Whether you manage data initiatives, work with data professionals, or are employed by an organization that regularly conducts data projects, a firm understanding of what the average data project looks like can prove highly beneficial to your career. This knowledge—paired with other data skills—is what many organizations look for when hiring.

No two data projects are identical; each brings its own challenges, opportunities, and potential solutions that impact its trajectory. Nearly all data projects, however, follow the same basic life cycle from start to finish. This life cycle can be split into eight common stages, steps, or phases:

  1. Generation
  2. Collection
  3. Processing
  4. Storage
  5. Management
  6. Analysis
  7. Visualization
  8. Interpretation

Below is a walkthrough of the processes that are typically involved in each of them.


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Data Life Cycle Stages

The data life cycle is often described as a cycle because the lessons learned and insights gleaned from one data project typically inform the next. In this way, the final step of the process feeds back into the first.

Data Life Cycle

1. Generation

For the data life cycle to begin, data must first be generated. Otherwise, the following steps can’t be initiated.

Data generation occurs regardless of whether you’re aware of it, especially in our increasingly online world. Some of this data is generated by your organization, some by your customers, and some by third parties you may or may not be aware of. Every sale, purchase, hire, communication, interaction—everything generates data. Given the proper attention, this data can often lead to powerful insights that allow you to better serve your customers and become more effective in your role.

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2. Collection

Not all of the data that’s generated every day is collected or used. It’s up to your data team to identify what information should be captured and the best means for doing so, and what data is unnecessary or irrelevant to the project at hand.

You can collect data in a variety of ways, including:

  • Forms: Web forms, client or customer intake forms, vendor forms, and human resources applications are some of the most common ways businesses generate data.
  • Surveys: Surveys can be an effective way to gather vast amounts of information from a large number of respondents.
  • Interviews: Interviews and focus groups conducted with customers, users, or job applicants offer opportunities to gather qualitative and subjective data that may be difficult to capture through other means.
  • Direct Observation: Observing how a customer interacts with your website, application, or product can be an effective way to gather data that may not be offered through the methods above.

It’s important to note that many organizations take a broad approach to data collection, capturing as much data as possible from each interaction and storing it for potential use. While drawing from this supply is certainly an option, it’s always important to start by creating a plan to capture the data you know is critical to your project.

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3. Processing

Once data has been collected, it must be processed. Data processing can refer to various activities, including:

  • Data wrangling, in which a data set is cleaned and transformed from its raw form into something more accessible and usable. This is also known as data cleaning, data munging, or data remediation.
  • Data compression, in which data is transformed into a format that can be more efficiently stored.
  • Data encryption, in which data is translated into another form of code to protect it from privacy concerns.

Even the simple act of taking a printed form and digitizing it can be considered a form of data processing.

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4. Storage

After data has been collected and processed, it must be stored for future use. This is most commonly achieved through the creation of databases or datasets. These datasets may then be stored in the cloud, on servers, or using another form of physical storage like a hard drive, CD, cassette, or floppy disk.

When determining how to best store data for your organization, it’s important to build in a certain level of redundancy to ensure that a copy of your data will be protected and accessible, even if the original source becomes corrupted or compromised.

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5. Management

Data management, also called database management, involves organizing, storing, and retrieving data as necessary over the life of a data project. While referred to here as a “step,” it’s an ongoing process that takes place from the beginning through the end of a project. Data management includes everything from storage and encryption to implementing access logs and changelogs that track who has accessed data and what changes they may have made.

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6. Analysis

Data analysis refers to processes that attempt to glean meaningful insights from raw data. Analysts and data scientists use different tools and strategies to conduct these analyses. Some of the more commonly used methods include statistical modeling, algorithms, artificial intelligence, data mining, and machine learning.

Exactly who performs an analysis depends on the specific challenge being addressed, as well as the size of your organization’s data team. Business analysts, data analysts, and data scientists can all play a role.

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7. Visualization

Data visualization refers to the process of creating graphical representations of your information, typically through the use of one or more visualization tools. Visualizing data makes it easier to quickly communicate your analysis to a wider audience both inside and outside your organization. The form your visualization takes depends on the data you’re working with, as well as the story you want to communicate.

While technically not a required step for all data projects, data visualization has become an increasingly important part of the data life cycle.

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8. Interpretation

Finally, the interpretation phase of the data life cycle provides the opportunity to make sense of your analysis and visualization. Beyond simply presenting the data, this is when you investigate it through the lens of your expertise and understanding. Your interpretation may not only include a description or explanation of what the data shows but, more importantly, what the implications may be.

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Other Frameworks

The eight steps outlined above offer an effective framework for thinking about a data project’s life cycle. That being said, it isn’t the only way to think about data. Another commonly cited framework breaks the data life cycle into the following phases:

  • Creation
  • Storage
  • Usage
  • Archival
  • Destruction

While this framework's phases use slightly different terms, they largely align with the steps outlined in this article.

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The Importance of Understanding the Data Life Cycle

Even if you don’t directly work with your organization’s data team or projects, understanding the data life cycle can empower you to communicate more effectively with those who do. It can also provide insights that allow you to conceive of potential projects or initiatives.

The good news is that, unless you intend to transition into or start a career as a data analyst or data scientist, it’s highly unlikely you’ll need a degree in the field. Several faster and more affordable options for learning basic data skills exist, such as online courses.

Are you interested in improving your data science and analytical skills? Learn more about Business Analytics, Data Science Principles, and Data Science for Business, three online analytics courses designed to help you build your data proficiency.

Tim Stobierski

About the Author

Tim Stobierski is a marketing specialist and contributing writer for Harvard Business School Online.