Matt Shearer

What is Domain-Driven Design?
Domain-Driven Design (DDD) is a method for developing a team's understanding of
a problem space. It emphasises placing the primary focus of a project on the
core area of the business (the core domain). This often takes the form of
facilitated workshops with domain experts and the development of a shared set of
conceptual models. DDD has the additional benefit of creating a shared
understanding (ubiquitous language) between teams when designing technical
solutions.

Skilled facilitation of domain modelling sessions helps our clients get to grips
with their domain. Through collaborative domain modelling exercises, we work
with stakeholders to gain a broad understanding of the target domain. We
typically focus on the high value elements of the domain, iteratively refining
and validating models to achieve the right level of detail to deliver the most
business value.

Would my project benefit from Domain-Driven design?
Common reasons that you would look to Domain-Driven Design would be projects
that need to distill the knowledge of domain experts into a product. These types
of projects can often suffer from a disconnect in domain understanding.
Discovering this disconnect in code or in data models is expensive to fix. DDD
can address this by tackling differences in domain understanding before
implementation begins. It can create a perfect bridge of understanding between
the business stakeholders, the problem-space, and the technical teams.

Another application of DDD is in projects that require strong information
architecture. Where the intention is to organise a product around the entities
and relationships of the domain. This is often seen in approaches that build on
taxonomies or knowledge graphs.

We see Domain-Driven Design as a critical starting point for all our knowledge
graph implementations, and on the majority of our project engagements, including
development of our own products.

Domain-Driven Design

Since 2017 we have been developing Luminery - A video moment SaaS platform
[https://luminery.com] that massively boosts video reach, engagement, and
monetisation opportunities.

Luminery enhances video with machine-friendly structured metadata that enables 
all players in the video value chain to create new brand experiences, and to
harness this new core business data.

Luminery on Jamie Oliver
We have been working with Jamie Oliver to make the moments in his video more
"Cook along"-able, and to add products to key video moments when they are
actually relevant to the viewer.

Screen grab showing how Luminery promotes Jamie Oliver products when consumer
intent is at its height.Luminery on the Rugby World Cup
We worked with the Japanese broadcaster J SPORTS to enhance their Rugby World
Cup video [https://video.jsports.co.jp/] with activated video moments, promoting
their e-commerce products, their SVOD service, and highlighting each important
sporting event.

Rugby World Cup moments with new SEO and Social optimisation from Luminery. Each
sporting event was labelled with optimised metadata.

Luminery: Video moment technology

Paul Wilton

Data Language has exceptional experience in the design, implementation and
deployment of knowledge graphs, linked data,  and semantic technologies. We have
been working with graph databases since our inception, when our founders worked
on possibly the UK’s first enterprise scale knowledge graph for BBC News and
Sport. Since then we have helped global organisations such as Cochrane,
Wellcome, and Euromoney implement them too. Our typical approach to implementing
a knowledge graph solution is :

Domain Driven Design
Through collaborative domain modelling exercises, we work with stakeholders to
gain a broad understanding of the target domain, iteratively refining and
validating ontology models to achieve the ideal level of detail to deliver the
best outcomes, business value and utility.

Technical and Data Architecture
Not all graph databases are equal. We are wholly vendor-agnostic, and our deep
experience in designing scalable, maintainable, and evolvable knowledge graph
solutions, means we will always select the right graph technology to deliver
your use-cases. We understand the difference between label-property graphs and
RDF based graph databases. We have a total grasp on the practical and pragmatic
use of semantics and inference to ensure your solution is not over-engineered
but delivers just the right level of complexity to optimise your total cost of
ownership, and ensure your solution can evolve as your business evolves.

Integration - Data and Software Engineering
We can help you integrate the knowledge graph with your business systems and
workflows, either by helping your own software engineering team with querying
the graph using query languages such as SPARQL or Gremlin, through to developing
microservices and web interfaces that encapsulate your target use-cases. We are
skilled at data engineering, and can help you populate your knowledge graph,
transforming and loading your own business data, and linking to or ingesting
open-data as required.

Automation and Deployment
We can work with and guide your own infrastructure team, or take on the task of
engineering, integration and deployment entirely ourselves. We are skilled at
contemporary best practices for deployment of infrastructure in the cloud or
on-prem. Our devops engineers are skilled in the use of infrastructure-as-code
for fully automating the deployment and maintenance of the entire knowledge
graph solution.

Graph databases

Knowledge Graph Engineering

Alexi Buckingham

AI Text Classification
Data Language has been tackling the problem of text classification for many
years and has used their wealth of experience in NLP, data science, and text
analytics to create a product that is world leading. 

Tagmatic is a sophisticated content classification SaaS product that uses AI and
machine learning to make content categorisation and tagging as easy and
efficient as possible. Our product dramatically reduces the cost and effort
associated with tagging and organising written content, speeding up
behind-the-scenes processes and ensuring accuracy.

With Tagmatic, the difficult engineering and scaling is done for you,
dramatically reducing the total cost of ownership of developing AI based
classification in-house.

