Leverage graph technologies to discover hidden insights in your EHS & Sustainability data

© 2018 iPoint BiS GmbH
Leverage Graph Technologies
Discover hidden data insight!

About CiDER
Revolutionizing EHS and
Sustainability management
CiDER is the only EHS and Sustainability tool on the market that not only
delivers Business Intelligence Reporting and Compliance capabilities but
also creates Actionable Insights for a Smarter Enterprise.
By exploiting the impact of CiDER, organisations are able to obtain critical
insight to base decisions on. CiDER is breaking new ground with its highly
intuitive, self- service platform, complete with elegant web services, data
visualisations and analytics tools.

What is a Graph?
A graph is composed of two elements: a node and a relationship.
Node
Relation
Node

What is a Graph?
A graph is composed of two elements: a node and a relationship.
Node
Relation
Node
Node = Vertex
Lets start with basic elements that make up a graph - First
of all we have what are called Nodes - A node is a data
point, any sort of data point, often it can also be plainly just
referred to as a “thing”. each node can represent an entity
(a person, place, thing, category or other piece of data)
Mathematical Terms
Relation = Edge
Nodes are connected by relationships - each relationship
represents how two nodes are associated - These
relationships can be as many as you like, they can be self
referential, relationship can loop back to a node, you can
operate a relationship in any direction, or either direction.

Why Graph ?
► 2 x growth in Data Volume every
3 years
► Data Connections are
increasing as rapidly as data
volumes
► People, Process, Assets, Devices
are increasingly related
bought
bought
viewed
bought
returned
Business Processes
e.g., Risk management, Supply
chain, Payments
Connected Data, not just BIG Data

Why Graph ?
Today, Enterprises don’t just need to manage large volumes of
data, but they need to generate insights from their existing data.
Over the years, traditional enterprise organisations have
collected a lot of data. This legacy data has the potential to
uncover insights and crucial trends, but they key to unlocking this
potential is to identify the relationships between data points.
Big Data has made large volumes of data available to
enterprises. It’s not the data volume or velocity that drives
sustainable success, but it’s the knowledge of the relationships in
your data. Understanding and modeling the complexities of
enterprise entities and their relationships to the data is the key.
The available data can be turned into a real power house by
accessing and revealing the relations within. Harnessing the
power of connected data (i.e. data relationships) is essential to
sustainable competitive advantage in todays ever-more-
connected, ever-more-competitive world.
bought
bought
viewed
bought
returned
Business Processes
chain, Payments
Source: https://neo4j.com/resources-old/sustainable-
competitive-advantage-white-paper/

Why Graph ?
If you look at this model, you think its quite rich, its nice, I can
understand it, but its no big deal.. but of course that’s just the
way we are using it to model it, but consider that this little
pattern might be one among millions, possibly tens of
millions even hundreds of millions of such nodes. What graph
databases provide us with is the ability to process patterns in
millions of nodes connected with millions of relationships,
and that’s the objective of graph technology; to be able to
handle a very large number of nodes and relationships but
still maintain the simplicity that we see in the pattern.
Graphs provide the ability to add as many nodes as you like
and to add new types of relationships. That’s why we say that
it is suitable for an agile environment; with an evolving data
model, you can just add new concepts and new nodes
dynamically at any time, as the business grows and
requirements change.
bought
bought
viewed
bought
returned
Business Processes
chain, Payments
Source: https://neo4j.com/whitepapers/graph-
based-search/?ref=solutions

Social Networks - Graph
Use Case
‣ Discover unique
relationships
‣ Friend-of- friend
Recommendations
‣ Shortest Path
ME
X
Y
CONNECTED_TO
CONNECTED_TO
V W
Z
CONNECTED_TO
suggest
CONNECTED_TO
CONNECTED_TO
CONNECTED_TO
shortest path

Use Case
One of the areas where Graph Databases have been most widely
used are Social networks (for example, LinkedIn)
• Discover new professional opportunities - Graphs help you understand how people are connected, even
when you don’t know their initial relationships by leveraging the possibility of identifying unknown
connections through visualisations.
• Humans are social creatures, so graph-powered recommendations within a social context are more
effective. Friend-of-friend recommendations help users connect and build networks faster and more
organically.
• Shortest Path - What is the shortest connection between me and someone else of significance to me —>
Who would be the closest to me to introduce me to someone else…. This capability for example can be
extrapolated to supply chains to figure out alternative supply channels.
This capability for example can be extrapolated to supply
chains to figure out alternative supply channels.

