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graph database Tag

10 March
Connected data in telecoms2

How the Telecoms industry leverage triple-store technologies

jphare   |   Events

Big thanks to Connected Data meetup group regular Valerio Malenchino of Ontology for his really in depth overview of how telecoms providers use Connected Data tech. It was really interesting to see how semantic impact analysis can be conducted in a way that is both accessible and detailed for users.

Valerio gave us a great introduction to both why triplestores are used for integrating disparate data sources as well as some practical examples of the analytics that are applied to answer connected data questions.

 

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08 September

MeetUp – what connects graphs and linked data?

jphare   |   Events

Another successful MeetUp completed! We wanted to thank everyone for attending. Great talks from Nigel Higgs and Jesús Barrasa. Nigel quickly explained that this MeetUp aims to bring together Graph database and Linked data professionals to spark new ideas and conversations. Jesús presented his work on how Explicit Semantics can be used in Graph Databases using Neo4j. This talk sparked a lot of interesting ideas and questions about merging these two technologies together, what would be the opportunities and threats of this approach.

 

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To be apart of the conversation next time join our MeetUp group. Our next event is going to be on the 4th of October! Follow us on Twitter for more news on Connected Data 

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31 August

Connected Data London 2016

jphare   |   Events

 

 

We wanted to thank everyone for joining us at the Connected Data London 2016. We had over 70 Connected Data Practitioners on the day leading to some thought provoking conversations around shared challenges and solutions. It was great to see people sharing so many practical experiences around working with the different standards, technologies and use cases.

 

Our speakers demonstrated how they have implemented connected data strategies in such leading companies and organisations as NASA, BBC, Financial Times and the European Bioinformatics Institute, University of Southampton and Brunel University. Topics included next generation search, recommendation engines, connected data science and knowledge management.

 

 We also wanted to thank CDL’s sponsors Ontotext, Neo4j, Linkurious and Semantic Web Company for helping make this event a reality.

 

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The next Connected Data event is planned to be a MeetUp in London, it is scheduled for the 7th of September. If anyone has any suggestions on topics / speakers please do drop us a line – it would be great to see you there!

You can find the event slides here, check out the video gallery for full talks from the event. To follow the news about the next Connected Data London conference sign up to our newsletter

We hope to see you at the next Connected Data London conference!

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23 May

Connected Data London – the leading conference bringing together the Linked and Graph Data communities

jphare   |   Events

 

We are happy to announce our brand new conference – Connected Data London 12th of July, Central London. Join us and practitioners from both Linked and Graph Data communities at this great event.

 

There’s a lot going on in the world of connected data in 2016. From investigative journalism triumphs such as the Panama Papers to important pan-European Linked Open Data intiatives. Technologies using the semantic web and graph-based approaches continue to develop and mature at a rapid pace.

 

An array of vibrant and diverse communities has emerged across a range of sectors. Academics, innovators and entrepreneurs continue to push the boundaries ever further. The variety of connected data problems practitioners are tackling is truly awe inspiring. Journalism, academia, financial services, life sciences and publishing are a few examples of where linked and graph data techniques are breaking new ground.

 

Until recently however practical examples, reference cases and connected data war stories were hard to come by. Similarly despite possessing a surprising amount in common these communities have until also remained relatively disconnected. Connected Data London 2016 is the only event that brings together the Semantic Web, Linked Data and Graph Data disciplines under one roof.

 

 

Attendees will have a unique opportunity to:

 

  • See how thought leaders from various sectors including – aerospace, journalism, government, academia and many more are applying connected data technologies in their fields.
  • Network with industry leaders and early adopters on their practical experience of connected data approaches
  • Learn the theory behind the technologies
  • Understand the challenges and best practices of the graph and linked data communities
  • Consider the possibilities for your business or organisation

So whether structured, unstructured, open, closed or graph data is your thing, Connected Data London 2016 is the event for you.

