Agnostic Data Analytics Management Platform
What is A.D.A.M.?
Every organisation generates data, and the volume, variety and importance of that data continue to grow.
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Data may come from enterprise applications, operational systems, databases, financial systems, customer platforms, websites, mobile applications, sensors, devices, APIs, files and external sources. As organisations expand, data becomes increasingly distributed across departments, business units, applications and technology environments.
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The challenge is no longer simply collecting data. It is bringing data together, understanding it, improving its quality, governing it and making it consistently available.
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ADAM, the Agnostic Data Analytics Management Platform, provides the foundation for this entire data journey. ADAM brings together data from multiple sources and takes it through integration, ingestion, profiling, quality management, transformation, governance and modelling before making it available for reporting, visualisation, analytics and data science. This creates a common data foundation across the organisation.
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Instead of different departments maintaining their own versions of information or using different definitions and business rules, ADAM enables organisations to establish common definitions, governed datasets and a single source of truth for analytical and decision making purposes. A single source of truth does not mean replacing existing systems of record. Each operational system can continue to serve its intended purpose while ADAM brings information from authoritative sources together and creates a trusted analytical view across the organisation.
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Whether a private enterprise, multinational corporation, government ministry, government agency or public sector organisation, ADAM provides the foundation to turn fragmented information into meaningful insight.
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Connect the data.
Govern the information.
Create the insight.
Enable better decisions.
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ADAM connects these stages, helping organisations move from data to trusted information, from information to insight, and from insight to intelligence.
Agnostic by Design
The word Agnostic is fundamental to ADAM.
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ADAM is not tied to a particular cloud provider, database, operating system, programming language or technology vendor. It can operate fully on premise, in the cloud or across a hybrid environment, allowing organisations to determine where and how their data platform should operate.
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ADAM follows an open and modular architecture, allowing organisations to select technologies according to their requirements, existing investments, technical standards and future direction. This also means that organisations do not have to replace everything simply because they are introducing a new data platform.
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ADAM can work alongside existing enterprise systems and technologies while providing a common data foundation across the organisation. Whether the organisation operates a traditional data centre, a public cloud environment, a private cloud or a combination of different environments, ADAM can be configured around the organisation's architecture.
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Your data architecture should adapt to your organisation, not constrain it.
ADAM creates a controlled data journey:
Data Sources → Integration → Ingestion → Processing → Storage → Analytics
From Data Sources to Data Integration
Data comes from everywhere.
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An organisation may have hundreds of systems generating information across finance, human resources, operations, sales, customer service, supply chain, manufacturing, asset management, procurement and other business functions. Government organisations may have information distributed across ministries, agencies, departments, public platforms and external stakeholders.
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ADAM provides the integration and ingestion capabilities required to bring these different sources into a common data environment.
Data can be acquired through different integration patterns, including batch ingestion, scheduled transfers, API integration, file ingestion, change data capture, event driven integration and streaming.
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Once data enters ADAM, it can be validated, transformed, standardised, enriched and routed according to defined rules and data models.
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The approach reduces unnecessary duplication and provides a structured foundation for managing information across the organisation.
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Instead of every application independently connecting to multiple downstream systems, ADAM provides a centralised data architecture through which information can be managed and made available to authorised users and applications.
Data Quality, Profiling and Preparation
The value of analytics depends on the quality of the underlying data.
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Data from different systems often contains different formats, structures, naming conventions and levels of quality. The same customer, supplier, employee, asset, location or transaction may be represented differently across multiple systems.
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ADAM provides capabilities for data profiling, validation, cleansing, standardisation, enrichment and quality management.
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Quality rules can be applied as data enters the platform, with quality scores, exceptions and failed records captured for further action. The platform can also support quarantine and data stewardship processes for information requiring correction or review.
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This turns data quality into an ongoing discipline rather than a one time cleansing exercise. Most importantly, quality processes help ensure that the information used to build the organisation's analytical datasets is reliable enough to support business decisions.
Trusted Data Analytics Start with Trusted Data
Data can be assessed for:
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COMPLETENESS
Is the required information available?
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ACCURACY
Does the information correctly represent the underlying business reality?
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CONSISTENCY
Is the information consistent across systems and datasets?
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VALIDITY
Does the data comply with the required rules and formats?
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UNIQUENESS
Are duplicate records identified and managed?
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TIMELINESS
Is the information sufficiently current for its intended purpose?
The A.D.A.M. Medallion Architecture
At the heart of ADAM is a Medallion Architecture that progressively transforms data from its original state into trusted, business ready information.
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RAW LAYER
The Raw Layer retains data in its original or received form. This provides a historical reference for traceability, reconciliation, recovery and future processing. It allows organisations to retain the original information before business rules and transformations are applied.
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CONFORMED LAYER
The Conformed Layer is where data is validated, cleansed, standardised, transformed and harmonised. Information from different systems is brought into common structures, definitions and relationships so that it can be consistently understood across the organisation. This is a critical stage in establishing a single source of truth. Different systems may remain the authoritative source for their respective operational records. ADAM does not need to replace those systems. Instead, it brings information from those authoritative sources together and creates a governed and consistent analytical view.
