What is data democratization, and how can I get started with it?

Data democratization is the practice of making data more accessible for everyone within a business, regardless of their technical expertise. The goal of democratization is to empower employees and leaders to leverage data to make better, more informed decisions. 

Data democratization vs data governance

Data democratization breaks down technical hurdles and aims to give every person within an organization the ability to access and understand data if they need to. Data governance ensures the data is used responsibly and securely. 

While it might seem that the two concepts oppose each other: democratization opens up and governance locks down, they are closely related. 

For example, democratization provides an employee with the training, skills, and tools to use data for analytics, while governance puts guardrails, access controls, encryption, and data-sharing standards in place to ensure that only the correct people with the necessary permissions can use it.

Data governance is a framework that ensures data doesn’t fall into the wrong hands. This helps an organization meet strict regulations and compliance obligations, especially when handling sensitive data. Democratization improves data literacy and data quality, shifting responsibility for data from just IT teams to departments across an organization. 

How does data democratization work? 

Now, let’s explore how democratizing data works. The process aims to solve specific data challenges that employees and decision-makers face. Typically, they can’t access or trust data, lack the skills to conduct data analytics, don’t have the right tools, and have to wait on experts for insights. 

The primary purpose of data democratization is to put the power back into workers' hands so they can use enterprise data effectively. The main principles that support this are:

1. Accessibility

Data democratization requires a fresh approach to data architecture to improve accessibility. Data no longer offers value at rest. It needs to be flexible, fluid, and retrievable from numerous locations. 

Data fabric and data mesh architectures support self-service consumption so employees and stakeholders can use intuitive analytics tools for relevant insights.

2. Education and empowerment

Achieving data democratization requires a cultural shift where workers feel comfortable using and interpreting data. Providing employees with the tools to do this and prompting data literacy through training as part of a broader digital transformation push is key. 

This helps empower workers to trust the data they have access to and to wield it effectively to make decisions that drive value for the business.

3. Governance and security

Accessible data must be secure. Data governance initiatives support data democratization by putting a framework in place to protect data integrity while enabling broad usage. 

The use of access controls based on jobs and user attributes protects sensitive data, while strict governance policies and procedures uphold quality standards and outline how data should be used, stored, and deleted.

4. Modern tooling

A worker is only as good as his or her tools. Data democratization requires feature-rich business intelligence (BI) tools that sit on top of a data lake or data warehouse. 

This allows workers to access data quickly and generate their own reports without relying on data experts. Businesses also need to invest in a high-quality data cataloging platform to collect, organize, and enrich data that is ready for employees to use. 

What are the benefits of data democratization?

Imagine this scenario. A marketing manager wants to analyze data to assess the performance of a recent email campaign. 

However, due to a lack of skills and tooling, the manager needs to submit a request to IT first and wait for the information to be sent. This dependency wastes time and resources. 

Data democratization allows non-technical users to cut to the chase and access everything they need to make fast, smart, independent decisions.

Other benefits of data democratization include: 

• Increased efficiency: Employees are less dependent on specialized data teams and are capable of handling their own data queries, which cuts data process costs and frees up specialists to focus on higher-impact analysis that drives revenue and growth.
• Enhanced innovation: Raising the bar for data literacy creates an environment where teams can test new ideas and experiment with compelling new strategies, services, and products, all driven by accurate data-driven insights. 
• Competitive advantage: Upskilling employees, eliminating data silos, and improving data quality facilitates real-time decision-making, which is a key competitive differentiator in rapidly changing markets shaped by generative AI and new data trends.
• Better customer experiences: Improving data standards makes an organization more agile, ready to meet shifting customer preferences and demands, and capable of delivering truly differentiated experiences.

The roles of RBAC and ABAC n data democratization

Democratized data doesn’t mean universal access to information. The cons of data democratization are an increased difficulty in limiting and tracking usage, which poses a data security and privacy risk. 

Access controls secure and control data access while supporting data sharing across the business. The two most popular models for access control are RBAC and ABAC.

1. Role-based access control (RBAC) 

Role-based access controls assign permissions based on static, predetermined roles. For example, a manager overseeing marketing campaigns would have access to analytics dashboards and campaign performance metrics. However, any information outside their purview – such as  financial records and HR documents – would be inaccessible.

RBAC is easy to implement and effective at securing data and maintaining compliance in structured environments. However, it doesn’t work as well when job roles require more nuance or when an organization needs to scale, which can lead to role explosion.

2. Attribute-based access control (ABAC) 

Attribute-based access controls are more dynamic, granting access based on specific attributes such as job title, data asset, action, and location. This granular level of controlling access makes it a more flexible option for policy-making. ABAC also grants or blocks access at the time of the query rather than relying on set permissions and privileges.

Both RBAC and ABAC support safe and secure accessibility to data. RBAC is typically better suited to smaller enterprises with well-defined roles, while ABAC lends itself to larger enterprises with more complex environments. 

What are some examples of effective data democratization?

Increasing accessibility to data has driven many large corporations to enact data democratization strategies. There is a common theme that the recent explosion in raw data with varying levels of quality, relevance, and trustworthiness has paralyzed teams who either lack the skills or motivation to sift through unreliable or irrelevant data sets.

Data virtualization software used to combine data from multiple sources has been key. But big businesses are getting even more creative to deliver the data that management solutions employees need.