 

 

Topics include: 

 

  • Linked Data integration approaches & technologies such as endpoints and APIs
  • Connected Data visualisation approaches
  • Connected Data Quality & Governance approaches
  • The pros & cons of leading data storage technologies such as triple stores, graph databases and document stores
  • Linked Data Standards including RDF and SKOS
  • The power of open source query languages such as SPARQL and CYPHER

 

Find out more here

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03 March

GraphTalks London: Graph-based Metadata Management & Data Governance Seminar

jphare   |   Events

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We wanted to thank everyone who joined us and our partners Linkurious and Neo4j at our breakfast seminar.

 

In this introductory event we explored high-level key concepts of Neo4j and explored using Neo4j to rapidly and efficiently manage enterprise metadata. It was followed by an informative Q and A debate and a lively networking session.

 

The event slides can be found here: Intro Graph based metadata management & data governance in financial services – by Anthony Flynn Graphs in Metadata Management – by Jesus Barrasa, Mixing agile & enterprise metadata requirements using Neo4j – by James Phare and Jean Villedieu.

To watch our latest webinar about Lean Data Governance and Metadata Management click here. To sign up for our newsletter and find out about our latest events click here.

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16 December

Data to Value and Neo4j announce a strategic partnership

jphare   |   Blog

LONDON and MALMÖ —December 16, 2015— UK-based consultancy Data to Value Ltd. (http://datatovalue.co.uk/) and graph database world leader Neo Technology (http://neo4j.com/) announced a new strategic partnership today.

 

Neo Technology is the creator of Neo4j, the world’s leading graph database, which easily stores and analyzes highly interconnected data. Data to Value will be leveraging Neo4j principally within the financial services sector through their next generation Lean Data consulting.

 

While relational databases are flexible, they are not well-equipped to store and analyse high-volume, highly connected graph or network datasets. Graph technology is now a dominant feature in many use cases such as online retail recommendation engines, fraud prevention and master data management.

 

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Within financial services, graph technology remains a relatively untapped resource despite many banks, asset managers, hedge funds and other organisations facing graph or network data challenges.

 

Traditionally, many data solutions in the business performance, investment management and risk management spaces have relied on heavily abstracting data using techniques such as the party-role pattern. As a consequence, this often hinders business users’ abilities to understand data using familiar terms and analyse the inherent connections within the data.

 

Graph structures are a more intuitive way of modelling highly connected data types.

 

A key focus of the partnership is for Data to Value to help financial services customers leverage the power of Neo4j in overcoming graph challenges including:

 

  • Regulations and data lineage requirements such as BCBS239
  • Risk requirements such as systemic risk, counterparty risk and market/concentration risk analysis
  • Business performance and client management requirements
  • Fraud, AML and KYC requirements
  • Data governance, strategy and metadata management requirements, such as impact analysis

 

“We are delighted to have partnered with Neo Technology to bring the power of graphs to our customers,” notes James Phare, Managing Director of Data to Value. “Our unique Lean approach to managing data is focused on minimising waste and applying the most appropriate tools to data problems. Neo4j’s performance, scalability and accessibility makes it unrivalled in the graph space and the perfect solution for exploring interconnected data. We spend a great deal of time helping organisations to understand the relationships buried in their data. The partnership with Neo technology will help us to further mature this proposition for our clients.”

 

Emil Eifrem, the CEO of Neo Technology agrees.

“Graphs are eating the world,” notes Eifrem, “and financial services can greatly benefit from tapping into the power of graph-connected data. We’re proud to partner with Data to Value as a leader within the financial services sector to help their clients tackle issues related to fraud detection, data governance and impact analysis.”

 

For further details or to arrange a follow-up call, please contact Data to Value Ltd. or Neo4j.

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01 December

Graph data powered financial risk management

jphare   |   Videos

 

Take a look at our new video illustrating some of the techniques we use for applying graph technology and network analysis techniques to financial risk management challenges for hedge funds, banks and asset managers. These include credit / counterparty risk, market risk and systemic risk.

 

 

 

 

This example explores how the Volkswagen emissions crisis could affect companies within the VW group and also external companies directly and indirectly. It is an excerpt from a longer video exploring our cognitive investment management services that can be found here.

