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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Defining Areas of Responsibility for Your Data Science Team

Scottish novelist and folklorist Andrew Lang once wrote, “I shall try not to use statistics as a drunken man uses lamp-posts, for support rather than for illumination.” Unfortunately, many organizations that consider themselves...

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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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Working Together on a Data Science Team

As I explained in a previous post, Building a Top Notch Data Science Team, a data science team should consist of three to five members, including the following:

  • Research lead: Knows the business, identifies assumptions, and drives questions....
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Building a Top Notch Data Science Team

Building a data science team is not as simple as hiring a database administrator and a few data analysts. You want to democratize your data — you want the organization’s data and the tools for analyzing it in the hands of everyone...

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Steering Clear of Common Data Science Pitfalls

Many people have the misconception that science is equivalent to truth. In fact, people often cite science as the authority on a specific issue. They seem to believe that anyone who challenges scientific claims is challenging the truth and in...

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Five Approaches to Statistical Analysis

Data science teams capture, store, and analyze data to extract valuable information and insight. In recent posts, I focused on capturing and storing three types of data — structured, semi-structured, and unstructured — and I encouraged...

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Big Data or Big Garbage?

Software developers have a popular saying, “Garbage in, garbage out.” They even have an acronym for it: GIGO. What is true for computer science is true for data science, as well, and perhaps even more so — if the data...

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Distinguishing the Three Data Types

When organizations capture and analyze big data to extract knowledge and insight from it, they often must aggregate three diverse data types:

  • Structured
  • Semi-structured
  • Unstructured

In this post, I highlight the differences among these three...

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