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Glossary

Data Asset

A data asset is any digital object or entity made up of data. It could be a dataset, document, visualization or data service.

What is a Data Asset?

A data asset is any digital object or entity made up of data. It could be a dataset, document, visualization or data service and can include tools designed to make that data available and usable. For example, a dashboard that displays real-time data on a website is a data asset.

Data assets take raw data generated by data sources (such as systems or sensors) and enrich and organize it so that it can be understood and used. They can be structured or unstructured. Data assets can be used internally, shared externally for free (either with partners or via open data), or be charged for in order to generate revenue (a data product).

Data assets are one of an organization’s most valuable assets and need to be protected, managed and exploited effectively to deliver benefits around better decision-making, greater efficiency through process improvements and increased transparency.

What are examples of Data Assets?

Given the variety of data generated and shared with organizations, there are a wide range of data asset types. This includes:

  • Customer data, including customer details and profiles
  • Sales data
  • Sensor data
  • Survey or census data
  • Mobility data
  • Financial data, such as budgets and accounts
  • Forecasts
  • Web and social media data

Why are Data Assets important?

Successfully harnessing data is crucial to organizational performance, enabling businesses to:

  • Better understand customers and meet their needs
  • Increase efficiency and productivity
  • Take better, faster and more informed decisions
  • Plan future strategies and manage risk
  • Collaborate, both internally and externally
  • Innovate and create new services/products
  • Launch new services based on data, generating additional revenue streams

What are the key issues around managing Data Assets?

To successfully exploit and generate value from their data assets, organizations need to focus on four areas:

Data strategy

Organizations generate enormous volumes of data. Much of this does not deliver value in its raw form. They therefore need a data strategy that understands business needs and objectives, and focuses on collecting and making available relevant data assets to the organization and its partners.

Data security

Data assets have to be kept secure and protected, such as from competitors and hackers. They often contain confidential or personally identifiable information, meaning that their storage and use have to follow compliance rules, such as with the GDPR.

Data governance

To deliver value data assets have to be reliable, trustworthy and high-quality, Data governance programs ensure that corporate standards and best practices are met by data assets.

Data management

Data management is the overall end-to-end technical process of collecting, optimizing, sharing, exploiting, and monitoring data. The goal of data management is to turn raw data into valuable data assets that are available to the business and external audiences, and can be easily used and reused by them.

What is the difference between a digital asset and a Data Asset?

A digital asset is anything that exists in a uniquely identifiable digital form, such as a photograph or document. A data asset organizes digital assets into a more usable form in order to deliver wider value.

How do you manage Data Assets?

Successfully managing data assets requires a strategic, planned approach. It should cover all of the data within an organization, along with that collected from external systems or partners. It should follow a process that aims to answer these key questions:

  1. What are our data assets?
  2. Where are they located and in what format?
  3. Which are most and least valuable and useful?
  4. What do we want to use these data assets for?
  5. Who do we want to be able to use the data assets?
  6. How can we achieve this and create value from the data assets?
  7. How can we monitor progress towards these objectives?
  8. How do we ensure that data assets are reliable and trustworthy?
  9. Where are data assets being used and by who?
  10. How do we ensure we are maximizing value from each data asset?

 

Ebook - Data Portal: the essential solution to maximize impact for data leaders

 

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