Roman Gruhn MongoDB Australia June2017

1. The Double Win:
Business Transformation &
in-year ROI and TCO reduction
Roman Gruhn
Director, Information Strategy
roman.gruhn@gmail.com

2. The 3rd Revolution
Source: https://www.artcobell.com/artcobell_blog_evolving_learning_spaces

3. Source: Forrester Source: Deloitte

4. “Software Is Eating The World”
Marc Andreessen

5. $798B, 19th May ‘17 $31B, March ‘17$69B, December ‘16
How can Software drive success?

6. Gartner's 2016 Hype Cycle for Emerging Technologies

7. “Data Is The Currency of the Future”
(unknown)

8. Big Data

9. The 3Vs of “Big Data”

10. Driving Factors for Data Management
Forrester has asked executives: What are the primary issues driving your data?
“(…) the variety of data sources is
seen by our clients as both the greatest
challenge and the greatest opportunity.”*
* From Big Data Executive Summary of 50+ execs from F100, gov orgs; 2014
Data Variety
Diverse sets; data
streaming or new
data types
Data Volume
Greater than 100TB
Other

11. Modern Information Management

12. Modern business requires a modern form of
Information Management
Do you have the right tools?
If all you have is a hammer,
everything will look like a nail.
What is on the horizon for you?
vs

13. Legacy
Legacy systems are falling short
RDBMS systems were not created for today’s requirements and consequently try to bolt-on features to compensate for
the lack of capabilities. But this strategy can’t compete with data systems purpose-built to solve today’s problems.
Rigid Schemas
Resistant to
change
Throughput &
Cost make Scale-
Up Impractical
Relational Model Scale-up
Data changes constantly,
which fits poorly with a
relational model
Scale-Up clusters were
never meant to handle
today’s volumes
Today
Flexible Model
01
10
JSON
Scale-out
Flexible Multi-Structured
Schema that is designed
to adapt to changes
Scale-out to the end of the
world and distribute data
where it needs to be

14. Key Areas of Modern Information Management
Legacy Modern
Data in
Motion
Data at
Rest
Data in
Use
• File batch transfers
• Daily batches
• Cottage industry
• Record streaming transfers
• Continuous
• Urbanized
• Spreadsheet like structured data
• Many technologies to manage
• Centralised scale-up implementations
• Multi-structured
• Single technology data platforms
• Distributed scale-out implementations
• Batch processing / analytics
• Need for “Data accelerator” technologies
• Slow delivery schedules and feature backlogs
• Real-time by default
• Apps connect directly to shared data service
• Continuous Delivery / Improvement / Integration

15. Strategic
SaaS, Mobile, Social
Microservices /
API Access / JSON
Polymorph Data (structured,
semi-structured, unstructured)
Hadoop, Spark
Commodity HW / Cloud
Local Storage / Cloud
Software-Defined Networks
MongoDB and Enterprise IT Strategy
Our technology can help you transform your IT organisation and modernise the entire IT stack
by enabling you leverage strategic solutions on every level to drive business transformation.
Legacy
Apps On-Premise
Data Access
Object-Relational Mapping /
ODBC Access / SOAP
Database Oracle / Microsoft
Data Schemas Relational Data / Structured
Offline Data Teradata
Compute Scale-Up Server
Storage SAN
Network Routers and Switches
MongoDB sits right at the centre
of strategic IT and business
transformation, enabling full stack
modernisation.
By removing layers we can:
• Reduce complexity
• Reduce cost
• Increase business agility
• Improve data & service quality
• Facilitate innovation

16. Success Stories

17. Let our team help you on your journey to efficiently leverage the capabilities of MongoDB,
the database that allows innovators to unleash the power of software and data for giant ideas.
Being successful with MongoDB
We have worked with over 50% of the Fortune 500 companies. While the definition of success metrics
look different for each one of them, 2 key factors are consistent across all of our engagements:
5xProductivity*
We help our customers to increase
overall output, e.g. in terms of
development or ops productivity.
80%Cost reduction*
We help our customers to dramatically lower
their total cost of ownership for data storage and
analytics by up to 80%.
* Dependent on type of implementation

18. Problem Why MongoDB Results
Operational Data Source
Data lake to store data from multiple sources for operations on the data.
ODS is built to store and process read only customer transactions for
business operations, analysis and reporting.
Problem Solution Results
With the advent of mobile banking,
Barclays has experienced a significant
growth of traffic originating from mobile
devices to Mainframe platforms that
supports banking applications. Growth
of traffic, which is expected to
continue, has led to an increased cost
of operations and decreased
performance
Ability to provide high resiliency during
mainframe outages
Existing ETL processes that load transaction
data into Teradata on a daily basis are updated
to additionally feed data to MongoDB paving
the way for decommissioning of Teradata. In
subsequent phases of the project, MongoDB
will be updated in near real-time via a live
transactions feed
De-normalized real time data store using
MongoDB with the benefits to reduce growth
Stand-in capability to support Resiliency during
planned and unplanned outages across
mainframe system and other source systems
Reduced cost of operations
Reduced number of read only transactions to
Mainframes, thereby freeing up mainframe
resources for additional growth
Ready for Open Banking (PSD2) regulation,
enabling the bank to serve transaction data via
API interfaces

19. Bala Chandrasekaran, Director Data Optimisation & Simplification
“This is because MongoDB architecture is scalable and the mainframe isn’t under as much
pressure. The operational database now has over 13 billions transactions held in 114 million
documents”.
Customer Testimonial: Barclays