Skip to main content

Data democratization may sound like just another technology buzzword, but with organizations collecting more and more data every day, the accuracy, trustworthiness, and accessibility of data must be prioritized if businesses want to unlock its value. And, that's where data democratization can help.

But, what is democratization of data, exactly, and how can you achieve it while maintaining high levels of governance and trust? Keep reading to discover the steps to successful data democratization as well as how it can benefit businesses and support artificial intelligence (AI) strategies.

What is data democratization?

Data democratization means giving everyone in an organization the ability to access data (appropriately) along with the tools and training necessary to understand it. It means removing barriers and providing education so that all end users—employees, stakeholders, and consumers—feel confident working with data and trust the end results, particularly when it comes to AI models.

To truly democratize data and unlock the value of the information stored within, organizations need to undergo a shift in company culture and change the way they store and distribute data.

What is the primary purpose of data democratization?

Now that we have a clear data democratization definition, let's explore the reasons for doing it. As we mentioned, the goal of democratizing data is to remove barriers to access and understanding, so that non-specialists can view and leverage an organization's data and optimize its usefulness.

The process ensures that the right people can see the right data at the right time and for the right purpose. They can use it to make informed decisions and identify opportunities or problems, without requiring in-depth knowledge.

A data democratization strategy could include self-service analytics tools, which can be used without needing help from the data team or IT department, or in-depth training to help all employees feel comfortable with more tech-minded solutions.

When you democratize data access, you'll see an improvement in efficiency and productivity, which helps to improve employee and customer experiences and boost revenue. It also brings greater transparency, as everyone can see the data on which business decisions are based.

Do businesses benefit when they democratize data?

We already mentioned some of the advantages for business owners who democratize access to data. As well as the time and money savings that it contributes to, there are a number of other key benefits.

Break down data silos

Data silos happen when information is stored on separate systems, with each only accessed by a particular team or department. Data democratization requires the centralization of data, as storing it all in one place makes it more easily accessible to anyone who needs it.

This makes it easy to share data between teams, improving collaboration and cross-functional decisions. For example, the marketing, product development, and customer support teams will all need access to the same feedback data when developing new products.

Centralized, standardized data means that everyone sees the same information in the same format, so there are no gaps or duplications. It fosters a culture of collaboration and knowledge sharing. This also contributes to greater accuracy in datasets.

Remove bottlenecks

In some organizations, data is "owned" by the IT department, and other teams have to request access. Even when data is widely available, employees may have to ask for help in finding what they need - and understanding what they're looking at.

This creates bottlenecks, delaying tasks and taking up the data team's time. Data democratization not only grants access but educates everyone on where data is stored, how to find the right information, and how to use it effectively.

As well as helping other teams complete their tasks faster, this also frees the data team from constantly dealing with requests and tickets, allowing them to focus on advanced data work, such as AI and machine learning (ML) models.

Optimize data management

Data democratization means changing the way your organization houses, manages, and distributes data. Storing data in a central hub makes it easier to find, but the data team must ensure its quality and accuracy, as well as maintaining security when more people have access.

We already mentioned self-service applications, but you can also implement data federation software which collates a virtual database of information from different sources ready to use for business intelligence.

Meanwhile, data virtualization software allows applications to retrieve and manipulate data without the user needing a high level of technical expertise.

Increase data-driven decisions

When you democratize the data, you're ensuring that end users can make the most of it. With help from the software mentioned above, they can identify patterns and trends and gain deeper insights—without needing help from IT experts.

This means that everyone has the ability to make data-led decisions across all teams. For example, your teams could use data to decide:

  • Whether to continue investing in a marketing campaign
  • Which prospects are most likely to convert
  • How much inventory to purchase
  • Which version of a web page customers prefer
  • Whether certain software increases productivity.

What's stopping the democratization of data?

As with the implementation of any new process, there are certain challenges to adopting data democratization. Some of the main hurdles you may encounter include:

Compliance and security concerns

Concerns around data security prevent some organizations from embracing data democracy. When data is owned and managed solely by IT experts, who have to approve data requests from other users, there are strong checks and balances in place.

There's a fear that widening access could put confidential information - such as company finances or customer details - at risk of misuse or breaches. This also has ramifications for compliance, since there are penalties for not adhering to regulations like GDPR and CCPA.

Limited data literacy and inaccessible tools

When there is limited data literacy across a workforce, misinterpretation and mistrust of data can easily occur. Understanding data-driven insights and decisions is vital to implementing them successfully.

Inaccessible tools can compound this mistrust, as they can require specialized training and technical knowledge. This makes them inaccessible to non-technically trained employees, which hinders data collaboration and contribution across organizations.

The true democratization of data cannot occur without a reworking of the ways that companies currently approach data literacy and the tools used for analysis.

Low-quality data

The widespread access to information that is essential to democratizing data is also hampered further if end users can't trust the data they access. Unreliable data and inconsistent formatting make it impossible to extract accurate insights and make reliable decisions.

Unless the organization has robust data quality and governance policies, data lakes can turn into poorly managed data swamps. This leads to the 80/20 dilemma, in which data scientists only spend 20% of their time analyzing data because the other 80% is eaten up by the finding, cleaning, and organizing that is required before it can be used.

What is needed for a successful data democratization strategy?

