Introducing Information Management for NoSQL

As Gartner warns in the report, adopting NoSQL storage can result in Information … read more

Introducing Quantitative Enterprise Architecture

It is rather ridiculous that the discussion on Data Driven Enterprise did not … read more

OWL Modelling for Information Architects

Business Abstraction delivered OWL training customised for experienced Information Architects. The target audience … read more

Data-driven Enterprise

Data Centric approach makes the most pressing challenges of today’s IT effortless. Data Analytics and Data-driven optimisations are not additional challenges the organisation should rise up to – they are part and parcel of the original Architecture. Gradual transition to Data Centric Architecture is the better way to implement a Data-Driven Enterprise.

Business Abstraction offers training, consulting and resources for achieving deeper, more fundamental change to the enterprise using the new Data technologies.

Client-side Large Data Project Mentoring and Monitoring

New Big Data technologies represent massive jump in technologies. Many sites have no choice but to trust the implementation of their solutions to large vendors. However as said vendors also suffer from shortage of qualified resources, they prioritise sites that have own expertise to monitor their directions and capability of their consulting staff. Business Abstraction can be engaged to be trusted expect, monitoring the main vendor on behalf of the client. Our ability to review their proposals and productivity of the implementation will ensure that the vendor allocates only star performers to the client. 

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Developing Data-Centric Business Process Architecture

Business Abstraction approach to Business Process Architecture focuses on Business Motivation and Data, enabling implementation of a Data-Centric Enterprise.

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Developing Data Architecture

Integration of information across extended enterprise requires good understanding of the data the enterprise has. Capturing the overall organisation and nature of data in the enterprise is known as Data Architecture. Business Abstraction has capabilities to lead development of modern Data Architecture that take into account Big Data, Graph & Semantic Data, Unstructured Data etc.

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Implementing Semantic Metadata for Data Lakes

While the traditional approaches to Metadata may work well for databases with a couple of dozens of tables and in familiar domain, they weren't meant for the scale and complexity of Data Lakes. Fortunately, the Resource Description Framework, introduced by the creator of World Wide Web in 1997, provides facilities for highly descriptive and queryable representation of very complex datasets and conceptual domains.

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Implementing Virtual Graph

Virtual Graph is an agile way of integrating multiple complex datasets. It allows treating relational datasets, as well as Hadoop datasets accessible via Apache Hive, as an integrated, Ontology-governed Semantic Graph. Virtual Graph enables "schema on read" pattern - all data become available immediately, followed by iterative Ontology Modelling that gradually enables comfortable use of the data.

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Using Gremlin Graph Traversal Language (3 days)

As we learn to link data, enterprises naturally transition from tables to graphs. The best introduction to Graph model is through Gremlin, the standard supported by multiple commercial and open source products.

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Business Architecture for Data-driven Transformation (4 days)

The course is designed for Architects approaching or evaluating fundamental transformation of the enterprise on the basis of modern digital delivery and Big Data. It enables rethinking the enterprise from data-centric perspective. Do not wait for others to disrupt your industry - use your expertise and existing market share to be the first in.

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Business Analysis for Data Projects (4 days)

The course is designed to enable the use of experienced Business Analysts and familiar Business Analysis techniques in Data-focused projects.  It also delivers "User-level" understanding of Business Intelligence, Data Science, Big Data, Data Lakes, Artificial Intelligence and Machine Learning. It enables integration of the Data and Transaction dimensions of an enterprise.

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