Harvesting analytics and AI to reinvent the traditional ... · 1/5 vs traditional approach E.g....
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Harvesting analytics and AI to reinvent the traditional approach to problem solving
Transcript of Harvesting analytics and AI to reinvent the traditional ... · 1/5 vs traditional approach E.g....
Harvesting analytics
and AI to reinvent the
traditional approach to problem solving
Business is
anything but usual
2018: 7 Tech
1091
976
877
839
473
423
388
523
379
370
Apple Inc.
Amazon.com
Microsoft
Alphabet Inc.
Berkshire Hathaway
Alibaba Group
Tencent
JPMorgan Chase
Johnson & Johnson
20 years or less: average
age of S&P 500
companies
Efficiency x 50
Cost per transaction 1/7
Top 10 Largest Public Companies by Market Cap
Over 10 Years ($ bn)
Growth after
transformation
Traditional business
Digital Giants
Gap
What is the underlying problem?
Data & technology
consolidate as a
disruptive business
driver
Distributed vs. monolithic
Single Platform Natives: Distributed IT
age
Traditional Companies: Monolithic age
Technology
Traditional Companies are facing the legacy of monolithic technology
80% x750%Time and cost to move
data around due to a
separation between
operations and
intelligence
Number of FTEs that
are required for data
management through
a legacy state
Less than half the
time are algorithms
implemented in
operations
Tech experts Tech experts
TRYING TO COMPETE IN THE
TECHNOLOGY PLAYGROUND
Traditional
companies
TRADITIONAL BUSINESSES WILL FAIL
Digital Giants
How do we solve this problem?
Business data Fabric is the
future.
01Data-centric platform
02AI-led data management
Introducing 3 disruptions
10x more efficient
90% less FTEs
03
IT for BusinessIT4B
x5 Speed
SAP: ERP
Logistic
s
Stock
CR
M
Call
Cent
er
Big Data LakeDATA
MART
DATA
MART
DATA
WAREHOUSE
TP
V
AP
P
Lost
data
Campaig
n
Manager
Lost
data
Producti
on Line
E-
commerce
Operations
[separated from]
Data Intelligence
PROBLEMS
Data from
operations to
analytics
Intelligence from
analytics to
operations
Problem #1: Data silos and monolithic application centric organizations01 Data-centric
platform
02 AI led data management
A Business data layer is created through automated matching between technical data and business terms
Business terms &ontologies
Use case The benefits of the semantic data layer to deploy AI at scale and reduce time to market and cost
"A top 10 global bank is building a global platform
for trade finance (international factoring &
confirming, payments, FX…etc.)
The Challenge
Total reduction after
5 clients/countries/BU
41M vs 8M
1/5 the cost
Time To Market
Total development cost
1/5
100MClients
Top 10 global Bank
150kEmployees
+50Countries
Operations
[separated from]
Data Intelligence
PROBLEMS
03 IT for BusinessIT4B
Applications decouple from technology thanks to the business data layer
Sector data architecture and business terms standardization
UK
tenant
US
tenant
ITA
tenant
ERP CRM …
IT4 Business
Business experts Business
experts
BUSINESS
PLAYGROUND
Traditional
companies
TRADITIONAL BUSINESSES WIN
From enterprise-
wide to sector- wide
From devOps to devAI
From the technology playground to the business playground
Digital Giants
Longer process
Difficult to maintain
Higher cost
Non scalable and reusable
70’s 90’s CURRENT
Distributed PlatformsInternet Host
Legacy tecnology
Old Expensive Technology High Complexity
High Cost
Monolitic Silos Creation
Little Intelligence
No decomision of tech.
Human based procesess No automation
IT for Business
IT4B
Business Data Fabric
Semantic Services
Business-driven AI
Proactive Governance
Business assets
Business Domain Modeling
Technical legacy IT
The future
Enterprise
Powered by
Technoscience
&
Trusted
Intelligence
BUSINESS DATA FABRIC
Transforming a business through the business data fabric
Regaining control on your
future
Page 17
AI LED DATA
MANAGEMENT
GREENFIELD
Automatization of
Data Governance
and Data
Engineering
Launch of a new
business ventures,
becoming digital
natives by design
Data management
40% to 80% less human
Intervention required
BUSINESS DRIVEN
SOLUTIONS
Accelerate time to
market in building
business solutions
Speed
Productivity
4 uses cases for business transformation
IT
TRANSFORMATION
Enable
decommissioning
roadmap from legacy
to distributed
technology
Time to market
1/5 vs traditional approach
E.g. FinCrime, forecasting
Revenues
margin
Decommission vs
maintain
1.5 years payback
1/7 total cost of operation
Revenues
margin
AI digital native
50x times more efficient
operations
Reinvent
the future
Use case: Industry – RetailData sharing & data trading between Bank & Retailer
• Optimization of future consumer
demand
• Reduces time-costs, and optimizes
marketing investments
Business Driven Solution
Sharing valuable data between consumer products and retailers
Page 19
Water management utility firm based
in a traditional stack (Hosted
applications)
125
years old
Sensor-based (from source to
delivery) of the total pipe network70%
To take advantage of inputs to create
actionable intelligenceInability
SituationUse case: Utilities
Benefits
Transformed from a local utility to a global software provider
Fraud detected
Automatization through AI
Predict consumption
Maximized repair efficiency
9x in ingestion data volume per month
25x in sensor data reading/minute
8.3% increase in sector efficiency
Greenfield
Beatriz Sanz Sáiz
Global Data Analytics Leader
