What is Data Democratization?

Data democratization is the process of making digital information accessible to the average non-technical user of information systems without requiring IT involvement. It is the foundation for self-service analytics, an approach that allows these less-than-technical users (i.e., line-of-business) to gather and analyze data without having to seek help from a data steward, system administrator, or someone in IT.

Democratizing data is critical for organizations that wish to be more data-driven and realize data’s full value. Data democratization has many challengesbut even more benefits, and many are seeking solutions to democratize data across an organization. But, by adopting best practices for data democratization, organizations can avoid the pitfalls of the past.

The business benefits of data democratization

Giving teams access to the data they need is foundational to being a data-driven organization. However, studies show that workers waste as much as 3.6 hours per day, nearly 50% of their workday, searching for data. Democratizing data gives them easier access to that valuable data.

Easy access to data is crucial across all industries. Healthcare workers can enhance patient care, improve health equity, and improve healthcare outcomes. Retail workers can improve customer personalization, increase operational efficiency, and adapt more quickly to trends and shifting consumer preferences. Financial services teams can improve client engagement, streamline regulatory compliance, and increase operational efficiency.

It’s clear that data democratization isn’t just a technical endeavor—it delivers substantial value. By making data accessible to more workers, organizations can foster faster decision-making, drive innovation, and empower teams to make data-driven decisions without waiting for IT departments. Data democratization also enhances agility, allowing organizations to respond more quickly to market trends and customer needs. Furthermore, democratized data can lead to improved collaboration between departments, as insights are shared across the organization, breaking down silos that traditionally limit access to valuable information.

The benefits of data democratization seem obvious, so why haven’t more organizations moved more quickly to democratize data?

Top data democratization challenges and how to overcome them

There are a couple of reasons why data democratization hasn’t taken over the data workspace. Things like data literacy, data governance, and other needs require that organizations create a plan to address these challenges while ensuring workers get easier access to the data they need. 

Here are the data democratization challenges we’ve identified as the most prominent:

Data literacy issues

Data literacy, which is the ability to understand and communicate around data, underpins the success of data democratization initiatives. Poor data literacy can lead to misinterpretation, reduced data quality, inefficient data usage, and more. Building a data literacy program that educates workers on data interpretation, data governance, and using data tools will foster a data culture that improves data literacy.

Weak data governance

While data democratization aims to give more workers more access to more data, a lack of data governance can risk data integrity issues, security and privacy concerns, regulatory non-compliance, or worse. Organizations must establish a robust data governance framework to ensure data quality, compliance, and security without impeding data access. 

Data in silos

Enterprises have seen some success in getting their data into data warehouses, data lakes, or business intelligence systems. That solves the storage problem. But with data stored in multiple silos, it is difficult for an enterprise to establish a single source of trusted data that everyone can rely on. What’s needed is the establishment of a data inventory.

Limited access

Once you find the data you think you need, how do you know it’s trustworthy? In the past, IT teams addressed this challenge with a system of top-down governance, in which IT manages the data, ensures data quality, establishes business rules, and delivers what they know is trusted data to the business users who request it. 

This old way has failed for a few reasons. By erecting a wall between the people who managed the data (IT), and the business users who needed to act on it – IT teams blocked the free-flow of information necessary to move with agility. This perpetuated a system of data “haves” and “have nots”, creating data bottlenecks and lengthy wait times for business users seeking to access data (not to mention overburdening IT teams, as the imperative to use data for business decisions has grown in importance). 

Poor tooling

Existing business intelligence and data analysis tools just weren’t designed for the self-service analytics world we find ourselves in today. Effective data democratization relies not only on making data accessible but also on providing the appropriate tools for workers to interpret and utilize that data. When organizations lack robust tools, data democratization efforts will likely fail to meet desired outcomes.

A step-by-step guide for  democratizing data in your organization

As we’ve identified above, there are plenty of benefits to data democratization, but how to do it is the next question. Here are a couple best practices:

Step 1: Crawl your data sources

Wouldn’t it be great if you had a way to search across all of your data sources to find the information you need? And wouldn’t it be even better if, when doing so you could use the same natural language search capabilities you use when doing a search on Google? A data catalog does just that, providing you with a catalogued inventory of all of your data assets while allowing everyone on the team, including data analysts, data consumers, and data creators to collaborate.

Step 2: Make every bit of data accessible

Freeing up access to your data by taking a more grassroots approach to data management and data governance means everyone in your organization, not just your IT group, can easily find, understand, and collaborate on the data they need to make impactful business decisions.

Step 3: Deploy data governance management tools and processes

Establishing a solid data governance framework ensures that data is accurate, consistent, and secure. This should cover data quality, security, and compliance. A well-structured data governance methodology fosters trust in the data, encouraging broader usage across the organization.

Step 4: Ensure workers have the right tools

Making intuitive tools available enables employees to access, analyze, and visualize data effectively. Be sure to consider self-service analytics, data visualization tools that present data in an easily digestible format for easy comprehension and decision-making, and simple integration so tools can seamlessly connect with various data sources and systems.