Use Case
One thing you might notice
here is that the connections
are extremely rich and
meshed; in no way could you
describe this as a hierarchy,
or implement this as a
hierarchy. It is a very
complex and interlinked
network.

• Highly Complex Relations
• Cluster Analysis
• Hot Spots / Hubs
• Pattern Detection
Paradise Papers - Graph
Use Case

ICIJ - International Consortium
of Investigative Journalists -
leaked documents about
offshore legal entities.
More than 13 million leaked documents,
emails and database records have been
analyzed using text analysis, full-text- and
faceted-search and most interestingly,
graph visualization and graph-based
search.
Since the ICIJ announced their investigation,
we’ve seen many reports being published
covering activities of companies like Nike, Apple,
the Queen of England’s estate, and connections
of Russian investments to politicians and
companies like Facebook and Twitter.
Use Case
Source: https://neo4j.com/blog/depth-
graph-analysis-paradise-papers/

Graph visualization is a powerful way to
explore the data. For example, identifying
highly connected clusters of nodes can be
done by visually examining the graph.
Cluster Analysis —> Clustering is a visualising aid, that allows one to cluster nodes
based on certain properties and these clustered groups then can lead to detection
of certain patterns. For example, from an EHS perspective, clustering the facilities
based on certain compliance rules, ongoing audits or types of incidences.
Use Case

Lots of connections going to a single node could mean a lot of related data, establishing the
importance of the node.If this node in turn is connected to other important nodes (for
example in infrastructures networks, telecommunications networks, road networks), it could
point out the significance of the node, how critical that node could be, and what influence
that node would have on the business (for example suppliers that have a lot of connections
to one’s business)
With each node, we can store a lot of metadata, like location details, addresses etc. Using a
service such as Google’s geocoding API we can perform a lookup to turn these address
strings into latitude and longitude points.
Use Case
Another example of graph
visualisation to explore data is
Hotspots/risk analysis

Once we have geocoded these addresses, we
can use geographic analysis to find more
insights into the data. One visualisation tool we
can use is a heatmap, where observations are
represented as colors.
All this is not even taking into account the Visual
Inspections algorithms that can be built on top
to generate even more insights automatically,
based on the entire graph network. (Google
PageRank). CiDER’s search will be based on
such pattern detection algorithms to bring
about more insights from your data.
Use Case

✓ Real-Time Recommendations (Walmart)
✓ Order Tracking / Routing (FedEx)
✓ Fraud Detection (Generali, Credit Card Companies)
✓ Network Analysis (Google’s PageRank)
✓ Master Data Management (Pitney Bowes)
✓ Identity & Access Management
These are some of the other successful implementations
of graph technology by industry leaders:
Successful Graph
Applications
Source: https://neo4j.com/resources-old/top-
use-cases-graph-databases-white-paper/

In its drive to provide the best
customer web experience,
Walmart needed to optimise
online recommendations.
This new user experience
required data that connects
complex buyers and product
data to gain insights into
customer needs and
product trends.
Walmart’s team substituted a
complex batch process with a
graph database, the perfect tool
for real-time recommendations.
Successful Graph
Applications
By design graph databases can
quickly query customer’s past
purchases, as well as instantly
capture any new interests shown
by the customer’s current online
visit — essential for making real-
time recommendations)

≠
‣ Complex and
hierarchical data
‣ Dynamic Structures
✓360-degree insights
✓Flexibility & Scalability
Master Data Management
Visual management of organisational structure in CiDER -
Master Data Management in CiDER powered by Graph
Database

Master data, such as organizational
and supplier data, has deep hierarchies
with top-down, lateral and diagonal
connections. Even though we think of
them as hierarchies, most businesses
are actually networks. Once you have
the real-life complexity of multiple
entities, with multiple types of
relationships, the vision of a beautiful,
perfect hierarchy is destroyed. This
happens with organisational structures,
product “hierarchies”, locations,
documents etc.
As mentioned earlier, graphs are data
structures that describe both data and
their relationships. The most commonly
recognised forms of a graph are business
networks and hierarchies. Using a graph
database allows you to map your master
data in a much more real world way,
without the constraints of strict
hierarchical structures.
For example, think about mapping your
Business Units (or business structures)
and regional/geographical structures in
one hierarchical tree —> nearly
impossible!
Now also consider mapping your supply
chain using hierarchical trees?
The highly dynamic nature of master data,
with the constant addition and re-
organization of entities, makes it harder to
design systems that accommodate both
current and future requirements. Most of the
current systems work with SQL or Relational
DBs, that require a strict schema which needs
to be defined ahead of time before
implementation and therefore becomes
harder to change, adapt to changing
business structures and adjust to new
requirements.
Source: https://neo4j.com/use-
cases/master-data-management/

