Read More Greenside US Tour 2016 Report Silicon Valley Disruption - Keeping ahead though ...

111 Pages·2016·12.83 MB·English

by  Andy Langley

US Research Tour 2016

Table of Contents

Introduction ................................................................................................................. 3
Executive Summary ...................................................................................................... 4
Trends ....................................................................................................................... 6
Fast forward 2017… .................................................................................................. 7
Data .......................................................................................................................... 8
The Innovative Enterprise ...................................................................................... 11
Security ................................................................................................................... 15
Actifio ......................................................................................................................... 18
Airware ....................................................................................................................... 21
Arundo ....................................................................................................................... 23
Black Duck .................................................................................................................. 26
CaféX .......................................................................................................................... 28
Cazena ........................................................................................................................ 31
Crossdeck ................................................................................................................... 34
Databricks ................................................................................................................... 36
Datameer ................................................................................................................... 38
DeltaID ........................................................................................................................ 40
Exabeam ..................................................................................................................... 43
GigaSpaces ................................................................................................................. 46
Gigster ........................................................................................................................ 49
Gigya ........................................................................................................................... 51
GoodData ................................................................................................................... 54
Joyent ......................................................................................................................... 56

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US Research Tour 2016

Lookout ...................................................................................................................... 60
MarkLogic ................................................................................................................... 62
Menlo Security ........................................................................................................... 65
Mesosphere ............................................................................................................... 68
Microsoft .................................................................................................................... 71
Oblong ........................................................................................................................ 75
Okta ............................................................................................................................ 77
Orbital Insight ............................................................................................................. 79
Origami Logic .............................................................................................................. 81
PinDrop ...................................................................................................................... 83
Perlert ......................................................................................................................... 86
Skysafe ....................................................................................................................... 90
Splunk ......................................................................................................................... 93
Springpath inc. ........................................................................................................... 96
SS8 .............................................................................................................................. 98
Stack Overflow ......................................................................................................... 101
Striim ........................................................................................................................ 104
Synapse.ai................................................................................................................. 107
Talena ....................................................................................................................... 109

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US Research Tour 2016

Introduction
This was our 20th annual Research Tour for Linda and I and our second as Ntegra
Greenside. It was undoubtedly one of the best. It is hard for us to believe that the
Tour has survived and prospered for so long and that it goes from strength to strength.
Back in 1996, we managed to see around a dozen companies during the week but this
year it had grown to almost 40, many of which were mind-blowing.
To be able to see so many remarkable companies in such a short period of time is both
stimulating and challenging. The speed of change continues to accelerate at a
frightening rate and the Valley is driving disruption throughout the business world. All
companies need to look at the Valley to see whence new competition might emerge.
The Tour is like a shot of adrenalin to both the delegates and to us. The feedback from
the companies we saw about the quality of questions and understanding are a great
credit to those who joined us on the Tour.
We would like to acknowledge the contributions made by all the organisations that
helped us to put the Tour together and especially to our five host companies –
Microsoft, Andreessen Horowitz, Splunk, Intel Capital and Sequoia Capital; to all those
who presented to us; to those who helped us put the Tour together and most of all to
the delegates who made it such a stimulating experience.
We eagerly look forward to next year’s Tour.
Mike Portlock
Chairman
Ntegra Greenside

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US Research Tour 2016

Executive Summary
In May 2016, a party of eighteen senior executives from the UK spent a week in Silicon
Valley, California, visiting a selection of IT companies on the 20th Ntegra Greenside
Annual Silicon Valley Research Tour.
The purpose of the tour was to learn companies we saw: mobile everything,
about the latest developments in the the maturing of software as a service
deployment and application of IT, (SaaS) and enterprise readiness to
focussing on how these are being adopt the Cloud. Whilst some of the
exploited by major organisations for themes remain consistent, it was the
business advantage. We received speed with which they are maturing
presentations from 39 different that was truly impressive.
companies of varying size and
In the last few years we have seen the
maturity, all of which were actively
rise of the ‘post Blackberry’
involved in providing products and
Smartphone within the enterprise,
services to help organisations become
with many Silicon Valley companies
more effective in their use of
having a ‘mobile first’ mindset, a view
technology. In addition, we received
which has transformed how we
presentations from our three host
interact with a broad range of services
Venture Capital (VC) firms: Intel
both within and outside the enterprise.
Capital, Andreessen Horowitz and
Smartphones have become the default
Sequoia Capital.
end-user device as they are easy to use
Tech companies continue to attract and can be used almost everywhere,
unprecedented levels of investment. thanks to ubiquitous high-speed
As in previous years, there were some wireless connectivity.
strong recurrent themes across the

