Data Science Life Cycle 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...
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,...
What is the AI Life Cycle?
In the rapidly evolving world of artificial intelligence (AI), project management can be as complex as the technology itself. A staggering number of AI projects fail, not due to a lack of technical prowess, but because of ineffective project...
OSEMN Data Science Life Cycle
Different data scientists have different processes for conducting their projects. And different types of projects require different steps. However, most data science projects flow through a similar workflow. One popular representation of this...
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 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...
What is a Machine Learning Life Cycle?
Explore what is a Machine Learning Life Cycle, and how it compares with a Data Science Life Cycle (by looking at OSEMN and CRISP-DM)…
What is SEMMA?
The SAS Institute developed SEMMA as the process of data mining. It has five steps (Sample, Explore, Modify, Model, and Assess), earning the acronym of SEMMA. You can use the SEMMA data mining methodology to solve a wide range of business problems,...
KDD and Data Mining
What Is the KDD Process? Dating back to 1989, the namesake Knowledge Discovery in Database represents the overall process of collecting data and methodically refining it. The KDD Process is a classic data science life cycle that aspires to purge...
What is a Data Science Life Cycle?
A data science life cycle is an iterative set of data science steps you take to deliver a project or analysis. Because every data science project and team are different, every specific data science life cycle is different. However, most data...
CRISP-DM is Still the Most Popular Framework for Executing Data Science Projects
During the past few months, we conducted a poll to see what project management framework teams used to help execute their data science projects. Based on our survey of 109 respondents, nearly half of the respondents most commonly use CRISP-DM. This...
What is a Data Science Workflow?
A data science workflow defines the phases (or steps) in a data science project. Using a well-defined data science workflow is useful in that it provides a simple way to remind all data science team members of the work to be done to do a data...
CRISP-DM for Data Science Teams: 5 Actions to Consider
While there is no standard process for a team to use when working on a data science project, CRISP-DM (CRoss-Industry Standard Process for Data Mining) is one framework that is often considered for data science projects. Perhaps because of this,...
Data Science Project Roadmap Example
Various process models and frameworks such as CRISP-DM, TDSP, Domino Data Labs Life Cycle, or Data Driven Scrum describe how to execute a data science project. While useful, such models do not explicitly explain how to communicate with stakeholders...
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...
Lessons from 20 Data Science Teams
What can you learn if you observe data science teams across 20 large companies? I asked Mac Steele, Director of Product at Domino Data Lab, to find out. Mac combined the lessons he learned from observing data science teams with concepts from...
Domino Data Science Life Cycle
The Domino Data Science Life Cycle is a modern life cycle approach. Domino Data Lab, a Silicon Valley vendor that provides a data science platform, crafted its data science project life cycle framework in a 2017 whitepaper. The paper wraps its life...
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...
What is TDSP?
Team Data Science Process If you combine Scrum and CRISP-DM, you will get something that looks like Microsoft's Team Data Science Process. Launched in 2016, TDSP is “an agile, iterative data science methodology to deliver predictive analytics...
Research and Development
What is a Data Science R&D Approach? The data science process can be viewed as a research endeavor that transitions into an engineering project. As such, some organizations combine traditional research methodologies with modern agile...
Bimodal: Agile-Waterfall
Agilists juxtapose waterfall as the antithesis of agility. However, Eric Stolterman, Senior Executive Associate Dean at Indiana University, believes that crystalline processes such as waterfall and liquid processes such as Scrum should...
What is CRISP DM?
The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the base for a data science process. It has six sequential phases: Business understanding – What does the business need? Data understanding – What data...
What is Waterfall?
Waterfall, also referred to as the classic life cycle or traditional project management, originated from manufacturing and construction and was applied to software engineering projects starting in the 1960s. A waterfall project flows through...
Hybrid Approaches
Without well-known, comprehensive project management approaches specific for data science, teams often combine elements from two or more approaches to cater to their own needs. Although combining multiple approaches can be challenging, they allow...