With scalability built into the underlying
design (flexible schema) graph databases
(and therefore CiDER) allows master data to
grow as fast as the business does. You can
add new elements and new relations and
the schema evolves automatically over
time as you add more to the graph.
Think of changing
business structures, with
new acquisitions, mergers
or divestments.
The best data-driven business decisions aren’t
based on information silos, Instead you need
real-time master data information about data
relationships. Graph relationships easily connect
your siloed data between other systems such
as HR systems, ERP systems, BMS systems etc., to
provide a consistent vision of enterprise data.

✓Groups & Roles defines permissions
✓Users are managed in Groups
✓Access reduced down to content level
Identity & Access
Management in CiDER

Identity and Access Management is about managing information about parties (e.g. administrators, business
units, end-users) and resources (e.g., files, shares, network devices, products, agreements), along with the rules
governing access to those resources.
Traditionally, identity and access management has been implemented either by using directory services or by
building a custom solution inside an application’s backend. Hierarchical directory structures, however, can’t cope
with the complex dependency structures found in multi-party distributed systems.
A graph database access control solution allows for both top-down and bottom-up management:
• Which resources – company structures, products, services, agreements and end users – can a particular
administrator manage? (Top-down)
• Given a particular resource, who can modify its access settings? (Bottom-up)
• Which resource can an end-user access?
Access control and authorization solutions powered by graph databases are particularly applicable in the areas
of content management, federated authorization services, social networking preferences and software as a
service (SaaS) offerings, where they realize minutes-to-milliseconds increases in performance over their relational
database predecessors.
Identity & Access
Management in CiDER
Source: https://neo4j.com/resources-old/top-use-
cases-graph-databases-white-paper/

NAEM 2016: Approaches to EHS&S Data Management
Decentralisation, within a central system
‣ Decentralised
Structures
‣ Diverse Operations
producing variety of
data
‣ Individual operations,
unique risks and
requirements
EHS&S - Graph Application

Decentralized
structures may not
lend themselves to a
centralized reporting
systems
Diverse operations
may produce a
variety of data
types
Internal culture may
value decentralized
decision making
Acquired businesses
may have their own
software tools to
manage their own set of
diverse requirements
EHS&S - Graph Application

Gartner (https://www.gartner.com/it-glossary/predictive-analytics/)Difficulty
Value
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics
What
happend?
Why did it
happen?
What will
happen?
How can we
make it happen?
AI|ML
utilize
graph power
Capability Maturity Model

RDBMS/Excel
Used for record keeping (created for the purpose of storing
data in structures), These structures impose certain boundaries
and constraints, where data has to be stored in certain fashion
which might not represent its natural way of occurring,
particularly with its connections and therefore makes it difficult
to be used for diagnosis.
Graphs
Created for storing and relating data the way it
naturally occurs, therefore respresents the natural
state of data and its connections, which are important
to understand the “why”.

Most systems today are focused on descriptive analytics; describing
what happened based on the data and information available. But in
order to find improvement and optimisation potential, as Gartner
describes it, we need to follow this path of analytics. From “what
happened” to “why it happened”, the graph technology can really
provide the push and empower systems to get there rather quickly
and easily, because it provides a natural (close to reality) way of
storing and understanding the data and relationships.
A lot of the analytics today still use tools like excel, which is still a good
tool for descriptive analytics, but it won’t help us climb through this
ladder of business analytics.
Sure, normal RDBMs can be used, for
example Oracle supports a lot of analytics,
but if your goal is to walk this path, graphs
will provide the push and the technology
support to empower these possibilities.

CiDER will start implementing machine learning, based on
its graph implementation to learn pattern detections, trends
and make predictive analytics. Graphs will deliver the
learning patterns for implementing the machine learning.
Machine learning needs data patterns to learn from, for
example the detection patterns/queries of the graph that we
saw earlier is something we will use to feed the machine
learning with valid data, which in turn will train machine
learning to provide better suggestions.
We have picked the CiDER technologies with these
steps in mind. A lot of the existing systems were
developed with the need for reporting and
descriptive analysis, which is still very much the
necessity. But it does not bring that additional
value with all that data being available today. With
CiDER’s technology we want to turn this data into
insights thereby generating that additional value
for businesses.

Thank you
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Leverage graph technologies to discover hidden insights in your EHS & Sustainability data

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