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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Democratizing Data in Your Organization

Democratizing data involves making it available to personnel throughout an organization and providing them with the tools and training needed to query and analyze that data. In this post, I discuss the potential benefits and drawbacks of data...

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Asking the Right Data Science Questions

Albert Einstein has been credited with saying that if he had an hour to solve a problem he’d spend the first 55 minutes analyzing the problem and the last five minutes solving it. Whether Einstein actually said this is subject to debate, but...

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