7_2_Graph_Guide

Graph Solutions

Overview

In an ever connecting world, where everything is linked to everything, enterprise data are still mostly stored in silos. And this is not only a technical limitation but rather an organizational mindset. Graphs help to break these silos, make information wider accessible and help gain new insights.

The most prominent example is the friend-of-a-friend concept. Social networks but also recommendation systems use this paradigm to propose people you may also know or products you may also like. Graphs provide additional semantics as well as wider views across organizations (business units, departments, business partners) along the value chains and across systems. This leads to significantly higher transparency and new insights for operations and advanced analytics.

What is a knowledge graph?

A knowledge graph is a collection of knowledge for a certain domain. It can span across domains in enterprise knowledge graphs. The content is

  • collected by data integration architecture: Data is automatically loaded from enterprise sources, from web resources and by direct input through UIs.

  • refined by machine learning algorithms: ML algorithms can categorize, cluster and group as well as create semantic relationships of data.

  • curated and governed by subject matter experts: Domain experts define the content available in a knowledge graph and define access requirements for end users.

In graph theory for information technology, a graph is semantic representation of knowledge through nodes and edges. Nodes (or vertices) usually represent data points while edges connect them physically. These connections can be either directed (uni-/bidirectional) or non-directed.

Database paradigms 2

What is the advantage of using a graph over using relational DBMS?

In a graph the data points are physically connected via edges, which makes querying extremely fast. When querying through SQL, data is collected by comparing indexes across different tables. This becomes very slow with an increasing amount of data and their heterogeneity. For instance, looking to get a complete customer view for insurance, we need to collect data about a customer, contracts, offers, claims, about products, about relationships, … A graph is capable to collect these information faster than RDMBS via graph traversal, i.e. by checking each node in a graph that is connected via edges to the starting node. On top of that, graph data models are much more flexible since nodes with new labels (new data objects) or new properties can easily be added.

What is a graph database?

Various graph databases exist, with the most prominent being property graph databases and RDF (Resource Description Framework) stores. In enterprise architecture, property graphs are wider spread because they provide higher elasticity when it comes to data modelling.

A property graph consists of nodes and edges, each of which can be qualified with further properties. That allows a data model to be highly flexible, as properties can simply be added on demand and properties that are not valid for certain nodes are simply non-existent (instead of null).

A semantic data fabric for your organization

Due to its ability to link heterogeneous data, disparate data sources are made easier accessible to unveil hidden connections between data. Complex relationships will emerge that provide deeper insights in ever-growing amounts of data. By relating big data sets to master data companies will create smarter data that will drive business transformations to new heights. Easier access to a smart data foundation will also support data scientist in their work. A lot of ground work for AI solutions can be made using graph ML algorithms.

The key value levers of graphs are as follow:

Discovery & Searchability Due to the physical linkages between nodes, it is very easy and fast to search data in large data sets. In addition, new relationships between nodes can be inferred.
Flexibility & Modelling Due to its open data model architecture, graph databases can be enhanced with new data objects easily.
Analytics & Semantics With its flexible data model, semantic meta data can be saved in the data model along with the actual data to create better insights and more meaningful analytics.
Performance & Speed Designed as No-SQL database with additional semantics on top, property graph databases are extremely fast for querying complex data.
Get further insights about how graphs create value.
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Typical use cases

The strengths of the graph lie in the connection of heterogenous data.

Fraud Detection & Analytics

Uncover fraud, e.g. in insurance, using real-time analytics across many different data sources and objects.

Knowledge Graph

Inventorize your corporate assets using graph technology for added transparency and faster search, e.g. across claims, offers and contracts.

Network and Database Infrastructure Monitoring

Create higher transparency in complex interdependencies central to managing networks and IT infrastructure.

Master Data Management

Eliminate silos in master data and reference data and create better inter-connected insights across various data objects in real time. Enable 360° views of customers or complex supply chain hierarchies for materials.

Social Media and Social Network Graphs

Better understand social connections, infer new relationships and identify opinion leaders about your products or services.

Identity & Access Management

Map users, assets, relationships and authorizations to create higher transparency in identify management.

