We have an independent Big Data, Data Science, Data Analytics and Data Management practice. This practice also includes Enterprise architecture and IT strategy. We provide advisory, consulting, engineering and support services to provide an enterprise solution for Data. We want you to think of Data as a Service (DaaS) and help you monetise your data.
Whilst we are focused on the Capital Markets & Banking, the practice covers all verticals, but we have particular experience in FinTech, Retail, Manufacturing and Energy.
Big Data can be described as having one or many of the following characteristics: Volume, Variety, Velocity, Veracity (the quality) and finally the reason for all the effort, Value.
We work usually in an Agile (Kanban) way (to deliver BI functionality) but in a more structured way to create the project data analysis and data blending deliverables.
We can help you realise a wide range of insights and benefits (the Value), such as:

· Operational optimisation
· Actionable intelligence
· Identification of new markets, customers
· Improve predictions
· Fault and fraud detection
· Improved decision-making

Where we can help…


The identification of data sources and the ability to acquire, prepare, blend and ingest that data in business real-time:

· Application of best practice data management processes, such as sound data analysis techniques
· Data governance embedded into every phase and delivery through – Project initiation, Planning, Execution and Delivery, specifically assuring data quality.
· Profiling/Quality processes, auditable and transparent data lineage to provide trustworthy data.
· Using various technologies acquire data using ETL, streaming or real-time data capture.
· Data Types: transactional, time- series, image/voice/video, social media, documents/emails, biometric, geospatial.



Data Modelling, storing data in structured, semi or un-structured formats – but all data accessible for reporting, BI, and analytics:

· Documentation, data models, Meta-data management. Data Catalogue/dictionary
· The best storage engine – File System, Relational, NoSQL, Graph, In-Memory (MemSQL)
· Data Lake = DataWarehouse + NoSQL (Mongo, Redshift, Cassandra, Neo4j)+…
· HADOOP and Spark eco-system design, install and implementation – which distribution or roll your own?
· Data Virtualization – move or keep the data in-situ?
· Deployment: Cloud, in-house, Hybrid.


Visualisation/Data Exploitation:

Extract the value from the data, whether it is via reports, dashboards, analytics, BPM/BAM or actionable intelligence:

· Security – access, availability, archiving, regulatory
· MDM/Ref Data
· Advanced analytics: Predictive and prescriptive, PMML, plus machine learning
· Data Scientist Sandbox
· Self-service BI & Prep
· CLV & attribution modelling
· Personalisation & Customer targeting
· Click-stream analysis
· Search – Solr
· Data/Text Mining & NLP (sentiment analysis)
· KPIs and metrics
· PowerBI, Qlik, Tableau
· R, Python (+libs), KNIME, Alteryx
· Java, C++, .NET, Scala

Richard DaCosta
Head of Data Management
Richard has a broad industry background across Digital Finance, Energy, Investment Banking, Government Departments, Manufacturing and Retail over the last 30 years. He has worked at Director level in many blue chip companies as well as feeling at home doing hands on project management and analysis. In addition, he has worked as a Chief Architect responsible for a portfolio of 100s of applications. He has a proven track record of delivering corporate design frameworks and standards. More recently he has been the Chief Data Officer (CDO) at a Digital Finance company responsible for big data, data management, data science and BI, including the marketing DMP.
Contact RichardMore about Richard

Other Practices:

· Front Office

· Middle Office & Back Office

· Risk & Regulatory

· Quality Assurance

> Data Science

· Murex Practice

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