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Whilst the Smartphone enabled software. New tools will enable
enterprise remains important, we also everybody to be a great coder, so it is
see the increasing range of sensors, anticipated that designers will be king
robots and other ‘Internet of Things’ for the next ten years.
(IoT) devices providing new and novel
Data virtualisation and copy data
ways of interacting with services. It is
management solutions are also
likely that these will gain traction in the
becoming important because, although
home first, but the blurring of our lives
the old adage ‘storage is cheap’ still
between work and home will ensure
holds true, housekeeping multiple
these new ‘interfaces’ impact on the
copies of large datasets is very
enterprise as well. IoT was referenced
expensive.
by a number of our presenters,
Applying a schema or plan to data
particularly as a catalyst for big data
when it is inserted into a database to
becoming even bigger.
enforce consistency is becoming less
The trend for demystifying big data
popular. ‘Schema on Read’ was one of
continued this year, with many
the buzz-phrases of this year’s tour.
presenters talking about the
This innovative analytical strategy
democratisation of big data, whereby
involves applying the plan or schema to
everybody can be a data analyst. The
data as it comes out of the database,
challenges of managing data were
rather than when it goes in. This
discussed, both in terms of storage and
approach allows for a much more
access. A number of the start-ups and
versatile organisation of data and
more established companies have
enables differing views of the same
been concentrating on developing
data to be (virtually) present.
products, services and add-ons that
UAVs (Unmanned Aerial Vehicles, or
enable business users to more easily
drones) featured on the tour for the
‘wrangle’ their data. A key enabler is
first time in 2015. This year we saw
provision of convenient, automated
further evidence of the growth and
tools which translate, format, move
speed with which this market is
data to and from the cloud, and
maturing. UAVs have become yet
generally ease the previously onerous
another sensor platform providing
task of managing huge volumes of
huge volumes of data for driving
information (even before the analytic
analytics and insights. AI and machine
queries have been run).
learning advances mean new analytical
A similar paradigm exists for software.
techniques can be applied to this data,
Our VCs predicted that in 2016 and
which also facilitates unlocking
beyond, we should expect a rise of
potential in historic data.
designers ahead of coders; for the last
A great example of the innovative use
ten years only coders built great
of remote sensor data was Orbital

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Insights, who have combined advanced when adopting new technology,
analysis via a mix of AI, machine particularly from ‘early to market’
learning and data science to drive a companies. We delved into security
range of applications exploiting digital and big data in greater depth, given
imagery. For example, they use their seismic impact on the enterprise.
satellite images to measure car park What are the key messages or
occupancy at Walmart stores and use ‘takeaways’ for us this year?
this information as a proxy for footfall • Enterprises need to have a clear
and business performance. They can strategy to work with emerging
measure and report on this data, technology companies. Trying to
almost in real-time, way ahead of depend on traditional governance,
traditional approaches. procurement and project processes
will not work. The enterprise needs
As in previous years, security was again
a ‘Red Door’ that shows companies
a key topic. Threat landscapes are
the way in. Process, governance
constantly changing and becoming
and culture need to be closely
ever more sophisticated. Prevention
aligned.
alone is futile, with hackers becoming
• We should now regard the ‘cloud’
more elusive and networks already
as safe for sensitive data and apps.
compromised. Several start-up security
This does not remove the need to
firms asserted that enterprises should
ensure that solutions align with
simply assume that they have already
security architecture, end to end.
been breached, but they just don’t
However, saying no to a service
know about it, yet! 72% of CEOs
simply because it is cloud-based
surveyed by KPMG in 2016 cited cyber
could mean you lose out to your
risk as their top business risk and felt
competitors.
there was more work to be done to
• Finding and retaining staff will be as
protect their organisation. Security by
hard as it ever was. Think about
obscurity is no defence, but no one
building ecosystems for innovation
vendor can provide the protection that
and always be mindful that “you do
is required. A multi-layered, multi-
not employ all of the smart people”
vendor security architecture was
(Mark Zawacki on our tour in 2014).
deemed necessary. Enterprises need
Working more in the open (think
to invest in ‘Red Teaming’ and similar
‘open source for people’) will be
techniques to stress test their
how successful companies grow
architecture and rehearse their
and develop.
countermeasures and responses.
• Agility is critical for success.
Trends
Enterprises need to increase focus
This year’s tour has reflected on some on services based IT architectures,
of the challenges that enterprises face APIs and micro services.

