Data Governance and Data Democratization

Data governance is concerned with the setup of processes, workflows and roles within a company. Where is data located? Who owns it? Who should get access to it and how? These are just a few questions that require proper data management. The goal of data governance is to ensure that the data is trustworthy, findable, usable and consistent within a company. Data governance can also help to enable data democratization, which refers to making data available to a wide base of users within a company. The goal of widening the user base for data within a company is to make its culture more data-driven and have everyone harness the power of data. Traditionally, data has been located in silos pertaining to a certain department in a company, which makes cross-departmental access of data difficult. Furthermore, with data being stored in databases, access is difficult for the average layperson from a technical point of view. By setting up clear guidelines and procedures for how data is to be accessed, data governance can alleviate these barriers to data democratization. 

Data democratization is the idea that organizations perform better when data can be used by as many employees as possible. It strongly draws on training all staff and giving them the right tools to apply self-service analytics. 

Data governance makes data more protected, trustworthy, findable, usable and understandable, while data democratization ensures that value creation out of data is widespread across the organization. Both disciplines can strongly support each other and increase the value of data exponentially. 

Data governance is the discipline of managing data to ensure that it can be (re) used by people in the organization in an effective and compliant way. Although the goal of data governance is pretty clear and definitions of data governance are similar across organizations, how to implement data governance to achieve the goal is much less obvious. So the way data governance is implemented can vary significantly across different companies. Lightweight implementations can involve some basic rules for how data is documented, managed and shared that are followed organization-wide. More heavyweight data governance programmes can include setting up new roles and responsibilities within the organization, establishing governance committees or steering councils, training people across the organization, and implementing new processes and tools that support data governance. The benefits of data governance can be manifold. They typically include:  

  • The reduction of negative business impacts resulting from poor data quality  
  • Increased trust in using data from other departments for innovation and process improvement  
  • Higher data compliance through better controls  
  • Some significant efficiency gains when using data, as the user can find and understand the data more quickly  

Without data governance, however, there is a risk of chaos, as nobody really knows what is inside the data and synergies between data users are difficult to realize. Each user has to deal with data quality separately and cannot trust other people’s data. 

In a data-driven organization, employees use data from across business departments to improve their daily work. Data democratization is the idea that an organization performs better when as many employees as possible can use data from across the organization. One element of data democratization is ensuring that data is ready for others to use, and here data governance can help. Another element is giving nonexpert data users tools to easily process, combine, transform and visualize data; for example, with self-service BI and dashboarding tools and easy-to-use data science tools that work on a drag-and-drop basis. 

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