What is data democratization? Definition with pros and cons

Data democratization is motivated by a drive to find value in the ever-growing volumes of data that organizations produce and collect. Exposing data to more people via data democratization is thought to be a way to gain more from data by increasing the number of people with unfettered access to view and work with it. 

Definition of data democratization

Data democratization is the release of data access to allow average end users to gather and analyze an organization’s data without IT or other technical resources. The stated goal of data democratization is to allow non-specialists to easily access data from their preferred devices without requiring specialized training, tools, or skills.

In essence, data democratization is barrier-free, enterprise-wide access to all types of data, including structured and unstructured, semi-structured, and dark data.

Effective data democratization includes an education component to enable users (i.e., from office managers to engineers) with a range of technical capabilities to access and work with data. Data democratization also includes enabling users to present data and use it to inform decision-making.   

Why some enterprises choose data democratization

Some enterprises choose data democratization due to expectations of how it can serve particular purposes and support different departments or roles across the organization. Examples of how enterprises expect to benefit from data democratization include the following. 

Customer service

For customer service, data democratization is expected to broaden the amount of information available to company representatives when engaging with customers on the phone or in person. It is meant to allow customer service representatives to quickly find even more detailed information about customers, such as past activity or purchases. 

Customer success

Customer success may be improved with data democratization in much the same way as customer service. By giving teams unfettered access to customer data, customer success teams would be able to access more information about individual customers and use this visibility to find ways to maintain and increase customer satisfaction.   

Customer support

Data democratization is expected to facilitate customer support by offering more details about what a customer has purchased and how they have utilized products. The assumption is that this information will help customer support representatives identify issues and find solutions more quickly.   

Executive leadership

Data democratization’s promise of fast access to 360-degree views of all areas of the enterprise’s operations is believed to help executive leadership gain improved insights into performance and identify areas that demand attention. This assumption is based on the idea that more data drives better insights faster.   

Human resources

Human resources functions are meant to be improved by data democratization, because of the information that can be gathered and analyzed about requirements assessments, performance reviews, and recruiting efforts.   

Marketing

Marketing teams see great potential in data democratization as a mechanism for accessing customer and solution-related information to support targeting, testing, and campaign optimizations.   

Research and development

Data democratization is expected to support research and development teams by providing access to vast amounts of data that could help identify areas for improvement and opportunities for innovation. Research and development teams could gain access to data from across the organization that could provide insights into customers’ buying and usage patterns and use the data to direct which new features to build and which to phase out. 

Sales

Sales teams see potential in data democratization as it promises greater customer insights. For example, sales teams have an opportunity to work with data to identify which prospects to target based on their likelihood to convert. Information made available with data democratization is expected to help with the creation of buyer personas that leverage details about prospects’ activities and other related data.   

The right data in the right tools at the right time

Data democratization requires that users have access to the right tools to get a complete and clean set of the right data they are seeking at the right time. It also requires that users be able to process and act upon data. This means providing users with the tools they need to visualize and analyze data.  

There are a variety of tools that facilitate data democratization, including the following.   

Data catalogs

Data catalogs help data democratization initiatives by using metadata to create a searchable inventory of information in an organization. Data democratization is supported by making it faster and easier to organize and access data for analysis and research. 

Data governance tools

Data governance is critical for successful data democratization initiatives. Among the key capabilities that data governance tools provide are managing and protecting data assets. Data democratization initiatives can only be effective if systems are in place to ensure that data is accurate, available, and protected in accordance with compliance requirements for security and privacy.   

Data visualization tools

Data visualization tools facilitate data democratization by helping users easily represent data graphically. By simplifying data rendering in visual formats, such as charts, graphs, and heatmaps, data visualization tools allow data to be shared in an accessible format that supports data democratization.   

Self-service analytics tools

With accessibility at the heart of data democratization, self-service analytics tools play an important role. A type of business intelligence tool (BI), self-service analytics tools help users to take advantage of data democratization initiatives by allowing them to access data and perform data analysis tasks without IT, BI, or data science teams.  

Self-service analytics tools facilitate data democratization by providing overlays onto or simplifications of complex data models to make them accessible to a broad user base.   

Data democratization and data literacy

Without data literacy, a data democracy program cannot be effective. Because data is inert, simply making data available with a data democracy initiative does not deliver results for most users.

Few users have backgrounds in data and are, at best, ill-equipped to put it to use effectively and efficiently. An organization’s data democratization initiative should include a data literacy component as a complement to other tools to make information not just accessible, but also understandable, consumable, and actionable. 

Providing data literacy training supports data democratization by helping users gain the skills needed to be able to read, analyze, and interpret data. Organizations that embrace data democratization need to empower users with this kind of data literacy support to ensure that they are comfortable using data and develop the proficiency required to work efficiently with data as part of their functional workflows.  

