BDW Chicago 2016 - Chris Gladwin, Founder Cleversafe & Ocient - How Big Is Data? And How Big is Data Going to Be?

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This document discusses the rapid growth of data and the challenges posed by increasingly large data volumes. It notes that data is growing exponentially as technology improves, driven by factors like genome sequencing, medical imaging, video, and sensors in devices. The amount of enterprise data is doubling every two years, and will reach 20 exabytes of structured data stored annually by 2017. However, 90% of this data may never be accessed. Analyzing such enormous datasets, with potential queries in the billions per second, requires advanced computing techniques like machine learning to handle the scale.

Technology

How Big is Data?
And How Big is Data Going to Be?
Chris Gladwin

Top 10 Strategic Technology Trends for 2016
Source: Gartner
Gladwin’s Law:
Better Technology =
More Data
(a LOT more data)

Human Genomes
Medical
Consumer
Research
$10M
In 2006
$3B
In 2003
$1K
In 2014*
3
HD
1K
TV
.5K
UHD
4K
UHD-2
8K
Digital Video
Digital
Video
Phone
Video
Demand is Growth on Growth
* Illumina HiSeq X Ten
Plus Camera Phones, Lidar, Satellites, Microscopes, Seismographs, MRIs, X-Rays,
Telescopes, Radio Telescopes, Spectrum Observatories, Cars, Aircraft Engines, etc.
Network Management
Enterprise
Network
Metadata
2025
3.3 PB
2020
1.0 PB
2015
0.4 PB

Data growth challenge
Enterprise Exabytes Deployed per Year (millions of Terabytes)
0
20
40
60
80
100
120
2009 2010 2011 2012 2013 2014 2015 2016 2017
Unstructured content
Structured data
Source: IDC
Capacity
increase
60–80%
per year
Unstructured
will be
79%
of all new
data

Cloud = Larger Storage Systems
5
Enterprise
Medium Business
Small Business
PCs
Future
Medium
Business
Small Business
PCs
Public Clouds
Enterprise
Private Clouds
Past

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2004 2014 2024
Worldwide Enterprise Storage Deployments
Storage
Capacity
Deployed
Per System
Zettabyte
x 1000
Exabyte
x 1000
Petabyte
x 1000
Terabyte
Source: IDC

Data analysis challenge
Enterprise Exabytes Deployed per Year (millions of Terabytes)
0
20
40
60
80
100
120
2009 2010 2011 2012 2013 2014 2015 2016 2017
Unstructured content
Structured data
Source: IDC
20 Exabytes of
Structured Data
in 2017

Implications
20 Exabytes of Structured Data in 2017
90% is archived and never read
10% of records are read
Up to a minute response on data reads
Average record is 500 bytes
77,000,000,000,000 reads per second!

Data AND Data Analysis Growth
Computers, sensors, and machines
are creating evermore digital data
Trillions of
Records
Billions of
Queries
per Second
Organizations
Need to Analyze
All This Data
Computers, with machine learning, are
required to analyze data at this scale

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BDW17 London - Mick Ridley, Exterion Media & Dale Campbell , TfL - Transformi...

Big Data Week

Hello London, the ground-breaking media partnership between Transport for London (TfL) and Exterion Media, gives new opportunities for brands to talk to the London audience in innovative ways and generates vital revenue for London’s transport network. TfL and Exterion have been working together in the Hello London partnership for a year. Part of the collaboration was around the utilisation of data collected by TfL to better inform advertising investment decisions. This has led to ground-breaking work in the Out-of-Home advertising sector and the first example of this is Taps Segmentation. Developed by the TfL Data Science team, it allows Exterion to understand demographic patterns at stations based on aggregated contactless and Oyster card usage. This de-personalised data can be analysed for different times of the day and is a game changer – allowing Exterion to rethink how both their classic and digital inventory can be packaged and tailored specifically for clients. The presentation will cover how TfL and Exterion have collaborated, the approach used by TfL and how Exterion are using it to generate revenue which is reinvested in the transport network.

BDW17 London - Abed Ajraou - First Utility - Putting Data Science in your Bus...

Big Data Week

Data Science is now well established in our businesses, and everyone considers data as a key asset and critical for our competitiveness. However, Data Science is not easy to manage, very often projects failed and the investment made is not seeing as profitable. The aim of this talk is to share the knowledge in different areas: * avoid classical mistakes in Data Science * use the right Big Data technology * apply the right methodology * make the Data Science team more efficient

BDW17 London - Steve Bradbury - GRSC - Making Sense of the Chaos of Data

Big Data Week

BDW17 London - Andy Boura - Thomson Reuters - Does Big Data Have to Mean Big ...

Big Data Week

The document discusses some of the risks associated with big data, including the risk of data breaches getting more costly as data volumes and repositories increase. It notes that smaller breaches involving 10,000 to 100,000 records on average cost hundreds per record, while mega-breaches of millions of records can cost billions and be in the range of pounds per record. The main sources of risk are identified as user error, system glitches, and attacks, with malicious attacks being the costliest. It provides some recommendations around applying security controls like access management and automation while also considering dependencies and maintaining good data hygiene.

BDW17 London - Tom Woolrich, Financial Times - What Does Big Data Mean for th...