Airbnb

Airbnb struggled for years with a fragmented data landscape that made it challenging for employees to find or trust data. The solution: a self-service ‘Dataportal’ with a unified search function and metadata designed to transform data discovery and exploration. 

Now, non-technical employees at the vacation rental company can search for data tables, charts, logging schemas, and dashboards and get access to the data they need instantly. The system also logs everything a user has made, consumed, and favored to increase transparency. 

Walmart

Retail giant Walmart created a cloud data platform capable of processing 2.5 petabytes of data every single hour from more than 200 different sources. Its success in unifying data and providing teams with real-time insights via touchscreen smart boards transformed data analysis.

The democratization of data laid the groundwork for proactive and reactive analytics, empowering teams in respective departments to make better business decisions.

How to get started with data democratization

The sheer volume of data can make decision-making more difficult, not easier. Democratizing your data can solve organizational inertia. Here’s how to do it.

Step 1: Define objectives

Democratizing data is an ongoing process that requires significant investment. Secure support from leaders and stakeholders and demonstrate how the process will drive positive business outcomes. 

Set clear and realistic data objectives: improving data accessibility, enhancing data quality, and supporting innovation through AI and machine learning. These goals should be measurable and achievable.

Step 2: Conduct a data audit

Data landscapes can be messy. If you have siloed and inaccessible data sets, workers will quickly run into issues and bottlenecks. Take stock of your existing architecture, including storage systems and analytics tools. 

Identify areas that need to be improved. Consider whether you would be better off consolidating fragmented systems and integrating platforms. Democratization is easier when you have a unified data ecosystem.

Step 3: Map a data framework

What does data democratization look like for your business? Define the structure and flow of data you need to support seamless access while still maintaining control and security. You will need to document all of your data sources and how you plan to bring them together. 

For example, data virtualization can be used to create a single point of access for multiple sources. From here, outline how data will be cataloged and categorized, define the pathways that employees will use to share and collaborate, and select the tools needed to extract insights from data. 

Step 4: Establish controls

Now, it’s time for data governance. You need to think about how new data standards will be communicated and enforced. As part of this process, you should:

• Liaise with data leaders to implement governance protocols
• Decide whether a role-based or access-based model is best; the aim is to protect sensitive data while ensuring the right employees are always granted access
• Implement policies to ensure data management meets compliance with regulations like GLBA, APA, and GDPR
• Establish audit trails to track how data is used and shared
• Put data security measures in place to monitor unauthorized access, handle data breaches, and ensure permissions are regularly updated

Step 5: Invest in user-friendly tools 

Making data accessible requires user-friendly tools. Invest in technology that’s suitable for both technical and non-technical teams. Using smart dashboards and data visualizations can drastically reduce the time it takes for employees to conduct data analytics. Employees should be able to pick out trends, patterns, and outliers with ease and act on them immediately. 

Step 6: Train employees

Employee empowerment is fundamental to data democratization. You need to equip workers with the knowledge and skills to use data in their jobs every day. Promoting the importance of self-service tools through training schemes is critical. You should aim for workers to have at least a basic knowledge of data types, visualization techniques, and analytical methods.

Step 7: Monitor and refine  

Data democratization is an ongoing process. The end goal is for everyone to get the answers they need from data-related questions. This takes time and commitment, supported by an open and honest data-driven cultural shift in the workplace. 

Make sure to monitor your data democracy strategy and collect feedback from employees to improve processes over time. And don’t forget to update your tools and governance protocols as your business and its data demands evolve.

Data democratization best practices 

The path to accessible and trusted data is made easier by following these best practices:

• Don’t overreact to mistakes: Employees won’t turn into data experts overnight. Leaders must continually encourage data literacy and see mistakes as part of the process, a way for workers to learn and improve.
• Liberate legacy data: Legacy systems can be a roadblock in data democratization efforts. Use tools with API integrations to send data to cloud-based platforms so it can be accessed and acted on.
• Create personas to level up data proficiency: Airbnb recently created a ‘Data University’ where it trains employees using three specific personas: newbies, managers, and data specialists. Identify the level of data proficiency each of your employees needs to interpret data, and then train them accordingly.
• Promote the value of data: Increase employee confidence and highlight the importance of data by using dedicated channels, newsletters, emails, and events to promote new strategies, training initiatives, and customer success stories.  

Summing up 

Democratizing data by empowering employees with the right tools and skills and migrating to a unified data ecosystem can solve the most pressing data challenges and ensure workers find and act on the most relevant information they need to succeed in their jobs.

RecordPoint can help you to navigate the complexities of data democratization. We have advanced tools for data categorization, data discovery, data governance, and more. All are designed to make your data more accessible, secure, and compliant.

Explore the RecordPoint platform today and take a tour to see how we can empower your teams to drive innovation and growth.

FAQs

What does democratic data mean?

Data that is democratic is immediate and accessible to people who need it within a business. These employees also have the knowledge and tools to perform data analysis without help from others. 

What is an example of data democratization?

An example of data democratization involves a self-service analytics platform that gives business users access to data via a simple and intuitive dashboard. For instance, a product manager might gather user feedback on the fly and make informed decisions about feature updates without having to rely on specialist data teams.

What is the difference between data transparency and data democracy?

Data transparency is about accountability and ensuring data collection and management practices are open, honest, and ethical. It is related to data integrity and building trust with stakeholders. Meanwhile, data democracy is concerned with improving accessibility to data so teams can use it more effectively in their work.