The Basics

What is ADML?

Analytics Design Markup Language (ADML) is a markup language based on JSON that encompasses the set of processes required to develop and deliver a successful data product. ADML is used to capture the context and codify the outcomes, assumptions, data requirements, resources, hypotheses and learning that comprise the data product, as well as the interrelationships between these components.

It relates the data product to its intended and realised business benefit value. The markup language provides a flexible mechanism for defining a schema for the lifecycle of a data product built to address a specific issue and it ensures the resulting data product aligns with an organisation's strategic imperatives or operational obligations. It has proved useful in applications for data products that produce analytical dashboards and machine learning models. In addition, it facilitates a common language for the component processes required to build a data product.

What is a Data Product?

We define a data product as a configuration of data that can be consumed to solve a particular problem.

A data product embeds business requirements, design thinking and intended outcomes. It is important to note that much of the time people start analysis with a data product in mind. We assert that, regardless of whether this is the case, ADML can be applied to refine the definition of the product being developed. In the instances where the analysis is less focused, ADML encourages those who use it to align their designs and artifacts with a defined outcome to be delivered via the data product.

Our definition of a data product includes different types of products that might be developed. Despite the emergence of specific functions in organisations that relate to only one type of data product (such as data science teams, data analytics teams and visualisation teams), all of these can — and should — be unified by the desired outcomes and associated hypotheses of the business problem being addressed.

Data Product vs Outcome Focus

Irrespective of whether the development of a data products starts with the data product itself or with the end outcome in mind, ADML is positioned to ensure the data product is aligned with the outcome. With ADML, data products are developed with the outcome and context of the problem firmly in mind. Equally, by starting from the outcome desired, data products can be developed to ensure the outcome is achieved.

What ADML is NOT

ADML is not a data modelling methodology. It is not a data governance methodology. It does not prescribe data management techniques.

Why use ADML?

There are many benefits of using a structured approach such as ADML to improve the development of data products and address problems using data assets. They include:

• Developing a reusable library of assets to improve the speed and reduce the cost of development;
• Capturing the expected contribution of a data product to business value to help with managing resources and giving priority to the right data investments;
• Linking data lineage to context (i.e., problems, imperatives and business value contribution) so that change processes can be managed in line with their value;
• Ensuring data products are developed that focus on solving business issues;
• Embedding the value of data to help organisations realise the value of data assets;
• Codifying organisational learning and facilitating continuous improvement;
• Combining business requirements with design thinking to ensure data products deliver value and are aligned with the intended outcome and organisational needs or issues, thereby mitigating unintended consequences of data product use;
• Linking with complementary methodologies such as CRISP-DM while avoiding lock-in and constraints; and
• Implementable at any stage during a data asset's lifecycle by any type of user archetype, reducing risk for new and in-flight projects alike.

Limitations

As with any framework, the adoption of ADML is not a panacea. It will require additional governance, resources, commitment to the rituals and an agreed location in which to store the ADML schema and associated artifacts.

Prerequisites

An organisation will be best suited to benefit from adopting ADML when they satisfy one or more of the conditions listed below.

1. There is organisational alignment in the development of data products using cross-functional teams;
2. It is investing in, or considering investing in, data assets;
3. There is a desire to solve strategic, tactical and operational issues using data; and
4. It has an appetite to increase its analytics maturity, improve return on data investment or increase the number of data-informed decisions made.

How does it work?

ADML is a framework to manage the design and specification of data analytics assets, which we term “data products”. It is not aligned with any particular technology, operating model or project management methodology.

It is underpinned by:

• A design methodology; and
• A technical schema to capture outputs of the design process.

By design methodology, we mean the processes and procedures to follow.

By technical schema, we mean the standardised data structure to capture the outputs of the design methodology. The markup language adheres to this technical schema.

How much does it cost?

ADML is free for anyone to implement, or software companies to adopt as a standard under a Creative Commons Attribution-NoDerivatives 4.0 International License.

We ask that ADML is NOT ADAPTED in anyway.

If you do use ADML please ensure you attribute appropriately by including the following text "Powered By ADML"