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30 November

Webinar: Cognitive Investment Management, Research & Risk Management

jphare   |   Videos

In this short webinar watch how we help leading Investment Managers, Banks, Brokers, Asset Managers and Hedge Funds supercharge their existing investment approaches with Big Data, Semantics and Machine Learning.

 

Director James Phare talks about how we use machine learning algorithms, semantics and other techniques to process huge volumes of unstructured, semi structured and structured data in order to identify hidden risks and opportunities.

 

Learn to extract relevant content from huge volumes of data, standardise it and transform it into structures that can be analysed. We demonstrate how we use visualisation tools such as Tableau to explore and analyse news stories related to the Volkswagen emissions crisis. We also show you how we use graph technology such as Neo4j to structure this data into graphs or networks in order to explore hidden relationships and dependencies in company, sector, country and product data.

 

 

If you would like to see the webinar slides follow this link.

 

 

 

About us

 

Data to Value are a specialist Data consultancy, based in London. We apply graph technology to a variety of data requirements as part of next generation data strategies. Contact us for more details if you are interesting in finding out how we can help your organisation leverage this approach.

 

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03 September

Managing your data with graph databases

jphare   |   Blog

Data is becoming more and more important in today’s business environment. It can be related and linked to products, sales, customers, significant events and many other categories to provide a rich, objective view of a business’ performance. Data however is often maintained in individual silos and databases yielding separate and inconsistent versions of the truth. Most companies do not even realise their data is unnecessarily replicated and duplicated.  Revealing this truth however and identifying these relationships has traditionally been costly, error prone and time consuming.

 

Graph databases are trying to solve this problem. By using Graph databases a company can focus on data relationships – see patterns and trends that would not be evident in traditional databases. This emerging technology is also much faster and cost effective at performing functions such as cluster identification and graph traversal. For instance, a company can track customer behaviour in real time offering a complimentary product to the product they’ve purchased, leading to an increase in sales.

 

Graph databases embrace relationships as the main feature of its data model and is able to store, process, and query connections much more effectively. Graphs are used to model complex interactions where concepts are connected by various types of relationships, with multiple parents and multiple children. Graphs can have an almost infinite depth and even be circular in nature. This is a very different paradigm from most relational database implementations since the 80s which continue to be the main database option for many businesses.

 

Relational databases are considered to be ridged and do not perform as well as a graph databases in these tasks. For instance in handling hierarchical data. Let’s imagine we have an employee table which has a unique employee ID and a foreign key field holding the employee ID of the person’s manager, who themselves is an employee. This can be used to create an organisation’s management structure. But hierarchical data is often not that simple. An employee might be managerially responsible to one supervisor, but professionally responsible to another. The hierarchy data type cannot handle multiple parents, so is not appropriate for such data. Data might have bi-directional links like in a social media environment. Bob might “follow” John, but John may not follow Bob. Relational databases cannot handle these multi-directional relationships well.

 

This innovative approach to data management is transforming how data is handled and interpreted. It’s helping companies to understand customer behaviour and relationships,reduce fraud, better target relevant products and save money along the way.

 

For more information about the latest cutting edge data management techniques contact us.

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14 August
Data To Value Manta Flowchart

Using Graph databases for data lineage

jphare   |   Blog

Graph databases have come of age and are being applied to many use cases outside of traditional requirements now such as infrastructure analysis and social networking.  Optimised for storing relationships between things, or nodes as they are often called, many companies are unaware of the scope to apply this technology to their day to day problems.  Many areas once viewed as very challenging to model using traditional tools such as Data Lineage, Enterprise Architecture and dependency analysis are now much easier to model and analyse. This enables rich reporting for business stakeholders underpinned by robust and empirical analysis.

 

One area we have had great success is analysing data lineage, particularly within complex Data Warehouse and Data Integration stacks where often the linkages between data sources and dashboard elements / report columns can be buried beneath transformations, mappings and flows. Our software partner Manta Tools (powered by Titan DB) enables users to rapidly understand the relationships hidden in their Oracle, Teradata and Informatica stacks. This saves a great deal of time and cost when maintaining your Data Warehouse and implementing changes or upgrades.

 

Please do get in touch if you would like to know more about how this latest generation of graph tools can help your organisation.

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