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• Operational excellence is as generation of big data growth.
important as it always was. Cloud • Open source will become standard
services mean that software asset for infrastructure software, with
management is increasingly CIOs adopting an open-source,
important, as is data sprawl and software-first approach. Some
sovereignty. open source providers such
MongoDB and DataStax (who we
Fast forward 2017…
have seen on previous tours) have
What are some of the trends that we
built monetization models whilst
expect will develop over the remainder
many others have millions of
of 2016 and through 2017?
downloads, but no revenue. We
• The disintermediation of data expect more providers to find ways
scientists from data science and to monetize their offers.
coders from coding. Tools we saw • India is now the second-largest
this year, such as Datameer, are
market in terms of connected
helping everybody become a great
users. 353 million, ahead of the US,
data analyst or scientist. Similar
and second only to China. This
developments will happen in
market will be strategically
coding, where anybody will be able
significant to US-based enterprise
to write great code, so the value
software companies. Cloud-based
and demand for designers will
services are particularly appealing
increase. Increasing processing
for high growth Indian companies,
speeds coupled with infrastructure
as they do not have the same
growth nearing ‘critical mass’ will
investment in legacy infrastructure,
accelerate the use of neural
thus simplifying implementation
network based Machine Learning,
and operations management. This
Deep Learning and true Artificial
reflects mobile adoption in India,
Intelligence.
where rapid growth and lack of
• Fast data is as important as Big legacy enabled them to leapfrog a
Data. As enterprises more fully whole generation of technology (in
understand big data and start to this case wide scale deployment of
use it to drive value, the velocity fixed line networks). Bypassing
bar will be set ever higher. The entire generations of technology
enterprise must be able to rapidly can be a major catalyst for
process large datasets and analyse achieving stellar growth.
at pace to drive competitive action.
Planning is already underway for our
Technologies such as Spark, Kafka
2017 tour, with many of these themes
and Streaming will see widespread
and topics expected to play a major
adoption, driving near-real-time
role. I hope to see you in 2017!
analytics, and fuelling the next
Andy Jefferies

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Data
Last year's research tour identified a number of interesting trends and developments
associated with the management, manipulation and exploitation of huge volumes of
corporate and social data. As predicted, the year proved to be an important one with,
what had been considered just hype, becoming increasingly normal as organisations
realised that data, big and small, is critical to ensuring best business outcomes.
Throughout 2015/16 the continued, compound effect of Moore’s Law coupled with
improvements in compute, storage and network infrastructure capacity has enabled
innovators to accelerate improvements in data processing techniques and
capabilities. The convergence of these improved processing techniques and
infrastructure capacity nearing ‘critical mass’ will permit new paradigms in Machine
Learning, Deep Learning and Artificial Intelligence to become ubiquitous
This year, the companies we have seen capabilities are now becoming key
have continued to drive rapid growth enablers and differentiators within the
of systems that support non-relational eco-system of emerging big data
or semi-/un-structured forms of data, technologies, eliminating barriers to
as well as increasing their analytic enterprise adoption.
capabilities at massive scale and speed.
As well as working to provide
Established big data and analytics
enterprise class systems, companies
solutions are evolving and maturing to
are configuring their solutions to
integrate well and align with enterprise
enable both business users and data
IT standards. A number of the start-ups
scientists to fully realise the value of
presenting this year have come about
their data assets. There is growing
specifically to address the gaps in
demand from business users to have
newer technologies that make them
the same self-service access to insights
difficult to integrate with existing
that they get from traditional data
business systems and operating
warehouse environments. Companies
models. This trend is likely to persist as
such as Datameer are blurring the lines
the rate of development of new raw
between traditional BI/MI and big data,
capabilities increases. Big data start-
enabling business users to discover
ups are contributing, building and
insights in any data via wizard-based
integrating components such as
iterative ‘point & click’ analytics and
security, authentication, fine-grained
‘drag & drop’ visualisations, regardless
role based authorisation and business
of the data type, size, or source.
continuity capabilities that customers
Companies are rapidly negating the
expect from traditional enterprise
need for “swarms of experts in white
relational database management
lab-coats” to be continually nursing
systems. These, previously overlooked,