Step 5: Create and nurture a data culture

A data culture is an organizational culture of data-driven decision-making. It empowers data users and promotes data literacy with training programs to enhance employees' data skills, access to subject-matter experts who can provide guidance and best practices, and data usage encouragement that recognizes and rewards data-driven decision-making to reinforce its importance.

How data catalogs enable data democratization

Data catalogs are the cornerstone of any data democratization effort. They serve as a centralized hub where all users can discover, understand, and access data. By providing metadata, lineage information, and data quality metrics, as well as data governance management and controls, data catalogs make it easier for non-technical users to find and trust the data they need. 

Data catalogs further simplify governance by ensuring that data access is both controlled and transparent, offering insights into who is using the data and how. With a robust data catalog in place, companies can empower employees at every level to access and analyze data confidently.

Data democratization vs. data centralization: What’s the difference?

Data democratization and data centralization are often confused, but they serve distinct purposes. We like to define these terms as follows:

• Data centralization refers to consolidating data from various sources into a single location, making it easier to manage and analyze. 

• Data democratization is about freeing and enabling access to data across the organization. 

Centralization creates the foundation for democratization, but the real value comes when data is made accessible to everyone in the organization, not just technical experts. By understanding the difference, companies can focus on both organizing their data and making it usable for a wider audience.

Top tools and technologies for data democratization

The right tools are essential to making data democratization a reality. Self-service BI platforms allow non-technical users to analyze and visualize data without relying on IT teams. Data catalogs like Alation enable users to discover and understand data through intuitive search functionalities and metadata management. Additionally, cloud data warehouses like Snowflake offer scalable storage solutions that make data accessible from anywhere. These technologies work together to create an environment where data can be easily accessed, understood, and utilized across an organization.

How to measure the success of data democratization

Measuring the success of a data democratization initiative requires tracking key metrics that demonstrate increased data usage and business outcomes. Organizations should monitor how often non-technical employees access data and whether this translates into more data-driven decisions. Surveys can also gauge improvements in data literacy and user satisfaction with data tools. 

Additionally, businesses should look for correlations between data democratization and growth or efficiency metrics such as faster decision-making, improved operational efficiency, or increased innovation. Regularly assessing these metrics helps ensure that democratization efforts are delivering tangible benefits.

Data security in a democratized data environment

One of the primary concerns with data democratization is maintaining security as more users gain access to sensitive data. Organizations must implement robust security measures, including role-based access controls (RBAC), encryption, and auditing capabilities, to protect data while promoting access. 

It’s essential to strike a balance between openness and protection, ensuring that users can only access the data they are authorized to see. Security protocols should also be integrated into the data democratization strategy from the outset to prevent breaches and comply with regulations, making it possible to democratize data without compromising security.

Common misconceptions about data democratization

There are several misconceptions about data democratization that can prevent businesses from fully embracing its benefits. 

One common myth is that democratizing data leads to chaos, with too many users accessing and misinterpreting data. In reality, data democratization, when paired with proper governance and training, reduces data misinterpretation by empowering users with the right context and tools. 

Another misconception is that data democratization requires massive investments in technology. While technology is important, fostering a culture of data-driven decision-making is equally critical to success, often achievable with incremental changes.

The future of data democratization: Trends to watch

As data democratization continues to evolve, several trends are shaping its future. Here are a few things to keep an eye on:

• AI-powered tools like Alation’s Artificial Intelligence Co-Pilot, ALLIE AI, make it easier for non-experts to extract insights from data, further lowering the barrier to entry for data analysis. 

• The rise of low-code and no-code platforms is empowering employees to build their own data solutions without needing deep technical skills. 

• The growing importance of data literacy programs will continue as businesses recognize that democratization must be supported by a workforce that can confidently use and interpret data.

These trends suggest that the future of data democratization will be even more inclusive and accessible.

Conclusion

The latest generation of data visualization and reporting tools have democratized access to data and are only slightly more complicated to learn and use than the spreadsheets and charting apps that preceded them (but with much better visualizations). These tools have leveled the playing field and provided an even greater need for a data catalog to support them. 

Data catalogs complement self-service analytics tools by providing a tool-agnostic approach to data cataloging. You can use a data catalog to search your enterprise data sources and BI tools, and to learn how the data assets they contain are being used within your organization. A data catalog injects the metadata along with the tables, schema, queries and other data.

By adding human collaboration to the mix, more modern data catalogs go beyond a simple data inventory, allowing everyone to hold conversations around the data, improving its quality along the way. The Alation Data Catalog does just this. By improving productivity, analytics accuracy, and creating confident data-backed decision making, the entire organization is empowered to utilize data.

FAQs

What is data democratization?

Data democratization is the process of making digital information accessible to the average non-technical user of information systems so organizations can become more nimble and data-driven.

What are data democratization challenges?

The challenges of data democratization include data literacy issues, weak data governance, data in silos, limited access, and poor tooling.

How do you implement data democratization?

Data democratization can be implemented by crawling your sources and making everything accessible. Using a data catalog makes this process fast and easy, and provides a platform for data democratization.

What are the steps for democratizing data?

To democratize data, crawl your data sources, make the data accessible, deploy data governance tools and processes, ensure workers have the right tools, and use it all to create and nurture a data culture.