The connected data imperative: Why graphs

Presentations & Public Speaking

A presentation given at Neo4j GraphDay Chicago

Utpal Bhatt
VP Global Marketing, Neo4j
utpal.bhatt@neotechnology.com
@bhatt_utpal
#neo4j

09:00-09:30
09:30-10:15
10:15-11:00
11:00-11:30
11:30-12:30
12:30-13:30
13:30-17:00
Breakfast and Registration
The Connected Data Imperative: Why
Graphs
Transform Your Data: A Worked Example
Break
Enterprise Ready: A Look at
Neo4j in Production
Lunch
Hands-On Training Session
Agenda

The Connected Data
Imperative: Why Graphs? 
Neo4j GraphDay, Chicago
June 2017

• World’s leading (and ﬁrst) graph-database
• 150+ employees
• 50% in USA, 50% in EMEA
• HQ in San Mateo, CA, Engineering HQ in
Malmö, Sweden
• VC-ﬁnanced by leading investors incl Fidelity
• Average YoY growth since 2011 > 100% 
• Our core belief: connections in data are as
important as the data itself
San Mateo, CA
(Head Quarters)
New York City
North America
Neo4j Today:
Sydney
Asia / Paciﬁc
London
München
Malmö
Europe & Middle East
Stockholm
The World’s Leading Graph Database

ACCOUNT
HAS
REGISTERED
ADDRESS
PERSON
IS_OFFICER_OF
PERSON
NAME
STREET
BANK
WITH
LIVES_AT
LIVES_AT
NAME
COMPANY
BANK
BAHAMAS
2.6 TB
11,5 million documents
Emails, Scanned Documents,
Bank Statements etc…

2.6 TB
11,5 million documents
Emails, Scanned Documents,
Bank Statements etc… Person B
Bank US
Account
Person A
Company
Bank
Bahamas
Address
HAS
REGISTERED
IS_OFFICER_OF
WITH
LIVES_AT
LIVES_AT
NODE
RELATIONSHIP

2.6 TB
11,5 million documents
Emails, Scanned Documents,
Bank Statements etc…

“Lessons Learned Database”
A half-century of collective NASA engineering knowledge

“Lessons Learned”
Database
GRAPH DB
Load Data

Let’s Hear a Few Stories
— David Meza, Chief Knowledge Architect at NASA
“Neo4j saved well over two
years of work and one million
dollars of taxpayer funds.”
Impact

“Forrester estimates that over
25% of enterprises will be using
graph databases by 2017.”
Forrester Research, 2014

Adoption Highlights
Retail
7 out of top 10
retailers in the world
Finance
12 out of 25 top
ﬁnancial services ﬁrms
8 out of top 10
software vendors
Software
(As per 2017)

Real-Time
Recommendations
Fraud
Detection
Network &
IT Operations
Master Data
Management
Graph-Based
Search
Identity & Access
Management
Common Graph DB Use Cases

Startup Adoption
≤ 20 employees
Free Neo4j Enterprise Subscription
(Unsupported)
≤ 50 employees
Startup Pricing
(Includes Support)
Tiers, Neo4j Startup Program

Case Study: The Consumer Web
C
34,3%B
38,4%A
3,3%
D
3,8%
1,8%
1,8%
1,8%
1,8%
1,8%
E
8,1%
F
3,9%

Connections in Data
Ability to Leverage Connections

Connections in Data Ability to Leverage Connections
Function of Time Choice of Technology

Good for discrete problems
Insufﬁcient for connected
problems
RDBMS

Data lake
Good for Analytics, BI, Map Reduce
Non-Operational, Slow Queries
RDBMS

>50%of the Global 2000 are
Using or Piloting Neo4j!
As of today

Context drives meaning and
precision for Machine
Learning systems

FATHER_OF DRIVE
LIVES_IN
M
ARRIED_T
O
PLAYS
WHATCHES
BORN_I
N

Knowledge Graphs
“Graph-based machine learning is a powerful tool that can
be used to power useful features such as reminders in
Inbox and smart messaging in Allo, or used in conjunction
with deep neural networks to power the latest image
recognition system in Google Photos."

What's hot

Architecture matters. That's why today's innovators are taking a hard look at streaming data, an increasingly attractive option that can transform business in several ways: replacing aging data ingestion techniques like ETL; solving long-standing data quality challenges; improving business processes ranging from sales and marketing to logistics and procurement; or any number of activities related to accelerating data warehousing, business intelligence and analytics. Register for this DM Radio Deep Dive Webinar to learn how streaming data can rejuvenate or supplant traditional data management practices. Host Eric Kavanagh will explain how streaming-first architectures can relieve data engineers from time-consuming, error-prone processes, ideally bidding farewell to those unpleasant batch windows. He'll be joined by Kevin Petrie of Attunity, who will explain why (with real-world story successes) streaming data solutions can keep the business fueled with trusted data in a timely, efficient manner for improved business outcomes.

