This presentation was given by Robin Bloor of the Bloor Group, at the Austin Data Strategy Roadshow hosted by FairCom on January 27, 2016. Robin Bloor goes through how the shifting landscape in technology has changed the way organizations can work with data today. He talks about how the advancements in hardware, software and the growing rate of data is allowing database technology to morph, and organizations to look closely at how to handle this oncoming deluge of data.

1. Data Strategy
Robin Bloor Ph D

2. The Sequence of Topics….
1 The Technology
Landscape
2 The Data Story
3 Database Evolution &
Revolution

3. 1
The Technology Landscape

4. Ch-Ch-Changes
We are going through the most
dramatic changes in IT that have
ever occurred

5. Hardware Disruption/Evolution
 CPUs have become
processor clusters
(parallelism)
 Memory is becoming the
primary store for data.
 Memory is at least 3000x
faster than disk
 SSD is replacing spinning
disk. SSD is now on the
Moore’s Law curve.
These changes are dramatic

6. Consequences
 Most applications were
not built for such
hardware.
 Most database products
no longer align with the
hardware.
 Applications can go far
faster – often that means
they can be improved.

7. Software Disruption/Evolution
 The open source software
and business model
• Adoption by download
• Support licensing
 Parallelism – hence speed
and scalability
 Distributed software
architecture
 Event-based architecture
and real-time software
Software keeping pace with
the hardware

8. Consequences
 The Hadoop ecosystem
(with Spark): Hadoop is a
data OS
 A new software
revolution based on
Hadoop
 The Big Data “explosion”
– really it’s big analytics
 Predictive analytics and
real-time data analytics

9. Business and IT Disruption
 The cloud and cloud
deployment models
(SaaS, PaaS, IaaS)
 The Internet of things
(as opportunity or
threat)
 The forced business
need for Data Science
adoption.

10. Consequences
 Web businesses (Google,
Facebook, Linked-in, etc.)
 A changing of the guard
(sunset on the old guard:
IBM, Oracle, HP, etc.)
 Data-driven businesses
emerging (Uber, AirBnB,
etc.)
 The data/digital economy
 The birth of IoT-driven
businesses (digital
vehicles, etc.)

11. 2
The
Data
Story

12. The Visible “Big Data” Trend
 Corporate data volumes
grow at about 55% per
annum - exponentially
 Data has been growing
at this rate for, maybe,
40 years
 There is nothing new
about big data. It clings
to an established
exponential trend

13. The Growing Data Resource
 Corporate data, supply chain data, web data,
public data, social media data, Log (IoT) data,
data markets, text data sources…
 The sources of data are increasing…

14. Take Note!
You can know more
about a business
from its data than
by any other
means

15. Not Enough Data Scientists
A data scientist is a:
project manager,
statistician, domain
expert, software
expert, data
architect
Or he’s a consultant
So, he’s a consultant

16. The Software Industry Response…
 Automate the role
of the data scientist
with…
 Analytics in the
cloud
 Analytics
platforms
 Machine
Learning
 Vertical analytics
applications

17. Corporate Reality

18. The Data Analysis “Budget”
 Data Analysis is
Business R&D
 The focus is on
business process
 The outcome of
successful R&D is
a changed process
 Think of
manufacturing for
a useful analogy

19. 3. Database: Evolution or Revolution
The Consumerization
of BI

20. Database Landscape
The deluge of data means many
different data structures.
Databases were never built to
handle data structures flexibly.
Hence: many types of database

21. Big Data and the Data Reservoir

22. A Short History of Database

23. Types of Database
 Old: Navigational,
Key-value stores,
Hierarchical,
Relational, Object,
Object-Relational
 Newer: XML, Column
Stores, Document,
Time Series, Graph,
Event Stores,
Multivalue, RDF
 Also: Multimodel

24. In Summary…
1 The Technology
Landscape
2 The Data Story
3 Database Evolution
& Revolution

25. Q&A