Conducting Data Analytics in Sprints

In my previous post, Building a Data Science Life Cycle (DSLC), I encourage you to adopt a structure for your data team's activities that is conducive to the type of work it does — exploration. I refer to this structure as the Data Science...

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Building out a Data Science Life Cycle (DSLC)

A project lifecycle can be a useful tool for structuring the process that a team follows. (A lifecycle is a repeating series of steps taken to develop a product, solve a problem, or engage in continuous improvement.) It functions as a...

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Defining Success Criteria for a Data Science Project

In a previous post, Comparing Software Projects to Data Science Projects, I point out some of the key differences between traditional projects and data science projects. One of these differences is in the deliverables. With traditional projects,...

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Recognizing the Challenges of a Data Science “Project”

In a previous post, Conducting a Data Science "Project", I pointed out some of the key differences that separate data science from traditional project management. While traditional project management is focused more on goals, planning, and...

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Comparing Software Projects to Data Science Projects

In my previous post, Conducting a Data Science "Project", I pointed out the differences between project management and data science. These differences are summarized in the following table:

Project Management

Data Science

Planning

...

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Conducting a Data Science “Project”

The heartbeat of most organizations can be measured in projects. Various teams across the organization set goals and objectives, develop plans for meeting those goals and objectives, and then implement those plans in the hopes of executing their...

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Common Data Science Team Pitfalls and How to Avoid Them

In a previous post, Building a Top-Notch Data Science Team, I recommend creating a small team of three to five individuals consisting of at least one research lead, a data analyst, and a project manager. The research lead is primarily responsible...

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Approaching Data Analytics with the Right Mindset

Many organizations think that data science is solely about crunching numbers. Put a bunch of analysts in room, give them access to the data, and within a reasonable period of time, they’ll report back with their numbers and graphs revealing...

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Building a Cycle of Insight on Your Data Science Team

Large organizations have numerous departments or teams that perform different functions, including Research and Development (R&D), Production, Purchasing, Marketing, Human Resources (HR), and Accounting and Finance. While many teams respond...

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Breaking Down Data Silos in Your Organization

One of the biggest challenges your data science team is likely to encounter is gaining access to all of the organization’s data. Many organizations have data silos— data repositories managed by different departments and isolated from...

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