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corporate big data solutions. Self- collect, analyse and exploit data,
service, self-discovery and automated allowing users to quickly and easily see
commentary (describing why insights the impact of changes made on
and analytic results have come about) performance. This leads to a more
are what’s expected by business users human approach to performance
today. improvement based on feedback loops
and proven success.
Business users also want to reduce the
time and complexity of preparing data Adoption of NoSQL technologies and a
for analysis, when dealing with a preference for storing data in
variety of data types and formats. We unstructured schema-less form (where
saw a number of companies, such as data is applied to a plan or schema as
MarkLogic and GigaSpaces focusing on it’s being pulled out of storage, rather
end user data preparation. Their than when it is written) were common
customers can also dynamically themes. NoSQL and ‘schema-on-read’
asynchronously scale up or down the databases are becoming an established
amount of storage and compute part of the enterprise landscape as the
resources in the databases relative to benefits of schema-less database
the larger amounts of information concepts become more recognised and
stored in "data lakes". Storage of data understood. The Gartner Magic
is comparatively cheap compared to Quadrant for Operational Database
the cost of the compute resource Management Systems was previously
needed to process it, so it makes dominated by the likes of Oracle, IBM,
perfect sense to use the elastic Microsoft and SAP. Now, however,
provision of resources in the cloud to NoSQL companies, like MarkLogic,
ensure that compute is only paid for outnumber the traditional database
when it’s actually being used. The vendors in the leaders’ quadrant.
effort to ‘humanise’ IT, enabling people
Growth in the massively parallel
to intuitively interact with systems, as
processing (MPP) data warehouse
opposed to systems asserting
segment has been slowing recently and
behaviours that enable interaction, is
the "death of the data warehouse" has
increasing. A number of companies
been predicted. However, companies
demonstrated how data security can
such as Cazena are driving a resurgence
be moved into the background using
in the popularity and use of this
biometrics and new algorithms to
technology in the cloud. Their solution
enable faster, non-intrusive
provides self-service orchestration of
authentication and authorisation.
cloud infrastructure in Amazon AWS
Companies like GoodData are RedShift and Microsoft Azure SQL Data
providing both the tools and the Warehouse. Cazena uses these
expertise needed for organisations to environments to provide, what it calls,

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Data Mart as a Service (DMaaS) growth in the cloud. Established
alongside Data Lake as a Service companies such as Google, Amazon
(DLaaS) configurations on Hadoop and Web Services and Microsoft are
other schema-less databases. This developing IoT services to enable data
enables on demand provision of data to move seamlessly to their cloud
processing and analytics platforms, based analytics engines. Services that
with other technologies including ease the pain of wrangling and
Google BigQuery likely to be included conveniently storing this data, are
soon, giving customers seamless access enabling companies like Arundo to
to best of breed workload engines and develop predictive solutions that raise
heterogeneous infrastructures which asset utilisation and performance in
collectively provide the ‘best of all industrial companies. They do this by
worlds’. Something most enterprises combining sensor and transactional
would simply not contemplate on data with deep domain knowledge and
premise. experience to reduce maintenance
costs and avoid unexpected outages
Apache Spark has rapidly become the
using machine learning techniques.
big data platform of choice for an
increasing number of large enterprises. Using machine learning algorithms to
DataBricks co-founder and Spark enable systems to learn how humans
originator, Matei Zaharia, has seen it work (not the other way around) helps
grow, from a small component of the to spot patterns and connections
Hadoop ecosystem, to provide between activities and performance,
significantly improved data processing leading the way to more innate
speeds and become the largest current decision support and ultimately
big data open source project. It will be autonomous decision making
interesting to see if Spark retains its capabilities. Driverless cars, drones and
“poster child” status in the months to robotics are pushing back the
come. boundaries of what’s possible in this
domain. Developing technologies so
There was similar attention being paid
that interaction is driven by human
to Kafka, a fast distributed publish and
preferences and needs, rather than
subscribe messaging mechanism. This
technology capabilities, will come to
is undoubtedly one to watch for next
the forefront during the next year. IT
year, as it will impact the way new
literacy may not be a differentiator for
solutions can handle streaming data
individuals soon, but human literacy
elastically with resilience in the cloud.
and understanding will become an
It looks like a key enabler for managing
essential component of near-future
and processing data from the IoT.
systems.
Data volumes from devices in the IoT
Adrian Fern
are a further driver for petabyte scale

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