Big Data Week

BDW17 London - Andrew Fryer, Microsoft - Everybody Needs a Bit of Science in ...

Big Data Week

BDW16 London - Alex Bordei, Bigstep - Building Data Labs in the Cloud

Big Data Week

Building Data Labs in the Cloud summarizes how to build data labs in the cloud by connecting on-premise services through VPN or targeted firewalls, integrating identity services between on-premise and cloud realms, enabling single sign-on with two-factor authentication, using encryption with cloud or on-premise HSMs, leveraging Spark for data science, SQL, ETL, machine learning and graph processing, adopting a multi-context architecture for maintenance and efficiency, and ensuring real-time systems provide performance, stability, serviceability and fault tolerance.

BDW16 London - William Vambenepe, Google - 3rd Generation Data Platform

Big Data Week

1. The document discusses Google Cloud's 3rd generation data platform and services for managing large-scale data and analytics workloads. It focuses on managed services that allow users to focus on insights rather than infrastructure maintenance. 2. The platform includes services for data ingestion, processing, storage and analytics including Cloud Pub/Sub, Dataflow, BigQuery, Dataproc, Bigtable and Cloud Storage. It aims to provide a serverless platform with auto-optimized usage and pay per use pricing model. 3. Over 15 years Google has developed technologies for tackling big data problems including papers, open source projects and cloud products. Core components of their data platform are discussed including the Beam programming model and Dataflow for unified

BDW16 London - Scott Krueger, skyscanner - Does More Data Mean Better Decisio...

Big Data Week

We have seen vast improvements to data collection, storage, processing and transport in recent years. An increasing number of networked devices are emitting data and all of us are preparing to handle this wave of valuable data. Have we, as data professionals, been too focused on the technical challenges and analytical results? What about the data quality? Are we confident about it? How can we be sure we are making good decisions? We need to revisit methods of assessing data quality on our modernized data platforms. The quality of our decision making depends on it.

BDW16 London - Nondas Sourlas, Bupa - Big Data in Healthcare

Big Data Week

The document discusses Bupa's use of analytics in healthcare, including risk modelling and care management, and referral management. For risk modelling and care management, Bupa uses predictive modelling to identify high-risk patients for targeted outreach programs, which have led to reductions in outpatient visits, tests, and surgical procedures, saving 9-10% in care costs. For referral management, Bupa profiles over 18,000 consultants based on claims data to guide over 700,000 pre-authorizations, achieving estimated healthcare savings of 9-11% of guided spend.

BDW16 London - John Callan, Boxever - Data and Analytics - The Fuel Your Bran...

Big Data Week

Unsuccessful marketing campaigns are leaving customers disgruntled, making them 40% less likely to return. Companies are casting aside useful data that can provide further insights into better products/better connections with customers. John Callan, VP of Marketing at Boxever will discuss how AI can change how businesses predict trends, reduce risks, and improve efficiency. Audience will: Gain expert-level understanding of data and machine learning that’s used in today’s market Identify successful ways companies use machine learning to target customers with personalized content Learn from major airlines use-cases to skillfully target customers and show them exactly what they want to see.

BDW16 London - John Belchamber, Telefonica - New Data, New Strategies, New Op...

Big Data Week

Through the experiences of supporting a Multi-Country roll out using data to drive more effective Network capability, we will explain how we have: Created new internal capability to support local countries, developed skill sets in the country and provided technical infrastucture, algorithms and visualisations to drive the data culture and big data strategies across Telefonica business units. Through this framework, we will explain how to blend technical and business needs to maximise the benefits and drive better business performance.

BDW16 London - Deenar Toraskar, Think Reactive - Fast Data Key to Efficient C...

Big Data Week

The Basel Committee on Banking Supervision (BCBS) and local regulators has been focussed on making banks more safe and resilient. A whole raft of new capital charges and constraints on liquidity and leverage have been introduced: Basel II.5, Basel III, Dodd-Frank, FRTB (“Basel IV”), etc. These have significantly increased the risk data management capabilities banks must have—capabilities that only big data tools can provide. This talk will cover the challenges of building a position-aware risk management platform that properly aggregates all intra-day trading activity, monitors exposures and risk. The fast data stack can help banks create such a platform and provide a robust foundation to achieve compliance and, ultimately a significant competitive edge by making efficient use of capital.

BDW16 London - Jonny Voon, Innovate UK - Smart Cities and the Buzz Word Bingo

Big Data Week

BDW16 London - Marius Boeru, Bigstep - How to Automate Big Data with Ansible

Big Data Week

BDW16 London - Josh Partridge, Shazam - How Labels, Radio Stations and Brand...

Big Data Week

“At Shazam, we think data can be beautiful and stunningly inspiring. The pictures we paint with our data tell stories about changing culture, tastes, and shared discoveries. A truly great new song can sweep across the globe in a wave of Shazams that transcends politics, language, or religion”, Greg Glanday, Chief Revenue Officer at Shazam. This presentation will offer the audience a few examples of how they can use the data from Shazam to get fantastic insight into the consumers` preferences, and how to take that insight and apply it to a brand. Giving 3 or 4 great examples of what we do at Shazam, anyone in the audience can understand what this data means, really see this data and then be able to leverage it to make smart marketing decisions. Main takeaway: a clear understanding of what Shazam data is and how brands can use it.

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