The document discusses how enterprises are progressing with digital transformation and data analytics. It notes that while most want to be data-driven, many are not there yet. A graph shows 29% of enterprises have made great progress measuring value from data, while 27% collect more data but take less action. The document also discusses different decision making approaches, from relying on the highest paid person's opinion to using data and analytics. It outlines the evolution of analytics capabilities, from basic reporting to autonomous decisions. Finally, it discusses data architecture strategies like data warehouses, data lakes, and hybrid approaches.

1. STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph
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STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph
How will Digital Transformation transform all of us
The evolution of Big
Data Analytics
In Enterprises
Einat Shimoni
EVP
STKI

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STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph
Data is the new Oil

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STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph
Everyone wants to be Data-driven
Acting based on insights
Extracting insights
Gathering & managing dataData
Insight
Action
Information
Knowledge
Wisdom

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But most of us aren’t

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STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph
Data & Analytics Status
(a CX-focused view)
29%
Great progress in
measuring value
27%
More data
collection,
less action
23%
Working on
the customer
view
14%
Stuck! No
main
advances
5%
Data is at the
core of all
engagements
Source: Forrester

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STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph
The HiPPO:
The most common decision making style
HiPPO:
Highest
Paid
Person’s
Opinion

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STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph

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STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph
Versus the “Geek” Decision Making Style

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Andrew McAfee
Digital Economy Conference 2017: Opening Keynote:
https://www.youtube.com/watch?v=WyYaubtZuX8&feature=youtu.be

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The Evolution of Analytics
1 2 3 4
Classic
BI/ DW
Data discovery
Analysis
Predictive
Analytics
Augmenting
Intelligence, ML
5
Autonomous
Decisions
“What happened?” “Why?” “What will happen?” “Help me to make
decisions”
“Make the
decision for me”
35% use 20% use60% use90% use ~5%?

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Source: Forrester AI Techradar Q12017

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Einat Shimoni’s work Copyright@2017. Do not remove source or attribution from any slide, graph or portion of graph
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Data Tracks
Data Strategy
Plan
Data
Analytics
Innovation
Data innovation

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Einat Shimoni’s work Copyright@2017. Do not remove source or attribution from any slide, graph or portion of graph
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Plan your
Data StrategyDefine the data
architecture
Plan ways to get and
prioritize use cases
Set data principals:
Find the Balance between “single version
of the truth” and democratization of
data

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DW aren’t suitable
for streaming data,
real time analytics,
large volumes of
messy/complex
data, ad hoc
requirements.
Data Lakes aren’t
suitable for structured
reporting, they lack
maturity, sometimes
security and
integration. They
require a lot of data
preparation work.
DW Data Lake

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Source: KDNuggets

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Nice fluffy buzzwords
Information Fabric (Forrester)
Logical Data Warehouse (Gartner)
Data Reservoir (IBM)

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The LDW Architecture
Source: R20 Consultancy

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Everyone wants to be
data-driven
But there’s still a long way to go.