Tag Archives: methodology

The Goal is Data Products: Now How Do We Get There?

The primary output of data science is data products. Data products can be anything from a list of recommendations to a dashboard to a single chart or any other product that aides in making a more informed decision. In the end, data science should produce some usable results, and those results are the data product. The process used to created those data products needs a bit more formalization. Call it a: methodology, process, lifecycle, or workflow; but it needs to exist.

Dr. Kirk Bourne provided some thoughts in July 2014 with his article, Raising the Standard in the Big Data Analytics Profession. Data science needs some standards and possibly even a workflow, but the focus on data products cannot be lost.

burndown chart

Data Science is not Software Engineering

First, data science is often treated as software engineering because code is written. However, they are not the same thing. Agile methods, waterfall, and scrum are not pluggable methodologies that can be used with data science. Data science is more science and less engineering; therefore it should follow a more scientific method.

Existing Data Science Workflows

Luckily, some options already exist for data science. Much like software engineering, there is not a magic workflow that fits every project. The goal is to find a workflow that best fits the needs of the current project.

CRISP-DM

The most popular and oldest method is CRISP-DM. CRISP-DM was designed for data mining projects, which is closer to data science than software engineering, but still not exact. The 6 steps of CRISP-DM are:

  1. Business Understanding
  2. Data Understanding
  3. Data Preparation
  4. Modeling
  5. Evaluation
  6. Deployment

Data Science Project Lifecycle

The The Data Science Project Lifecycle is a recent modification/improvement of CRISP-DM with a bit more of an engineering focus. The steps can be seen as:

  1. Data acquisition
  2. Data preparation
  3. Hypothesis and modeling
  4. Evaluation and Interpretation
  5. Deployment
  6. Operations
  7. Optimization

Data Science Workflow

The Data Science Workflow: Overview and Challenges was presented on the ACM blog in 2013. It was part of a dissertation by Philip Guo. Here are the steps:

  1. Preparation
  2. Analysis
  3. Reflection
  4. Dissemination

Those are 3 options of workflows for data science. They are not the only options. Feel free to modify the workflows to best suit the project. It will be exciting to see the new workflows for data science that will be created in the near future. It will also be fun to see which ones turn out to be the most beneficial.

One thing a data product must do is help answer a question. Thus, a logical staring point for data science is a good question. Just don’t let the focus of the workflow come down to the process, which is often the case in software engineering. Let the focus be on data products.


Note:
I have previously written 2 posts on this topic, and I don’t think either post gets the methodology exactly correct.

The Data Science Methodology

Data Science Methodology

  1. Problem Formulation – First, identify the problem to be solved. This step is easily overlooked. However, many dollars and hours have been spent solving the wrong problems.
  2. Obtain The Data – Next, collect new data and/or gather the data that already exists. In almost all cases, this data will need to be transformed and cleansed. It is important to note that this stage does not always involve big data or a data lake.
  3. Analysis – This is the part of the process where insight is to be extracted
    from the data. Commonly, this step will involve creating and optimizing statistical/machine learning models for prediction, but that is not always necessary. Sometimes, the analysis only contains graphs, charts, and basic descriptions of the data.
  4. Data Product – The end goal of data science is a data product. The insight from the Analysis phase needs to be conveyed to an end user. The data product might be as simple as a slideshow; more commonly it is a website dashboard, a message, an alert, or a recommendation.

Can you think of anything the methodology is missing?


Note: This post is similar to the Data Scientific Method which I blogged about nearly 2 years ago.