Agile Data Science Blog

The GenAI Life Cycle

The GenAI Life Cycle

The GenAI life cycle delineates the steps for creating AI-based applications, such as chatbots, virtual assistants or intelligent agents. GenAI (or Generative AI), refers to advanced machine learning systems capable of creating content, such as...

read more
Agile AI

Agile AI

As AI practitioners we would like a blog post exploring some underlying concepts and practical tips of Agile AI. So that we can more effectively drive results with our AI initiatives   Agile is a flexible approach for managing initiatives in...

read more
The Data Science User Story

The Data Science User Story

To drive effective data science outcomes, data scientists and stakeholders need to jointly understand the deliverable requests and their value. And yet, communication breaks down whenever data science team members and stakeholders interpret the...

read more
The Data Science Maturity Model

The Data Science Maturity Model

A data science team’s process is a key driver to their projects’ success. However, as will be discussed below, there is not an existing AI / data science maturity model that is focused on how to evaluate (and improve) a team’s process. Hence, after...

read more
What is a Data Science MVP?

What is a Data Science MVP?

By their nature, data science products are risky. Building a Data Science MVP can reduce that risk by focusing the early development life cycle on discovery and learning. The onset of a data science project has a lot of unknowns – on the data,...

read more
What is Agile Business Intelligence?

What is Agile Business Intelligence?

Data by itself is often useless. However, by transforming the data into human understandable insights, organizations can harness the power of data to make meaningful decisions that drive outcomes. But making this happen is easier said than done....

read more
Data Driven Scrum – 5 Key Questions

Data Driven Scrum – 5 Key Questions

Data Driven Scrum (DDS) enables lean data science project agility and addresses the key challenges that have been identified when using Scrum in a data science context. This post describes five common questions teams might encounter when trying to...

read more
What is the Data Science Process?

What is the Data Science Process?

A data science process can make or break a team. Indeed, we see time and time again that many of the reasons behind data science project failures are not technical in nature but rather stem from process-related issues. Simply throwing compute power...

read more
Data Driven Scrum

Data Driven Scrum

Data Driven Scrum™ (DDS) is an agile framework specifically designed for data science teams.In short, DDS aims to improve a data science team's collaboration and communication. The Data Science Process Alliance created Data Driven Scrum to address...

read more
Data Driven Agile With Data Driven Scrum

Data Driven Agile With Data Driven Scrum

When teams try to have a data driven agile approach, they often try to use an existing framework, such as Scrum or Kanban. Yet, there are key challenges teams have in leveraging these frameworks. Therefore, the Data Science Process Alliance created...

read more
Kanban for Data Science

Kanban for Data Science

Kanban, which literally means billboard in Japanese, started as a supply chain and inventory control system for Toyota manufacturing in the 1940s to minimize work in progress and to match the supply of automotive parts with demand. Kanban is simple...

read more
Scrum for Data Science

Scrum for Data Science

Scrum Process Diagram (Wikimedia Commons, 2008) Given Scrum's popularity with software teams, it's no surprise that many organizations are turning to Scrum for data science product development. But, Does Scrum work for Data Science?Well...results...

read more
What is Agile Business Intelligence?

Agile Data Science

Conceptually Agile and data science are a great match. Most notably, both Agile and data science emphasize the same underlying concept that you build something, you learn from what you delivered, and then you improve it based on what you learn. And...

read more
Share This