In addition to helping users make the most of a data democratization program, data literacy can support data security. Teaching users how to work with data means facilitating their job functions, reinforcing an understanding of the importance of protecting the security and privacy of data, and instilling a sense of accountability for following data protection rules.   

Pros and cons of data democratization

Data democratization promises many benefits. And for all the potential rewards of this type of initiative, there are notable downsides. 

Data democratization pros

• Access to data from disparate sources is streamlined. 
• Average users can gather, analyze, and visualize data faster and more easily. 
• Broader access to legacy data is possible with data democratization. 
• Fewer data-focused experts are required to give users access to information. 
• Information can be shared between groups more easily. 
• More information is available to feed machine learning models. 
• Self-service analytics become more commonplace and widely used. 

Data democratization cons

• Data democratization has many associated expenses ranging from new solutions that must be set up and managed to provide access to increased security and governance to protect data.
• Data governance must be in place and adjusted to support the complexities that accompany the data democratization initiative. 
• Data quality oversight needs to be enhanced to ensure that standards are maintained. 
• Extensive training in data literacy is required to ensure the efficient and effective use of information. 
• Misinterpretation and misrepresentation of data can occur when users who lack data analysis and visualization experience use their access to pull data, conduct analysis, and present conclusions.  
• Reduced visibility is an ironic downside of data democratization, because users who lack the ability to filter data quickly become overwhelmed with the volume of what is available. 
• Security and privacy compromise risks increase as data protections are loosened to allow for broad access to information. 

Data democratization and data governance

Before embarking on a data democratization initiative, a data governance program should be in place that provides easy, open access to data. Without the structure and guardrails provided by a data governance program, a data democratization initiative can make the already difficult task of managing and protecting data even more challenging. 

A data governance framework supports data democratization initiatives by helping organizations identify risks and implement mitigation solutions before problems occur. This is especially important for organizations that are subject to regulatory compliance rules related to security and privacy protections for sensitive information. 

Data governance programs provide the policies and procedures around data access and use, establish data quality standards, and detail data usage best practices needed to implement data democratization effectively. This includes ensuring the accessibility, quality, and security of data. 

Data democratization best practices

Define objectives

A successful data democratization initiative requires that teams take time to assess the needs that are driving it. This includes defining data objectives and aligning them with the organization’s overall goals and the requirements of teams and individual users.   

Perform a data audit before beginning

A comprehensive data audit should be conducted to prepare for a data democratization initiative. This should include an inventory of data assets, an examination of how data is being used, where data access bottlenecks are occurring, and understanding users’ requirements.   

Establish controls to ensure compliance considerations

For most organizations, compliance is of vital concern. A data democratization initiative must include access and governance controls to enforce compliance-related rules.

Integrate siloed data sources to be included in the initiative

A data democratization initiative should begin with identifying siloed data and creating an integrated data set to make information accessible to users. Processes should also be implemented to support data democratization by preventing the build-up of new silos. 

Provide data literacy training for users

Without data literacy, a data democratization initiative is doomed to fail. Data democratization can only succeed when users have the skills needed to effectively make use of the information that is available. 

Use software to support the initiative 

Solutions such as business intelligence tools, data catalogs, data governance tools, data management systems, data visualization tools, and self-service analytics tools support success with a data democratization initiative. Agnostic solutions enabling interoperability between different systems and platforms are recommended when selecting tools. In addition, organizations may choose solutions designed for users’ various skill levels rather than specialized tools that require specific expertise and training. 

Secure buy-in from key stakeholders prior to launch and keep stakeholders engaged

From top leadership to IT, a successful data democratization initiative requires all parties to be on board. Taking time upfront to educate stakeholders about the reasons for the initiative and objectives for it is critical. It is also important to keep these stakeholders in the loop as the data democratization initiative is rolled out, taking time to solicit and respond to feedback from managers and users alike.   

Focus on relevant data

Data democratization does not need to incorporate every bit of data. When assessing the scope of the initiative, start with the data that will be of immediate use, then look to other information that could be useful. This helps eliminate distracting noise. 

Data democratization balancing act

Data democratization is an example of how more is not always better. The concept behind it, which is to prove more users with more access to data, has its merits.  

Data democratization can enable the enterprise to drive value from data. However, to be effective, it must be executed with care. Data governance programs need to be in place to ensure that data are handled correctly, data literacy training should be provided to ensure that users know how to use the information they are able to access, and initiatives must take care to consider that data is accessible.  

Considerations around the accessibility of information go beyond security and privacy. An effective data democratization initiative takes care that users get the right data. Without controls on how much information is available, benefits of data democratization are diminished as users are overwhelmed with more information than they can process.   

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