Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?

Self-Service Big Data and AI/ML
Reality or Myth?
Janet Jaiswal, VP of Product Marketing at SnapLogic, Inc.
August 23, 2018

2
Agenda
The Rise of the Data-Driven Enterprise
& Empowered user
Big Data, Big Problems!
Self-Service ML: Reality or Myth?
Demo and Key Takeaways
Resources

corporate overview
Rise of the Data-Driven
Organization & the
Empowered User

Most companies are undergoing Digital Transformation
but technology is holding them back
4
Source: Vanson Bourne
Legacy databases: The culprit behind the data dilemma
©2018 SnapLogic, Inc. All Rights Reserved

Employees are increasingly demanding self-service
yet significant barriers remain
Confidential Content©2018 SnapLogic, Inc. All Rights Reserved
Source: TDWI, https://tdwi.org/articles/2017/10/23/bi-all-data-driven-focus-on-user-empowerment.aspx
What are the most significant barriers to increasing users’ self-reliance and
reducing their dependencies on IT for BI, analytics and data preparation?
5

corporate overview
Big Data, Big Problems!

The data value disconnect
Source: 2018 Survey of 500 IT and business users across US & UK conducted by Vanson Bourne
80%
believe legacy
technology is holding
their organization back
from taking advantage
of data-driven
opportunities
51%
organizations use only
half of the data they
collect or generate
29%
have complete trust in
their organizations’
data when it comes to
making business-
critical decisions
©2018 SnapLogic, Inc. All Rights Reserved Confidential Content
7

8
Data lakes remain an empty promise...
#1 reason for failure: Talent shortage
90%
of Data Lake projects
are delayed and over
budget
60%
completely fail...
Source: Gartner Survey Analysis: Traditional Approaches Dominate Data and Analytics Initiatives, October 13, 2017

Key challenges that are slowing adoption
Confidential Content
1. Lack of specialized developer & data science skills
◦ Citizen Data Scientists - Scenario modeling and forecasting
◦ Skilled Data Scientists - Develop new algorithms and models (extremely large datasets)
and augment ML frameworks to create models
2. Lack of useful data
◦ Integrate a variety of endpoints to obtain relevant, quality data
◦ Prepare the data in useful format
◦ Create and manage big data (manage high costs due to size of data & skills gap)
A need for a no-code paradigm
◦ Employee empowerment means users of all skillset must be able to access data and
perform their own analysis
9

SnapLogic eXtreme: What is it?
SnapLogic eXtreme extends the SnapLogic
Integration platform
to provide a serverless, cloud-based runtime
environment
for
complex & high-volume data transformation
routines
servicing various big data use-cases
at elastic scale

Customer’s journey to the cloud for big data projects
• Big Data historically started on-premise
• High CapEx, High OpEx, Skill-set gap
• Sizing for peak loads
• Move to cloud for infrastructure savings
• Eliminate CapEx; Take advantage of IaaS,
High OpEx, skills-set gap remains
• Still sizing for peak loads
• Move to fully managed data architecture to
reduce complexity
• Dramatically reduces OpEx and skills-set gap
• Provides Elastic scale
11

EDW
Data Mart
Data Mart
Data Mart
The evolution of enterprise data architecture
S3, ADLS, GCS
SnapLogic eXtreme
EDWData
Lake
Push
Data Mart
Data Mart
Data Mart
Data Science
Workbench
Social Media
IoT
Database
SaaS App
File
Pull
Push
Stream
Big Data as a Service
(AWS, Azure, GCP)
1990’s & 2000’s
Batch
Today
Batch + Streaming
One Integrated
Platform
(SnapLogic EIC)

SnapLogic eXtreme: Key benefits
Improved Productivity
across Developers,
Big Data Architects
and Administrators
• Requires no special
data engineering skills
• Visual Spark pipeline
development with no
code
Improved TCO (Total
Cost of Ownership) for
Data Lake
Implementations
• Unified “Elastic Scale”
platform integrated with
Enterprise Integration
Cloud
• Fully-automated,
managed cloud-based
big data runtime
environment
13
• Do more with less
resources, improve
time to market, faster
time to insights
• Speed up data-driven
big data project
implementations
Improved
Business Agility
Enables ML-based
Analytics
• ML model building for
prescriptive and
predictive analytics
requires a large amount
of data

corporate overview
Self-Service ML:
Reality or Myth?

