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Glossary

Data Accessibility

Data accessibility refers to how easy it is for an organization’s data to be discovered, accessed, understood and utilized effectively by all.

What is data accessibility?

Data accessibility covers how easy it is for an organization’s data to be discovered, accessed, retrieved, understood, and utilized effectively by everyone that can benefit from it, without requiring them to have technical skills. Users could be employees across the organization or external audiences, such as partners, citizens and other stakeholders. Data accessibility is part of data governance, and is an ongoing process that aims to bridge the gap between having data and generating value from it.

Providing access to data for all is vital to improving business decision-making, boosting efficiency and increasing collaboration. Data access should be on-demand and self-service for users, without requiring support from specialist data scientists or tools.

How is data accessibility different from data availability?

Data availability simply means that data has been collected and stored somewhere within an organization. These data assets could be generated by business systems, sensors, customers or partners. While data is available, it does not have to be in a usable or understandable format and is often difficult to find. Employees may not even know that relevant data is there, for example. Data availability is therefore just the first step to generating value from your data.

In contrast, data accessibility goes beyond data being simply available to ensure that it can be found and used effectively by non-specialists, without requiring support, training or tools. Everyone can access the data they need, with confidence, whatever their level of data knowledge or experience.

Why is data accessibility important?

Organizations understand the importance of becoming data-centric, and accessible data is fundamental to achieving this. Strong data accessibility delivers these benefits:

  • It improves decision-making through seamless access to relevant data
  • It increases productivity by giving employees the data they need to do their jobs faster
  • It underpins collaboration between teams, breaking down departmental silos
  • It helps build a data-driven culture and drives data democratization
  • It enables automation of processes and the use of AI based on high-quality, trusted data
  • It saves time for data teams as they spend less time answering employee requests on data, increasing operational efficiency
  • It delivers transparency and regulatory compliance through access to comprehensive data

What are the challenges to data accessibility?

Organizations are now generating and collecting enormous volumes of data. This is often stored in multiple different systems, managed by different teams and can be in a range of formats. Making this data accessible requires organizations to overcome these challenges:

  • Data can be stored in silos, meaning that people outside a department find it difficult to discover or access it
  • Data quality levels may vary, with inconsistent formats and a lack of comprehensive metadata
  • Data may be in raw formats that cannot be understood or used by those without specialist knowledge or skills
  • Data may be difficult to discover with confidence. Users may not be sure that they have found the right dataset for their needs or that it is reliable and up-to-date
  • Data may not be accessible to all due to security or compliance concerns
  • It may be expensive to process and make accessible all of an organization’s growing data volumes

How do you ensure and measure data accessibility?

Strategies and processes to ensure data accessibility

Ensuring that data is accessible to all requires a six stage process:

  1. Understand and define the requirements of users. What types of data do they need and where is it within the organization?
  2. Define data standards and governance, including security and compliance protocols
  3. Clean, reformat and standardize this data by applying data governance processes
  4. Educate users about the importance of data in their daily lives
  5. Make data available in the right format and through the right tools to users, using access controls if required to manage the security of sensitive data
  6. Monitor data usage and feedback to improve accessibility moving forward

Centralized, one-stop shop data portals are vital to making data accessible to the right audiences. They provide a single place for data access, with an intuitive user experience that enables any employee to confidently discover, interact with, and utilize data, whatever their level of data skills. Data can be accessed in multiple formats (such as raw data, visualizations, dashboards and via APIs), depending on the needs of the user.

How to measure data accessibility

Ensuring data accessibility is an ongoing process and progress needs to be continually monitored and improved. You can measure data accessibility through metrics such as the usage of different data assets, overall user satisfaction, data literacy, feedback and the number of reuses around datasets. Data lineage tools help to gather this usage data, as does collecting regular feedback from specific groups of users.

 

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