRO
Martijn van der Meijden
Accenture
Managing Director
Digital Analytics
Kjeld Stipsen
Accenture
Senior Manager
Digital Analytics

4. Insights
Structured – Non-structured
Data Internal – External
Financial – Non- financial
Right decision
Big data
Internet of Things

5. PREVENTIVE MAINTENANCE:
ANALYSIS OF CORRELATION
BETWEEN PREVENTIVE AND
CORRECTIVE MAINTENANCE
CUSTOMER
CHURN AND CLIENT
SATISFACTION
PREDICT ON AN INDIVIDUAL
CLIENT BASIS AND KNOW
EXACTLY WHAT TO PROPOSE AT
WHAT TIME TO PREVENT CHURN
AND TO IMPROVE CUSTOMER
SATISFACTION
SOME EXAMPLES
DECISION MAKING SPEED
AND ACCURACY
DYNAMIC STOCK SYSTEMS:
LOCATION BASED ON DEMAND:
ALGORITHMS BASED ON DATA
HELP YOU TO TAKE THE RIGHT
DECISION.

6. 6Copyright © 2017 Accenture All rights reserved.
Faster
6
What does data modernization mean?
Better
More secure

7. Data is fuel in the digital world
High-performance companies recognize that data
is a strategic asset and strive to adopt a data-
driven culture. Data-based decisions lead to clear
business outcomes, yielding a measurable
return on investment.
Yet, companies are challenged to isolate the
important signals from the noise and
harness that data to change outcomes.
Copyright © 2015 Accenture All rights reserved. 7

8. Democratization
of data and
data discovery
New
data sources
Focus on
advanced analytics
Big data and
hybrid
architectures
Changing skills
requirements
MARKET TRENDS IN A DATA DRIVEN
ORGANIZATION

9. Realign Resources
• Dedicated data leader
• Business + IT integration
• Skill scarcity
Create New Roles
• Data Scientists
• Big Data Engineers
Revise Processes
• Data governance
• Meta data
Organizational Changes
Develop “Data Lake”
• Central repository of data with
redundant nodes dedicated to
specific data usage cases
Procure new platforms
• Data storage
• Analysis and reporting
Integrate existing IT
• Architecture
• Infrastructure
• Tools
Technology Changes
Shift Mindsets
• Data as an asset
Introduce New Processes
• Training
• Proof of concepts  business
as usual
Cultural Changes
Impacts
Implementing Big Data is not (only) an IT Initiative,
it is an organizational journey.
HOW DO WE MODERNIZE OUR DATA TO
UNLOCK THE VALUE BEHIND IT?

10. Engage
STEP 1
• Collaborate with
Business SMEs
to determine and
prioritise areas of
focus
• Identify what
knowledge &
insight within
identified areas
of focus can help
drive reduced
costs, better
customer
experience and
growth
• Identify specific
questions to help
frame the
analysis required
and identify the
target benefits
• Review and
iterate key
questions with
business
stakeholders
• Define and
quantify the
benefit that the
key questions
will deliver
• Identify the proof
points we need
that analysis and
insight to achieve
• Determine where
data can be
attained to
answer the key
questions
identified.
• Outline the
specific data
attributes
required.
• Assess the
quality and
completeness of
required data
attributes
• Determine how
available data
can be modelled
to produce the
insight that
provide answers
to the key
questions
identified
• Agree how the
data can be
enriched from
externals
sources if
applicable
• Develop iterative
data analytics
analysis to drive
out insight
required to meet
data set
hypotheses
• Explore options
to use and
machine learning
/ AI to enable
extra analysis
and value from
the data sets
• Work with pilot
group to validate
the business
value of
answering key
questions.
• Understand
impact on As-is
business
processes
• Feedback and
iterate
• Produce required
data models
• Extract,
rationalise and
transform data
for modelling as
and where
required
x weeks
DATA PREPARATION MODEL BUILD
MODEL VALIDATION
INSIGHT AND ACTION
DATA UNDERSTANDING
PRELIMARY ANALYSIS
Identify
Value
Opportunity
STEP 2
Identify Key
Questions
STEP 3
Formulate
Hypotheses
STEP 4
Understand
Data
Sources
STEP 5
Define
Model
STEP 6
Prepare
Data
Sources
STEP 7
Prototype &
Iterate
STEP 8
Pilot &
Execute
STEP 9
A high level approach to agile analytics:
Weeks, not quarters
10Copyright © 2017 Accenture All rights reserved. Subject to contact
Data
Sourcing
Hypothesis Forming Analytics & Modelling
Actionable
Insights

11. TRANSFORMATION TRACK RECORD: OBSERVATIONS
FROM THE FIELD
Unrealistic expectations about technology and data platforms (e.g. Hadoop will replace your EDW)
have hampered the success of data modernisation programmes
Technology has been regarded as a silver bullet to address delivery challenges attributable to
delivery approach and data architecture choices
• Delivery approaches have not been adapted at the pace or scale to meet business
expectations for prototyping and deployment speed
• Continued deployment of rigid, linear data architectures has diluted business engagement
and perceived value from investments in technology
Organisations have under-estimated the skills gap and organisational challenges to rotate to an
analytics and data value driven operating model
Existing data governance challenges have been exacerbated by the increased diversity in data
sources, data types and data platforms

12. 1. Democratization of data: Who are the decision makers in your organization to lead
data exploration and analytics themes to disrupt your market?
2. New data sources: How ready is your organization handle new data sources like
unstructured or external data?
3. Focus on advanced analytics: To which degree are you using your current and
planned analytics to make decisions based on hindsight or foresight?
4. Big data and hybrid Architectures: How many of your data sources can be unlocked
and made available in a data lake?
5. Changing skills requirements: What is your organization strategy to have the skills to
both connect, build, perform and consume analytics?
YOUR MODERNIZATION JOURNEY
QUESTIONS

13. Thank you!
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