Tagmatic's main features
 * Fully managed Software as a Service, trivial to integrate into your systems
 * Language agnostic
 * Classifies content using your own vocabularies and dictionaries
 * Self optimises and learns on-the-fly independently to your workflow
 * Dramatically reduces the time, cost and complexity of tagging content
 * Gives you more time to do what you do best

Benchmarking
We have benchmarked Tagmatic against Reuters RCV1 and Arvix Academic Paper
Dataset AAPD and acheived impressive results.  Read more about the benchmarks
here [https://datalanguage.com/blog/benchmarking-tagmatic].

Tagmatic is perfect for almost any text classifcation problem, and in any
language, some of its primary use-cases are : 

Publishing
Tagmatic was orginally built to tackle the challenges that publishers
[https://datalanguage.com/tagmatic-for-publishers] and media organisations have
with automatic and human-assisted content tagging: reacting to new terms (tags)
fast as they break in news, automatic on-the-fly training, consistency,
disambiguation, editorial tagging style  - all this is handled out of the box by
Tagmatic. 

Law and Legal Tech
Tagmatic is perfect for classifiying legal texts and documents, or
part-of-documents such as contract clauses. Once trained, it can be used to
automate the tagging of incoming legal documents, as well as archives of
existing documents. We have demonstrated Tagmatic on Hansard of some 200,000
parliamentary interventions for the 2005/6 period , classified with >7200
categories from the parliamentary library classification vocabulary, and
acheived extremely accurate results.

Healthcare and Life Sciences
Through our work with Cochrane, we have extensive experience with bioinformatics
vocabularies such as SNOMED-CT, MESH, and MedDRA. Tagmatic is ideally suited for
the automated classification and tagging of content against these vocabularies,
whether for classfiying clinical trials,  patient records, or general medical
documents.

Free Trial
If you are a publisher or work with high volumes, or large content archives and
are working on or thinking of building your own automated tagging platform, then
Tagmatic is an ideal solution - you can be up and running in hours, and reduce
your total cost of ownership of building, scaling, and hosting your own AI / NLP
based classification solution dramatically. 

Please get in touch for a free trial.

Tagmatic

Tolga Ergin

It’s what we do
Building innovative data driven platforms and products is what we do. We are
experts in innovative data driven products and platforms. 

We are world class data and software engineers, and build practical machine
learning and AI solutions that deliver real business value rapidly.

But we don’t just stop at the platform, our talented developers also build
beautiful, accessible data visualisations and user experiences.

How do we do it
Our approach is multi-fold. Tried and tested, and focussed on delivering early
value.

Domain-Driven Design
To build a great product it is critical to understand the domain. We use 
domain-driven design [https://datalanguage.com/domain-driven-design] at the
outset to gain a broad-enough and deep-enough understanding of the problem space
and value-proposition not just to deliver the right solution, but to deliver the
right value too.

Minimum Useful Product
Delivering early value is key. Working with stakeholders and the outputs of
domain-modelling we identify a Minimum Useful Product (MUP), that is both
desirable and viable.

Technical Strategy
We take a practical and pragmatic approach to technical strategy
[https://datalanguage.com/technical-strategy], tailored on business vision,
rigorous risk management, lean processes for agile governance, ultimately
designing technical solutions to support market opportunities.

Iterative Full-Stack Implementation
We build a feature-driven vertical slice through our platform as early as
possible. We automate from the outset using contemporary tooling for continuous
delivery, including infrastructure-as-code, and
test/behavioural-driven-development patterns.
Our data models are maintained and evolved iteratively too, modelling
just-enough to deliver the detail of the evolving-features, while keeping in
mind the big picture domain model. 
By delivering an early vertical-slice, we identify risks and challenges early.
Our vertical-slice is iteratively evolved feature-by-feature to deliver the MUP,
with user and stakeholder feedback at each iteration.

Data Driven Product Innovation

A great AI or ML model is worthless if it can't be delivered in a scalable, cost
effective and technically sustainable way. Our data science consulting practise
excels in the design and implementation of machine and deep learning models for
the analysis of data and media at scale.

Data Science seems to have something mystical about it, to the extent that
enterprises have almost come to consider it as a magical solution to impossible
problems. The reality is that behind DS there are tons of unfashionable data
engineering and analysis tasks, before statistical models can be applied to the
problem. 

Our team of talented experts here at Data Language can leverage years' worth of
technical excellence to ensure your AI solution can actually be scaled and
delivered at the highest standards of software engineering.

Understand your data 
Unlock the full potential of your data through early discovery, analysis, and
visualisation.

Learn fast
With so many variables to tweak, rapid feedback and short iteration cycles are
imperative to an effective delivery.

Models are software
As with any piece of code, statistical models also benefit from engineering best
practices for delivering at scale, with controlled total cost of ownership.

--

Read our white paper on rethinking how you do data science in your organisation:

 
[https://datalanguage.com/static/whitepapers/rethinking-data-science-in-the-enterprise.pdf]

Jamie Oliver uses Luminery

Related work

Luminery Contextual Commerce "Product Moments"

Luminery "How To" Video Moments.

Related work