Democratizing your data isn't something that can be done overnight. It's an ongoing process and requires cultural change throughout an organization. Here are five essential steps to follow:

1. Perform a data audit

First, you'll need to assess the current situation by carrying out an audit. Take note of where your data is stored (on-premise or in the cloud), who has access, and which tools are currently used for collection, management, and analysis.

Look at which parts of the system are working well, and identify any bottlenecks or inefficiencies. You'll also need to find out how data-literate your employees are and how much additional training is required across your organization. Finally, make sure to also include security and compliance protocols in your audit.

2. Define your data democratization goals

A successful data democratization strategy needs clear goals so that everyone knows what they're aiming for. You should align these with your overall business goals as much as possible - for example, faster access to customer data helps support service agents, which in turn improves brand reputation and revenue.

You can also include specific objectives for data democratization, such as empowering employees to feel confident in analyzing data, or ensuring that everyone knows how to use the relevant software. Once you know your goals, you can design a roadmap to get you there.

3. Centralize your data

Bringing all your data into a central hub is a key part of data democratization. Cloud storage is ideal, as it's hugely scalable, accessible from anywhere, and has a low cost of entry. End users also only have to visit one platform, rather than switching between tools.

It is also vital that the data is well-organized and searchable. The right software for data democratization will enable data teams to label and categorize the data, making it easier for users to find what they need.

4. Enact data governance policies

Although some organizations are concerned that data democratization could lead to security and compliance issues, the process actually makes your information safer. This is due to the focus on data governance and quality as well as ensuring that the right data is accessed by the right people.

A robust data governance policy should set guidelines for how data is stored and protected, who can see (and edit) which data, and how it should be used. It should cover the tools you use and outline how standards and processes will be enforced.

5. Maintain ongoing training for employees

It's essential that you invest in full and regular training at all levels of the organization so that everyone has the required data literacy to identify, discover, and analyze the data they need. All end users should be confident enough to handle data effectively and securely.

Train users on using the relevant data democratization tools, as well as on general data awareness. For example, they should develop an understanding of what data the organization collects, and how and why it does so, as well as examples of data democratization in action and the benefits it provides.

Johnson & Johnson supply chain optimization: a data democratization example

Like any global business, Johnson & Johnson is reliant on an efficient supply chain. As a provider of consumer goods and pharmaceuticals, they need to keep shelves stocked and ensure that vaccines are temperature-controlled and delivered on time.

Supply chain optimization is only manageable with accurate data - but fragmented systems and manual data analysis made it hard for the company to get actionable insights. The aim was to democratize data across the entire organization by creating a common data layer.

Thanks to migration from a legacy Hadoop infrastructure to the Databricks Lakehouse Platform, Johnson & Johnson has streamlined data pathways and removed barriers to data access. Databricks SQL provides scalable data warehousing capabilities, feeding data directly into applications and BI tools.

The Johnson & Johnson supply chain optimization is an ideal data democratization example. The company has improved business performance by achieving:

  • 45-50% reduction in cost for data engineering workloads
  • Reduction in data delivery lag from around 24 hours to under 10 minutes
  • Improved understanding of consumer and business needs.

How democratizing your data will strengthen your AI strategy

One of the important benefits that data democratization can provide for organizations is strengthening and supporting AI strategies.

Improved data quality and greater trust in data insights result in better datasets for the training and validation of AI models. With better training data sets, the accuracy and reliability of AI applications is also increased.

Data democratization also frees up data teams to focus on the development of AI applications instead of supporting other teams across organizations. With greater access to data, other teams can also support AI development with collaboration, experimentation, and innovation that would otherwise be unavailable to them.

Achieve data democratization with Databricks

As we've seen, the two main tenets of data democratization are centralized data and robust governance. Databricks products enable you to check those boxes and more, empowering your users to discover, access, and collaborate on trusted data.

The Databricks Data Intelligence Platform is built on the lakehouse architecture, which enables you to store all data in one location. With a single architecture for integration, storage, processing, governance, sharing, analytics, and AI, you can eliminate silos and reduce data duplication issues.

It's easy to share live data from your lakehouse to any computing platform without replication - and your information is highly secure thanks to data classification access policies.

Databricks lets you establish a single copy for all your data using open standards, and a unified governance layer across all data teams using standard SQL. Meanwhile, Unity Catalog (the industry's first unified governance solution for data and AI on the lakehouse) helps you seamlessly govern structured and unstructured data on any cloud or platform.

To discover how Databricks' cost-effective, future-proof architecture supports data democratization, get in touch for a free trial.

FAQs about data democratization

Why is data democratization important?

Data democratization gives the right people access to the right data at the right time. It makes it easier for end users to find trusted information and equips them with the skills and tools to understand and use it properly.

The process fosters a culture of data-sharing, which improves collaboration and helps users make data-led decisions. It also means that data experts don't spend all their time responding to requests for access or assistance.

How to democratize data across your organization

Understanding the primary purpose of data democratization and knowing how to achieve are very different. A brief guide would be to start by auditing your current data landscape and identifying bottlenecks and silos.

Next, define your data democratization goals and align these with wider business objectives. Choose a suitable platform for centralizing data, and make sure to keep it organized and standardized.

Finally, put governance policies in place to ensure security and compliance, and provide education around data democratization tools so that everyone in your organization becomes confident in working with data.

Guide your readers on the next steps: suggest relevant content for more information and provide resources to move them along the marketing funnel.

Try Databricks for free

Related posts

See all Data Strategy posts