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CONSUMPTION LAYER
The Consumption Layer contains data prepared for specific business and analytical requirements. This can include enterprise datasets, data warehouse structures, data marts and analytics ready datasets for dashboards, reporting, business intelligence and data science.
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As data progresses through the layers, it becomes increasingly structured, governed, trusted and aligned to business requirements.
The result is a common analytical foundation from which different departments can consume consistent information.
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Metadata, Lineage and Data Governance
A data platform should not only know where information is stored. It should know what the information means, where it came from, who owns it, how it has changed and where it is being used.
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ADAM provides metadata and governance capabilities that help organisations establish this understanding across their data environment.
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Metadata can include technical information such as schemas, data types and constraints, operational information such as source, refresh frequency and volume, and business information such as definitions, ownership, purpose and stewardship.
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A central data dictionary and business glossary can establish common terminology across departments.
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Data lineage provides visibility from the original source through the integration and transformation processes to the final analytical dataset.
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This is particularly important when creating a single source of truth.
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Users should be able to understand not only the number presented in a dashboard, but also where the number originated, which data was used, which transformations were applied and which business rules were responsible for producing it.
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Governance can also cover data classification, access controls, protection policies, audit capabilities and stewardship.
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The result is a data environment where information is:
Defined. Governed. Traceable. Protected. Trusted.
From Data Lake to Data Warehouse to Data Marts
ADAM supports the progression from raw data storage to structured analytical information.
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The Data Lake provides a flexible and scalable environment for retaining large volumes of raw and processed information from different sources.
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The Data Warehouse provides structured and integrated information designed for enterprise reporting and analytical workloads.
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The Data Mart provides focused datasets designed around specific business functions, departments, domains or analytical requirements.
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Together, these components create a structured analytical ecosystem:
Data Sources → Data Lake → Conformed Data → Data Warehouse → Data Marts → Analytics
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The data warehouse and conformed data structures are particularly important when an organisation needs a consistent enterprise view of its information. For example, different departments may previously calculate revenue, customer numbers, operational performance or service levels using different datasets and business rules. ADAM can establish governed datasets and common definitions so that the organisation can work towards a single source of truth for enterprise analytics. Departments can still have their own specialised data marts, but those data marts can be derived from the same governed foundation. This provides a balance between enterprise consistency and departmental flexibility.
From Data to Dashboards and Visualisations
The purpose of a data platform is ultimately to make information useful. ADAM prepares governed and integrated data for dashboards, reporting, visualisation and business intelligence.
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Different users require different views of information.
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Executives may require strategic performance indicators and enterprise KPIs.
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Management may require business performance analysis and trend monitoring.
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Operational teams may require detailed information about their day to day activities.
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Analysts may require the ability to explore data and identify relationships, trends and opportunities.
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ADAM provides the common data foundation behind these different analytical experiences. When dashboards and reports are built from governed datasets and common business definitions, organisations can reduce the risk of different reports producing different answers to the same question.
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This is where the concept of single source of truth becomes particularly powerful.
The dashboard is not the source of truth.
The governed data behind the dashboard is.
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ADAM provides that governed foundation so that different reports, dashboards, analytical applications and decision making processes can work from consistent information.
Sample Dashboard for Smart City
Sample Dashboard for a Telco
Sample Dashboard for Energy Co
Sample Dashboard for a Bank
Sample Dashboard for Property Co
Sample Dashboard for Government
Data Modelling, Data Science and Predictive Analytics
ADAM goes beyond traditional reporting. Once information has been integrated, cleansed, governed and structured, it can be prepared for more advanced analytical applications.
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ADAM supports data modelling and the preparation of analytics ready datasets for data science, machine learning and predictive analytics. Data models can bring together historical information, operational information and relevant business attributes to support different analytical use cases.
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PREDICTIVE ANALYTICS
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Identify patterns and determine what is likely to happen next.
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FORECASTING
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Estimate future demand, capacity, performance or operational requirements.
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TREND ANALYSIS
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Understand how performance and behaviour change over time.
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ANOMALY DETECTION
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Identify unusual patterns or behaviour that may require attention.
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MACHINE LEARNING
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Develop analytical models using structured and governed organisational data.
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WHAT IF ANALYSIS
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Explore potential outcomes based on different assumptions and scenarios.
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The quality of predictive analytics depends heavily on the quality and consistency of the underlying data. This is why ADAM's integration, data quality, governance, metadata and data modelling capabilities are important foundations for data science. A model built from fragmented or inconsistent information can produce misleading results. A model built from governed, well understood and properly prepared data has a much stronger foundation.
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From trusted data, organisations can build trusted models.
This allows organisations to progress from understanding what happened, to understanding why it happened, to anticipating what may happen next.
A.D.I.B., A.D.A.M. and A.I.D.A.
ADIB, ADAM and AIDA are complementary technologies within our broader data and artificial intelligence ecosystem. They address three different stages of the information and intelligence journey. ADIB connects and exchanges information across operational systems. ADAM brings that information together for data management, analytics and visualisation. AIDA builds on that governed data and analytics foundation to provide an intelligent, conversational experience through Generative AI. Together, they create a progression from connected operations, to governed data, to analytics, to intelligent interaction.
