DI&A Webinar: Big Data Analytics

The First Step in Information Management
www.firstsanfranciscopartners.com
Produced by:
MONTHLY SERIES
Brought to you in partnership with:
Aug. 3, 2017
Big Data Analytics

Topics for Today’s Analytics Webinar
 New Directions & Trends in Big Data Analytics
− Implications of New Directions
 Differences in Big Data Analytics Architecture
 New Tools for Leveraging More Data Types
 Key Take-Aways
 Q&A
pg 2© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

Polling Question
 What data types are you analyzing?
− Row and column
− Free-form text
− Geospatial
− Images
− Audio
− Video
− All of the above

New Directions & Trends in Big Data Analytics

Big Picture Trends
We are getting
better at Analytics
We are still
expensive
Software and Hardware
need to catch up

Different Questions Being Asked of Data
 Evolving from what to when to why then how?
 Prescriptive and Predictive Analytics are more
commonly adopted
 Graph Analytics shows relationships across
multi-structured data (that are virtually
impossible to see with structured data)
Implications:
− Analytics are Everywhere
− Real-time (Analytics)
 Decreasing Latencies

Big Data is the New Normal
 Big Data has changed the way we view
technology and how we respond to
innovations
 “Big Data” vendors now supporting other
types of data; and vice versa
 Expanding Sources
 Open Data
 Algorithm Marketplaces
 Crowdsourcing
 Consideration of “dark data”
Implications:
− Expanding Sources drive re-evaluation
of data governance concerns
− Driving to self-service Data Preparation
and Data Catalogs that are truly
business created and managed
− Outsourcing: Do non-traditional data
activity, Outsource “Mode 2”
 Additional Concerns: IP protection,
Regulatory, Governance,
Accountability, etc.

Internet of Things (IoT)
 Key component of a digital business
 More data, more complexity and more automation
 Driving spin-off areas of investment like:
− IIOT – Industrial Internet of Things
− IoT Edge Analytics
− Mobile App Edge Analytics
− Event Stream Processing
 Shift to a services-based model, not capital-based
Implications:
− Will drive increased data integration requirements
− Data Privacy and Security is still a big concern as the potential amount of
sensitive data collected can be large

Artificial Intelligence (AI) is Everywhere
 Key component of digital business
 Success is based on the data
 Availability of data and computing
power has fueled AI growth
 Types:
− Machine Learning
− Deep Learning
− Natural Language Processing and
Generation, Conversational AI Platforms
− Computer Vision
“AI is the new
electricity.”
- Andrew Ng
Implications:
− Be aware of how your data is used
− Corroboration of Correlation
− IT organizations are leveraging AI to better manage
their operations and the growth of data

Bots are Hot
 Based on a specific set of predefined rules
 Can be a unique implementation of AI when leveraging algorithms
− Conversational User Interface (Chatbots)
 Can emulate a User or an App
 Bots embedded in applications facilitate workflow
Implications:
− Bots not only use data – they also create it
− Data Privacy and Security need to be considered

Edge Computing
 Faster, more available analytics, even when you’re offline
 Enables the digital enterprise
 Flavors: IoT Edge Analytics and Mobile App Edge Analytics, Intelligent Apps
Implications:
− Architectures need to adapt and stretch to enable edge locations
− Lack of network availability drives requirements for data thinning
and file compression

Differences in Big Data Analytics Architecture

Recap: “Different” Things
Analytics are
everywhere
Big Data is the
new normal
Internet of
Things
AI is
everywhere
Edge
Computing
Bots are hot
 Unified Strategy
 Latency differences
 Storage
 Processing closer to/
within your device
 Integration of capabilities
in multiple areas
 Data obfuscation
New Directions/Trends

FSFP Reference Architecture – Abstract
Data Insight Architecture
1
Data
Movement/
Logistics
Context
Monitoring
Controls
Management Layer
Metadata, Lineage, Work Flow, Models, Reference Data, Rules, Canonical Data
Data Access Layer
Visualization, Prediction, “Closed Loop,” Edge Analytics
Vintage Area
ERP
CRM
Finance
Traditional Data
Collection
Contemporary Area
Edge Processing
Ingestion
Business Strategy
Smart
Machines
Social
Bots
Traditional
Stakeholders

FSFP Reference Architecture – Explicit
Data Insight and Analytics Architecture
1
Data
movement /
logistics
Cross-
generation
Abstraction
Processes
&
Mapping
Vintage Area Contemporary Area
Business Strategy
Vintage
Views
DBMS
Future
Apps
Data
Movement/
Logistics
Cross-
Generation
Abstraction
Processes
&
Mapping
Web
Services
Distributed
Processing
Data
Virtual’n
$
Monetization
EDW
RDBMS
Bot data
Unstr’d
Data
Edge
Vintage
Apps
Management Layer
Metadata, Lineage, Work Flow, Models, Reference Data, Rules, Canonical Data
Data Access Layer
BI/Reporting, Analytics, Mobile
DBMS
ETL
ETL
Data
Lake
DM
IoT

New Tools for Leveraging More Data Types

Vital New Capabilities for Data and Analytics
Source: Gartner, “What Big Data Means Today and How to Position Effectively,” Oct 2016, (High Tech Tuesday Webinar by Terilyn Palanca)

Key Take-Aways
 Analytics will be everywhere.
− Account for it in your architectures and your data
governance and management strategies.
 Take advantage of new technologies and service
providers to expand the use of sophisticated analytics.
 Recognize the skills gap that still exists across the Big
Data and AI spectrum and plan accordingly.
 Privacy will be increasingly important with computing
closer to the individual, including location data.
 “Data Freedom” will require insight enablers, not data
providers.
 Don’t rely on regulations to guide how you think you
should use your data.

Questions?
MONTHLY SERIES

Thank you for dialing in!
Please join us Thursday, Sep. 7 for the next webinar:
“Analytics, Business Intelligence and Data
Science: What's the Progression?”
Kelle O’Neal @kellezoneal
kelle@firstsanfranciscopartners.com

DI&A Webinar: Big Data Analytics

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