Resources

The ADML White Paper

We introduce Analytics Design Markup Language (ADML), a methodology which encompasses the set of processes required to develop and deliver a successful data product and an accompanying markup language based on JSON. 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.

Download the White Paper today to gain a better understanding of methodology, the process of implementation and how ADML can mean the difference between an untamed, under utilised analytics wilderness and true "data-first" collective approach to deliver successful data products across your enterprise.

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ADML Training Videos

ADML (Analytics Design Markup Language) created by BizData's own Nadav Rayman and Dr James Pearce has started conversations across the world, as more and more data-centric organisations understand the importance of methodically governing and managing mission critical data assets.

To help better understand how ADML works, particularly the four "Design Rituals" that underpin the entire ADML framework, we've put together a series of instructional videos to get you started.

Introducing ADML

Discover how ADML can mean the difference between an untamed, under utilised analytics wilderness and true "data-first" collective approach to deliver successful data products across your enterprise.

Ritual 1: Information Design

Introducing the first key ritual to follow when implementing ADML: Information Design. The purpose of this ritual is to define the semantics of a business area, identifying the information needs to support monitoring of performance and descriptive analytics. The primary “wave” that this ritual is concerned with are the business needs that elude measurement.

In a nutshell, Information Design is about defining a common language for describing what a business does and what you want to measure.

Ritual 2: Hypothesis Design

Introducing the 2nd key ritual to follow when implementing ADML - Hypothesis Design. The purpose of this ritual is to define the levers and constraints of a particular business issue, identifying the needs to support data-driven intervention, often associated with the practice of statistical analysis, machine learning and prescriptive analytics.

Ritual 3: Data Readiness

Introducing the 3rd design ritual in ADML: Data Readiness The purpose of this design ritual is to validate whether data exists in a form that will support the analytics objectives. The primary “wave” that this ritual is concerned with are the resources that capture or record data.

Before a data product can be built, the feasibility of fulfilling stakeholder expectations needs to be validated. Based on the information design and hypothesis design, sources of data need to be identified and tested for its completeness and utility.

Ritual 4: Learning

Introducing the fourth and final design ritual in ADML: Learning The purpose of this design ritual is to evaluate the performance of a data product. The primary “wave” that this ritual is concerned with are the outcomes that can be related to a data product.

A key reason for this design ritual is to ensure corporate memory of why the data product was developed in the first place, and what challenges were faced and overcome in the past. This knowledge can be used to refine assumptions, update expected benefits and inform future decisions.