As an enabler of evidence-based decision making, data is your most valuable technology asset and must be treated as such to derive maximum business value. ES365 assists in establishing your data governance structure, modelling how data flows into and through your organisation, how it is stored, how its quality can be maintained and improved. We also ensure your data can be secured, transformed and visualised.

Data Architecture

Enterprise Data Architecture (EDA)

Refers to the governance, tools and blueprints that define the structure, use and exchange of data inside and outside an organisation in support of the business strategy and legal operating environment. EDA is used to guide integration, quality enhancement and successful data delivery.

Business Intelligence

Business intelligence (BI) leverages software and services to transform data into actionable insights that inform evidence-based decision-making.

Data Infrastructure Design

Refers to the digital infrastructure required to govern, store, manipulate and share data within an organisation.

Enterprise Data Event Management & Flow

Refers to the design and management of how internal or external system events can be used to capture useful data for later use such as in analytics that may lead to competitive edge.

Data Analytics

The science of analyzing raw data in order to make conclusions about that information.

Master Data Design

Master data ensures that reusable business data is centrally managed as a single version of the truth in an organisation. Master data design refers to how changes to this data are controlled, accessed or disseminated to consuming systems on a read-only basis.

Data Modeling Data

Data modelling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.

Data Interoperability

Data interoperability addresses the ability of systems and services that create, exchange and consume data to have clear, shared expectations for the contents, context and meaning of that data.

Data Transformation

Data transformation is the process of changing the format, structure, or values of data. For data analytics projects, data may be transformed at two stages of the data pipeline.

Data Security

Data security is the practice of protecting digital information from unauthorized access, corruption, or theft throughout its entire lifecycle.

Data Governance

Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals. Data governance defines who can take what action, upon what data, in what situations, using what methods

Data Principles/Policies

Are elements of data governance, Principles are fundamental truths that guide decision-making, e.g. data is an asset.
Policies are the rules that staff abide by as they carry out their various responsibilities.

Master Data Management

Master data management is a technology-enabled discipline in which business and Information Technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.

Data Standards/ Procedures

A data standard is a technical specification that describes how data should be stored or exchanged for the consistent collection and interoperability of that data across different systems, sources, and users. Procedures are the instructions or steps that describe how to complete a task or do a job.

Legal & Regulatory

Refers to the legal and regulatory requirements when and what data may be obtained, stored and disposed of by the business. In South Africa the POPI act provides significant guidance in this regard.

Data Taxonomy & Cataloguing

A Data Taxonomy is simply a hierarchical structure separating data into specific classes of data based on common characteristics. A data catalogue is an organized inventory of data assets in the organization. It uses metadata to help organizations manage their data. It also helps data professionals collect, organize, access, and enrich metadata to support data discovery and governance.

Data Quality Management

Data quality management refers to the data governance that guides data quality, the business rules that determine the acceptability of data to the organisation and the tooling that involves the automated monitoring, evaluation, and correction of data within the organisation.

Data Science

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms … in managing a digital data collection for the purposes of obtain previously unknown insights into organisational data.


Exploration is the act of searching for the purpose of discovery of information or resources.

Ingestion/Cleansing/ Transformation

Ingestion — preparing data for analysis. Data cleansing – also known as data cleaning or data scrubbing – fixes, or if necessary, removes common data errors, including missing values and typos. Data transformation is the process of changing the format, structure to facilitate integration with data consuming systems.

Data Visualisation

Refers to the process of tooling and the design of how best to depict data for the purposes of facilitating unambiguous understanding of the data for the purposes of decision-making.

Machine Learning

Machine learning (ML) makes use of computer algorithms that iteratively process datasets and take into account the outcomes of the previous iteration to automatically improve on the previous result through experience.

Our Experience

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Enterprise Solution Architecture

Enterprise Process Transformation

Software Architecture, Design & Development

Managed Resources