The goal of building a strategy to democratize data is to allow all appropriate staff to access the organization’s data, regardless of their technical skills or job description.

Previously, a researcher would have to submit a request to the IT department for the information they needed. For example, if a production manager wanted a mid-month report on the amounts of scrap and waste, they would have to ask the IT department for the information – and wait. With data democratization, that manager can casually access the data without having to wait for IT.

Surprisingly, many organizations still believe research and analytics are strictly the responsibility of the IT department. This philosophy severely limits research in general and minimizes both enthusiasm and intuitive, spin-off research. A business that limits and restricts research is essentially crippling itself.

With a strategy to democratize data in place, businesses can stay competitive. Most businesses will have to develop a data democratization strategy, allowing for easy and open research to their staff. The process shifts the responsibility for research and data analytics from the IT team to all users within the organization.

Data integration tools are necessary for data democratization and will support research and data analytics by staff with a non-technical background.

A Brief History of Data Democratization

Data democratization came about as companies began to do “big data” research. The lack of data scientists and technologists to perform the research led to simpler, more user-friendly systems, so that the same work could be done by people outside of the IT department. 

By making data accessible to everyone in a business, without having the data bottlenecked in the IT department, a business becomes much more flexible and adaptable. Data democratization also frees the IT team to perform more valuable projects.

If an organization decides to implement a data democratization program to provide staff with easy access to the bulk of their data, there are several cultural and Data Governance issues that may need to be overcome. Some concerns with implementing a data democratization program are:

• The risk of people misinterpreting the data and making poor decisions
• Data duplication as researchers copy the data for easy access during research
• Exposing the business to ethical, legal, and privacy concerns

Data Governance Before Data Democratization

A Data Governance program should be installed before implementing data democratization. Providing open, easy access to data before setting up a Data Governance program could be devastating. Without a functional Data Governance framework, intelligent decision-making and automatic regulatory compliance are much more difficult.

Some risks come with developing a strategy to democratize data. While the goal of giving staff access to the organization’s data provides significant benefits, concerns about data privacy and other risks cannot be ignored. (With more employees working remotely, overly easy access to the company’s data can lead to hacking by cyber criminals.)

Processes can be developed that make the data accessible to all staff with the appropriate passwords.  

A Data Governance framework should be designed to meet the needs of your specific organization. It should focus on standardizing the business’s rules and processes for using, storing, and collecting data. 

Also, modern Data Governance programs are designed to protect businesses from breaking the laws and regulations established to protect people’s personal information — there are now numerous countries supporting personal privacy laws.

A well-designed Data Governance framework can help develop a program capable of identifying data sources, building catalogs, and delivering data. More specifically, a security system must be put in place that protects the personal information of customers and staff, while making the bulk of the information easily accessible. 

Proper security safeguards are built into the Data Governance program. Data processing guidelines and appropriate levels of encryption can be used when data is stored or transferred and are necessary to protect the data from cybercriminals.

Data Accessibility – Expanding Ownership to Democratize Data

The key for most organizations aiming to democratize data is determining what data should have limited access, with the rest of the data remaining accessible to anyone with a password. 

Providing users with software programs they can use is often the most visible data democratization benefit. User-friendly software should be available for non-technical users. 

Businesses have deployed user-friendly low-code, no-code, or NoSQL platforms as a way to expand data ownership. These systems allow more employees to take responsibility for the data they are working with, while following the guidelines established with the Data Governance program. 

Data Democratization Benefits and Outcomes

Modern businesses must make decisions quickly and efficiently to avoid losing profitable opportunities. More and more, these decisions rely on ever-increasing amounts of data. 

Organizations without access to data, or a data analytics process, typically take longer to make decisions and may remain unaware of business intelligence available to the competition. (Good data analytics requires a Data Governance program to provide high-quality data.) 

Some benefits of data democratization are: 

• Staff gain a broader understanding of the business: An understanding of the business priorities, goals, and processes promotes intelligent decision-making. As staff learns the system, they develop a holistic view of the organization and its data assets. A small amount of training in the use of metadata will speed up research and provide context for the data. As a business grows, so too does the amount of incoming data. Dealing with all this data becomes the responsibility of all staff working with it, and waiting for IT to collect it for research is no longer necessary. 

• Staff loyalty increases: Staff that perform research, are involved in the decision-making process, or have been authorized to make decisions as part of their job responsibilities, will come to feel a sense of pride and ownership of their job position. Businesses that have “engaged” employees – meaning they make decisions – have a turnover rate that is 31% lower than businesses that expect their employees to simply follow orders.

• Making data available: For a data democracy to function efficiently, it is essential to invest in both data integration and data analytics tools that are user-friendly. In many older organizations, these types of tools continue to be monopolized by their IT department, resulting in project delays and stalled decision-making. This situation causes a Data Management process that is slow, and ultimately destructive to the business. Having the proper tools makes the business more efficient.

• Enhancing the customer experience: Today’s customers expect smooth, uncomplicated interactions with a website. Unnecessarily complicated transactions result in a loss of trust and customers. Businesses that expect all of their staff to be concerned with the customer experience are in a better position to meet the customers’ expectations.

• Legacy data becomes useful: Data democratization also deals with liberating the data that has been trapped within a legacy system. Businesses must invest in the appropriate data integration tools to make the legacy data accessible. 

• Self-service analytics become commonplace: The process of data democratization leads to staff not only accessing the data, but also analyzing it. Modern business intelligence and data integration tools allow staff without technical training to process and analyze data, easily.

The Future of Data Democratization

The newest tools are coming from the cloud, rather than on-premises software. One reason these new tools have become so popular is due largely to how data has become so spread out. 

The new SaaS platforms can help businesses to manage and coordinate data from multiple departments and storage locations. A nontechnical business analyst can consolidate this data efficiently using cloud-based tools and develop useful business intelligence.

Also, search engines are now focusing on content, rather than keywords. This will improve the quality of information accessed by nontechnical researchers, in turn providing improved solutions. 

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