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Understand how data is acquired, manipulated, and consumed.

Introduction to The Data Supply Chain

How to think about data.

In order to create digital value, we have to understand how data is structured and how it moves from system to system. Otherwise we can't figure out if it can or can't be monetized, and if it's ethical.

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Keeping data in an accessible location.

The next stage of the data supply chain is storage—recording data to a trusted location, which is both secure and easily accessible for further manipulation. Storage is often some version of the cloud, or perhaps a specific server. Sometimes it’s a flash drive or local memory on a sensor. But wherever we store data, we need to make sure that we understand how that's going to connect to other systems.

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Combining disparate datasets to create new value with big & little data.

When we aggregate data, we combine disparate data sets to create a larger data set that's greater than the sum of its parts (often referred to as Big Data). This is where exponential possibilities begin, but so does exponential complexity.

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Asking questions about the past, present and future using data.

Once data is collected, stored, and aggregated it’s time to analyze it. We examine data to extract information and discover new insights. We might append this analysis to the original data, and/or use it to discard extraneous data.

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Applications of data inside and outside organizations.

Apply the insights gained from data analysis and use it to make better decisions, which effect change or otherwise help you deliver a product or service. This stage may also involve changing or adding to the data.

Share, Sell or Network

Considerations for sharing and selling data.

During the share or sell stage we send data—or insights gleaned from data—back to its source or to third parties. There are significant ethical considerations to take into account in this stage.

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The importance of revocation and deletion strategies.

Disposal of data is an important consideration at the end of the data supply chain. It’s never the most exciting, and it doesn’t generate any immediate value, so it’s often overlooked. However, regulatory requirements and common decency both demand that we think through how data will be disposed of when it’s no longer useful.

Conclusion to The Data Supply Chain

Data's journey has only just begun.

Advanced Content

Machines That Learn

Discover mechanisms by which machines can learn.
Read the Guidebook

Data Ethics

Engage in data-centric innovation in a consistent, responsible way.
Read the Guidebook

Creating Value with Data

Understand top strategies to leverage data inside and outside of organizations.
Read the Guidebook


Connecting systems in a systematic way.
Read the Guidebook

Making Decisions with Data

Use data to make better decisions—and shift culture.
Read the Guidebook

Innovating with Data

Get started towards a data innovation program.
Read the Guidebook

Data Sources Explorer

Explore some of the many types of data which can be acquired in the Data Sources Explorer, a gallery of dataset types.
Read the Guidebook

Attributes of Data

Criteria for assessing and organizing data sets.
Read the Guidebook

Glossary of Terms

Read the Guidebook