BIG DATA, DATA SCIENCE & DATA MANAGEMENT
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.
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