More and more companies are becoming data-driven
15
Source: https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848
“By 2021, insights-driven business will steal $1.8 trillion a
year in revenue from competitors that are not insights-
driven.”
15

ML is a key part of many organization’s plans
16
Sources: McKinsey Global Institute and Google
of respondents are exploring use cases for Machine Learning
16
of executives believe their organization’s future
success depends on the successful implementation
of Machine Learning
48%
60%

However, organizations have a lot of analysts but not
enough Data Scientists
17
McKinsey Global Institute predicts that the US economy
will be short 250,000 data scientists by 2024.
Source: McKinsey Global Institute “The Age of Analytics: Competing In A Data-Driven World.”
17

Why the shortage?
So they can satisfy the needs of…..Organizations are competing with a few elite
companies for talent
18

Machine Learning stages
Data
Collection
Collect and prepare data
Data
Preparation
Make sense of data
ML Model
Training &
Testing
Use data to answer questions
Model
Deployment
Deploy and operationalize models
70-80% of effort
19

SnapLogic EIC + ML: End-to-end data science
• Clean Missing Values
• Type
• Categorical to Numeric
• Numeric to Categorical
• Scale
• Shuffle
• Sample
• Date Time Extractor
Data Preparation
ML Data Prep Snap Pack ML Core Snap Pack
ML Analytics
• Classification Trainer
• Classification Cross Validator
• Classification Predictor
• Regression Trainer
• Regression Cross Validator
• Regression Predictor
• Remote Python Script
• Jupyter Notebook Integration
• Profile
• Type Inspector
ML Deployment
• Ultra (Real-Time) Pipeline
for hosting ML Models as
REST API
Model Deployment
20
Data Collection
• Connect
anything
(applications,
data
warehouses, IoT,
APIs and
processes)
• Any deployment
mode (on-
premises, cloud ,
hybrid)
• Any speed
(streaming,
batch, event-
driven, real-time)
Model Training &
Testing
SnapLogic EIC SnapLogic EIC
SnapLogic ML

corporate overview
Product Demonstration

Case Study: Iris AI’s Integration Assistant
22
Task
Integration Assistant
Conventional Approach
Integration Assistant
With SnapLogic ML
Data Acquisition 200 Lines Of Code
(LOC)
1 Days 10 LOC + 11 Snaps 20 Mins
Data Preparation 350 LOC 2 Days 150 LOC + 32 Snaps 3 Hours
Model Development 50 LOC 1 Days 0 LOC + 8 Snaps 1 Days
ML API Deployment 200 LOC 3 Days 0 LOC + 7 Snaps 20 Mins
Continuous Learning 200 LOC 3 Days 0 LOC + 0 Snap 20 Mins
Total 1000 LOC 10 Days 160 LOC + 58 Snaps 1 Days 4 Hours
22

Benefits of SnapLogic ML
No need for
specialized skillsets
• In some cases, no
coding needed
• BYOML - Bring your
own ML to the Native
Python Snap
• Operationalize model
training & deployment
Access to multiple
data sources
• Current ML products
do not offer integration
plus operationalization
Data security
• No need to send data
to cloud service for
training
• Keep you training data
completely private
ML Algorithm
accuracy
• Better accuracy with
our ML algorithms
compared to cloud
services
23

SnapLogic Machine Learning Showcase
Go to: labs.snaplogic.com
24

Demos available at SnapLogic ML Lab Showcase
25

corporate overview
Key Takeaways

Digital Transformation requires that organizations
become data driven
SnapLogic
Enterprise Integration Cloud
(EIC)
SnapLogic
eXtreme
SnapLogic
ML
Connects the entire organization
Simplifies at scale data processing
The only unified Cloud-agnostic platform.
It does not matter where the data sits, lives or breathes!
Simplifies data science with AI/ML Snaps
27

Key steps to
leveraging Big
Data and AI/ML
Use SnapLogic EIC to Make ALL Data
Sources Easy to Access#1
Make Big Data Fast to Deploy and Easy to
Manage with SnapLogic eXtreme#2
Make ML Tools Easy to Use and Accessible
for all Types of Users with SnapLogic ML#3
Empowers Users with SnapLogic EIC’s Self-
Service Capabilities to Alleviate IT Bottlenecks#4
28
Summary:
Key steps to
leveraging
Big Data and
AI/ML

Resources
30
• SnapLogic Integration Buyer's Guide
• Modern Enterprise Data Architecture White Paper
• Extending the Value of Microsoft Dynamics CRM White Paper
• SnapLogic vs. MuleSoft Comparison guide
30

Thank You
San Mateo, CA
New York, NY
London, UK
Hyderabad, India
Janet A. Jaiswal
VP of Product Marketing
Jjaiswal@snaplogic.com
www.snaplogic.com
Connect with SnapLogic via our
blog, Twitter, Facebook, or LinkedIn

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