Privacy, Risk and Compliance

Connect the dots for regulatory compliance (GDPR, BCBS 239, FRTB…). Manage your risk by connecting several data points and check against regulations.

Artificial Intelligence and Analytics

Build the base for your AI applications and leverage ML capabilities of most graph products. Graph ML will increase your models even more.

How we help

  • Digital Transformation
  • Operating Model Improvement
  • Machine Learning
  • Data Excellence
The digital revolution is not about technology – it is about a differentiating customer engagement and innovative products and services. Our industry know-how, proven digitalization approach and relentless execution makes you meet the future with confidence.
Digital product & capability building We offer an effective and fail-fast approach to test new opportunities and build a pipeline of incubation business cases. Our proven methodology drives opportunity testing from initial scoping to prototyping and live products.
Outside-in on digital opportunities We offer outside-in expertise on digital trends and competitor moves. Our insights are brought to live in the context of your business model, which helps you set the right priorities and decide when it is time to take action. 
Digital strategy and roadmap We shape your digital vision and build your transformation business case and roadmap. Our focus lies improving your customer focus and improving operational excellence. We also help you explore disruptive new products and services to increase your market differentiation. 
Transformation delivery Our industry knowledge gives us a clear view on the challenges faced by our customers. We help to set the right strategic priorities and drive change from targeted improvements to large scale transformation. 
The change from a product focused to a customer centric organization is a major shift across the industry. Organizations need to become more agile in responding to evolving customer needs and business priorities. We help our clients rethink the way they deliver value to their customers and become more effective.
Customer centricity Often, employees are not aware how their work supports the customer journey. We assess how your team delivers value to customers for key touchpoints and moments of truth. And we help you ensure a relentless customer focus across your organization.
Process and organizational simplification

We break down silos, set the right incentives, reduce complexity, and build lean processes to deliver customer value.

Workforce transformation

We help our customers shift from to define effective operating models to reduce costs and to improve the experience of both clients and employees.

A siloed and technically focused workforce needs to transition to an interconnected, collaborative and customer– centric operating model.

AI enabled automation

Augmenting the workforce through intelligent automation and data-driven insights are key levers to improve the customer experience and improving the operational effectiveness. 

We have developed our own solutions to help you drive automation and data-driven insights along your value chain. 

Machine Learning is a very potent approach to solving tough problems covering areas such as smart automation and predictive complex forecasting. We offer consultancy service to help you build machine learning capabilities. Additionally, our suite of machine learning products helps you to accelerate your adoption journey.

Strategy

We shape your Machine Learning vision and identify opportunities along your value chain as input for your transformation roadmap. Our focus lies on improving your customer focus and operational excellence. Additionally, we help you explore disruptive new products and services.

Solution implementation We support you in delivering your transformation roadmap, managing your stakeholders and suppliers and ensuring successful go-to-market. We either leverage our own solutions or help you build your own capability stack.
Opportunity testing We offer an effective and fail-fast approach to test new opportunities and build a pipeline of machine learning business cases from initial scoping to prototyping and live products.
Our product suite

Our product line includes cutting-edge data analytics and machine learning solutions to help our customers automate unstructured data extraction and take the right decisions.

Data is the lifeblood of any organization. Despite the high importance of data nowadays, many companies still struggle to organize their data and the processes around it. This is mainly due to organizational silos, legacy systems and the sheer overwhelming amount of data coupled with often poor quality. We help companies to tidy up their data management in order to enable a data-driven organization that is ready for future challenges.
Data Strategy It all starts with articulating a clear vision of data management and a defined path on how to get there. Without a strategy, data initiatives are often purely IT-driven projects that do not get the necessary business buy-in. We help to define strategy that fits your organization.
Governance & Data Quality Data quality suffers significantly without a clear governance. We support organizations to define governance, data structures incl. data lineage specifications and processes around how to manage all sorts of data.
Data Driven Enterprise The data-driven enterprise is the ultimate objective of a company to take decisions based on data. That means that the basis is set and next steps have to be taken to enable advanced analytics scenarios or process automation using state of the art technologies.
Data Infrastructure Technology is an important enabler for data driven companies and those who strive to become one. With our expertise we are able to advise on latest technologies such as streaming or graph technologies.