As a direct result of the technological advancements of the last decade, modern businesses can now capture consumer information from a broad range of data sources. Unfortunately, many forms of data are not used to further the company’s mission. Instead, these mountains of information are simply being collected and stored aimlessly. This lack of intentionality is due to the fact that in recent years, a majority of businesses have become effective at capturing information on their target audience. Sounds counterintuitive, right? However, because they have more information, organizations are currently overwhelmed as most do not know how to make their data work for them.
To remedy this issue, organizations must become more intentional about data. Below, let’s examine why being “data intentional” is essential for entities across all sectors, and especially for organizations that have a large volume of data and documents.
Data Intentional vs. Intentional Uses of Data: What’s the Difference?
There is an important distinction between being “data intentional” and the “intentional use of data.” The former, being data intentional, addresses the “why” and “how” of information gathering. Put simply, it’s a strategy. The latter, intentional use of data, is closely related to data privacy and how an organization uses what they collect.
When being data intentional, your organization must identify:
- Why they collect the data
- How they plan to gather this information
- What they intend to use it for
Organizations that are intentional about data collection have a clear goal before starting the information capture process. However, entities that fail to be intentional with their data collection practices often end up storing far more information than they will actually use. As a result, these businesses are frequently left with an abundance of unstructured data that is difficult, if not impossible, to search, analyze or use.
Intentional Data vs. Unintentional Data
Intentional data is information that was collected with a specific purpose in mind. Organizational leadership usually wants this information to be gathered so that it can be analyzed and used in a particular manner to reveal important insights about the company.
On the other hand, unintentional data is information that was collected because it was available. Collecting too much data without a clearly defined intent will make data analysis and presentation far more complex. Adding to this issue, this data may not have any bearing on an organization’s goals, which means that collecting it is a wasteful endeavor altogether.
There are no set rules for differentiating intentional and unintentional data. Theoretically, any data point can have value, depending on what questions organizational leaders want answered. Therefore, each entity must determine which data points are most relevant to their goals and focus their efforts on collecting, analyzing, and presenting that information.
Importance of Being Data Intentional
The most apparent benefit of being more data intentional is that it will allow organizations to better use all of the information they collect. Data analysts will focus their efforts on examining only the most relevant datasets. In turn, they will be able to present this information to critical decision-makers allowing them to make data-driven decisions and more effectively achieve organizational goals.
Being data intentional has become increasingly important in recent years, as consumer privacy has evolved into a mainstream issue. According to a recent Harvard Business Review article, only about one in five data leaders believe that the industry has “done enough” to address pressing AI and data collection ethical issues. This means that most data leaders would likely support more-stringent data usage regulations.
Organizations that are data intentional can more efficiently monitor the information they collect. This will insulate the organization from civil liability and ensure compliance with sweeping consumer data privacy laws like the California Consumer Privacy Act.
How Organizations Can Craft an Effective Data Intentionality Strategy
While organizations cannot become data intentional overnight, they can take meaningful action to better use their data by developing an effective data intentionality strategy. When developing their strategy, businesses must:
- Clearly Define Organizational Goals
Step one to creating any cohesive plan involves detailed goal-setting. Businesses, educational institutions, government entities, or any other organization that intends to collect data must first decide what they hope to accomplish by gathering this information. During this stage, organizations should also identify immediate use cases for the data they are collecting.
- Identify What Data Is Needed to Accomplish Those Goals
After an organization defines its goals, it must determine what types of data it should collect. Developing a sustainable data model will help them organize the various data elements and better understand the data required to accomplish their goals.
- Implement the Right Data Collection Technologies
Next, organizations should ensure that they have the right data collection technologies in place. Solutions such as intelligent document processing (IDP) software are invaluable assets.
IDP solutions convert unstructured or semi-structured documents into organized, usable data. This technology also serves as a delivery system that sends the extracted data to existing workflow tools or line of business solutions.
- Put the Plan into Action
Once the plan has been developed, and the right technologies are in place, organizations can begin to refine their data collection practices. Over time, companies will become far more efficient at:
- Data collection and capturing
- Gathering more meaningful insights from the information they collect
The success of your data intentionality strategy hinges on the effectiveness of your data capture capabilities. Without access to accurate, timely and actionable data, your organization’s plans will fall well short of expectations. Are you ready to become data intentional?
About the Author
Kevin Harbauer is the Chief Technology Officer at Ephesoft, based in Irvine, California, and brings 20 years of diverse software development, management, operations and implementation experience. He has proven success building and leading high-performing product development and IT organizations. Similarly, he has helped organizations adopt innovative technologies and practices, including DevOps, Continuous Delivery, Software-as-a-Service, cloud-based, cybersecurity initiatives and data-driven operations.
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