Agile Data Science Blog
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...
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...
Managing Generative AI Projects
Not stopping at merely utilizing apps like ChatGPT, many companies are building, or exploring the possibility of building, their own Generative AI bots for internal use as well as for use by their clients. However, since Generative AI is so new,...
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...
Data Science Agility: A Benchmark
Do data science teams use (or think they use) an agile data science team process framework? To help find out, my student (Sucheta Lahiri) recently presented an academic paper that reported on data science agility across 16 organizations.The key...
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...
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,...
The Machine Learning Process
The machine learning process defines the flow of work that a data science team executes to create and deliver a machine learning model. In addition, the ML process also defines how the team works and collaborates together, to create the most useful...
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....
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...
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...
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...
What is Agile Data Science?
Exploring how to “do” agile data science
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...
Vertical vs Horizontal Slicing Data Science Deliverables
Traditional software approaches favor developing software layer-by-layer (horizontal slicing) while software agilists strive to deliver software by thin end-to-end value streams (vertical slicing). …but what makes sense for data science? Consider a...
This blog ain’t much but Isaac Newton would like it
At the time of posting this, this blog has a generic template, broken links, no logo, only one post, and is all around…well, bad. So why would we voluntarily share with the world something so embarrassing? Because Jeff and I have been...
Data Science Methodologies and Frameworks Guide
The Need for Data Science Methodologies and Frameworks The field of data science has matured greatly in the past decade. And yet, teams often struggle to apply an appropriate data science methodology and team-based collaboration framework. Consider...
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...
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...
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...