DM Radio Webinar: Adopting a Streaming-Enabled Architecture

Watch full webinar here: [https://buff.ly/2R9qSfq] Data proliferation has become a major challenge for many customers as they deal with new regulations and more stringent compliance requirements. Hear how these challenges can be addressed with fine grained security, full data lineage and comprehensive auditability. In this Denodo DataFest session we will cover how to: Assure compliance with optimized data management Data classification with security policy enforcement Increase flexibility with extended protection capabilities

Regulation and Compliance in the Data Driven Enterprise

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Data catalog

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Joe Caserta was a featured speaker, along with MIT Sloan School faculty and other industry thought-leaders. His session 'You're the New CDO, Now What?' discussed how new CDOs can accomplish their strategic objectives and overcome tactical challenges in this emerging executive leadership role. In its tenth year, the MIT CDOIQ Symposium 2016 continues to explore the developing role of the Chief Data Officer. For more information, visit http://casertaconcepts.com/

You're the New CDO, Now What?

Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals. In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why Data Strategy is necessary for effective Data Governance - An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Data Strategy Best Practices

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"Information from the past won't support the insights of the future - businesses need real-time data," said Forrester Analyst Noel Yuhanna. In this presentation, he explains the challenges of latent data faced by business users, the need to accelerate fast data strategy using data virtualization, and the implications of such strategy. This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/a2xNyZ.

Accelerating Fast Data Strategy with Data Virtualization

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During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability. The speakers brought together best practice techniques with Apache Spark to achieve complete CDI. Speakers: Joe Caserta, President, Caserta Concepts Kevin Rasmussen, Big Data Engineer, Caserta Concepts Vida Ha, Lead Solutions Engineer, Databricks The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included: · Building an end-to-end CDI pipeline in Apache Spark · What works, what doesn’t, and how do we use Spark we evolve · Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior · Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching · Using GraphX for matching and scalable clustering · Analyzing large data files with Spark · Using Spark for ETL on large datasets · Applying Machine Learning & Data Science to large datasets · Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for. For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/

Mastering Customer Data on Apache Spark

Think Big, a Teradata Company

Building the Modern Data Hub

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Big Data Modeling and Analytic Patterns – Beyond Schema on Read

Neo4j GraphTalk Düsseldorf - Einführung in Graphdatenbanken und Neo4j

Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand. Topics include: - The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics - Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business - An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem - The challenges that come with transforming into an analytics-driven company and how to overcome them - Practical use cases from Caserta clients This presentation was originally given by Bob at the 2017 Strata Data Conference in New York City.

Creating a DevOps Practice for Analytics -- Strata Data, September 28, 2017

(Presented at MapR's Big Data Everywhere event in Redwood City, CA in December 2016) The relationship between business teams and IT has changed as the complexity of data has increased. A traditional data pipeline designed for an IT-centered approach to information management is not designed for the data demands of today's business decisions. Designing a big data strategy requires modernizing previous approaches. Self-service data preparation in a collaborative, intuitive, governed, and secure environment is the key to a nimble and decisive business unit.

Reinventing the Modern Information Pipeline: Paxata and MapR

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Data for analytics comes in many forms, from many sources. This data holds invaluable insights for business, but currently business intelligence teams are spending as much as 80 percent of their time preparing and cleansing this data, rather than analyzing it. The challenge for today's BI and data science teams is to make this data preparation phase more efficient, so they can combine data from multiple sources - on premise and in the cloud - and shape it to be fully optimized for analytics. This webinar will demonstrate how new cloud applications and services can enable an ecosystem where data preparation, movement and analytics are seamless, for both the technical and non technical user within the enterprise.

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Data Intelligence: How the Amalgamation of Data, Science, and Technology is C...

The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by Joe Caserta, a prominent consultant who has helped many global enterprises adopt Big Data, provided the audience with the inside scoop needed to supplement data warehousing environments with data intelligence—the amalgamation of Big Data and business intelligence. This presentation was given as the closing keynote at DBTA's annual Data Summit in NYC.

Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote

Graphs in the Real World

Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business. This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices. Takeaways: How are anchor modeling, data vault, etc. different and when should I apply them? Integrating data models to business models and the value this creates Application development (Data first, code first, object first)

Data-Ed Online: Trends in Data Modeling

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