Why Data Democratization? Why Now? What Does It Look Like?

Data democratization ensures that business users can swiftly access data, empowering them to respond promptly. This power is a significant competitive advantage that smart organizations seek. However, with power comes responsibility. Organizations aim not only to provide users with data access but also to ensure that they have access to the right data. This is data democratization with effective guardrails in place.

What is data democratization?

Data democratization is the ongoing effort to make data accessible to everyone within an organization, regardless of their technical expertise. That means giving business users access to data so they can feel comfortable working with it, can discuss it confidently, and, consequently, make informed decisions while building data-driven customer experiences. In data democratization, the role of IT is to ensure that users have access to only the data they need, with organizational control.

The control is necessary to prevent wild-west scenarios. For example, the organization doesn’t want to lose control of the data and have it end up in unpredictable places, like USB drives and users’ personal devices. It wants to ensure the data is used in compliance with industry statutes like HIPAA and with privacy laws like GDPR. And it wants to avoid Garbage In Garbage Out (GIGO), in which users make bad decisions because they’ve analyzed the wrong data.

The biggest reason to include organizational control in data democratization, though, is efficiency. Most users can’t work efficiently with data because they don’t understand it. Why not? Because they didn’t create the databases, structures, schemas, tables and column names in the data sources. And even if they were in the room when the data sources were being created for their department, what about the data they need from other departments in the organization? A business manager trying to pull together a bird’s-eye view of customer preferences could easily need data from Sales, Operations, Finance, Marketing and E-commerce. That can be like needing five different languages to buy a cup of coffee.

So, if you’re going to give business users access to data sources, keep in mind that they’re experts in business, not in IT or database programming. You don’t want them to waste their time looking for useful data in a lot of useless places.

Why is data democratization important?

Data democratization is related to data empowerment, a three-pillared IT approach for giving users what they need to make the best decisions for your organization.

The first pillar is data governance. To get a 360-degree view of data, you must understand what data you have, what it means and how it relates to the business. For example, to answer the question, “Who is buying our products in each region?” you would look for data sources for Customers and Sales, then determine the fields to query.

As described above, governance also involves understanding the guardrails — the rules, policies and regulations that are associated with the data. Does the data on Customers and Sales contain personally identifiable information (PII)? Must we comply with regulations or standards on the use of that data? You don’t have your customers’ permission to use their personal data for whatever you want; there are restrictions on its use. Information privacy is a hot topic everywhere, and in the U.S., the regulatory map is becoming a minefield as individual states establish their own privacy mandates.

In short, data governance is about walking the fine line between getting the greatest business use out of the data and reducing the risks that come with that data. The risks include fines, penalties and damage to your reputation if you leave sensitive data unsecured and unencrypted.

The next pillar is data operations, which involves preparing data for use and ensuring it’s available to your business users. Giving users access to all the data in the organization doesn’t matter if a simple query takes five minutes to run. The systems that deliver the data have to perform well enough to meet the needs of the business.

Finally, data protection covers the mechanics of ensuring your data is backed up properly and your myriad endpoints are secured. It includes archiving your data, retaining it for compliance and being prepared in case of audits. And it extends to setting policies on sensitive data so that it cannot be used improperly. If privacy laws prohibit the free examination and use of customer information, for example, data protection technologies can mask PII like name, address and age while leaving sales data operable.

You want the IT resources in your organization to focus on those three pillars — data governance, data operations and data protection — instead of fulfilling users’ requests for query results.

That’s why data democratization.

What business challenges does data democratization address?

It isn’t that IT is in the way. It’s that there is so much data and so many different tools for using it that data democratization was inevitable.  Data democratization helps to solve the following challenges:

IT talent shortages

The shortage of IT talent is real, in positions ranging from programmers to system administrators. In smart organizations, the move to groom a generation of “citizen analysts” has the potential to become part of business strategy instead of a stop-gap measure.

Data access timing

Every opportunity has a shelf life, and you can miss it if you can’t get to the data you need in time. Recent history has shown the role that data analysis can play in slowing the spread of a coronavirus and devising a vaccine against it. While not every company faces the same array of life-and-death decisions, almost every company faces formidable competitors. Decisions made quickly and based on the right data are the key to coming out ahead.

Data scientists’ prep time

Data scientists spend as much as 45 percent of their time on data preparation tasks, including loading and cleaning data. While that is an improvement over the 75-85 percent they were spending a few years ago, it still represents a big chunk of time massaging data instead of analyzing it.

It turns out that, when you drill into that data preparation time, it usually starts with answering a question like “What data do I have that could help me solve this problem?” Most people don’t know what data is available in the organization. And, if they do know, they don’t know where it is or how to get access to it. So the next questions are “Who owns system X, system Y, etc.?” and “How can I get access to the data in those systems?”

When they access the data source, they’re likely to see an arcane description of the data in it, like non-intuitive table and field names. So they ask, “Is this the right Net Sales field?” and “Which one shows me sales before taxes? After taxes?” Then, to get to their target number, they may need to combine multiple pieces of data points. They wonder, “Are the fields in this table related to the fields in that table? How? How do I have to combine them?”

That obstacle course of preparation slows the process of data democratization. The process goes even more slowly if you have to phone the right person in IT to walk you through the data.

What are the benefits of data democratization?

Data democratization eliminates obstacles to data access, empowering individuals to utilize data effectively.

Data-driven decisions

When more people have more access to the right data, it lets more people make better data-driven decisions. Data democracy has the opportunity to turn data into a true competitive advantage for the entire organization. Additionally, stakeholders can explore data rather than just look at the data to get what they need.

Empowering data teams

Data democratization means freeing up the data team for more advanced data work rather than having to answer every single ticket in the queue. With good data democratization efforts, analysts can find themselves responding to less duplicate requests and have more time to spend on proactive, strategic projects.

Better ROI

More efficiencies from data-driven decisions across an organization and more time spent on proactive analytics work, means more ROI on your data stack investment.

Steps for implementing data democratization

Democratizing big data requires a well-coordinated strategy, assuming essential data governance practices are in place.

Here’s a concise 6-step roadmap for implementing data democratization in your organization:

  1. Secure leadership support: Align your approach with individual business unit needs to gain leadership commitment for the necessary investment in self-service analytics tools and training.
  2. Assess your data ecosystem: Manage the increasing volume of data by evaluating and addressing issues within your data ecosystem.
  3. Unlock legacy data: Extract value from legacy systems by budgeting for data integration tools and architecture design, ensuring interoperability.
  4. Enhance data accessibility: Democratize data by providing all users with access, utilizing user-friendly technology, data analysis dashboards, and visualization tools.
  5. Promote self-service: Encourage users to incorporate data analytics into their routine, fostering trust in enterprise data through data management platforms and quality software.
  6. Provide ongoing education: Prioritize effective onboarding and continuous training to ensure all users are comfortable leveraging enterprise data as a competitive advantage.

What does data democratization look like?

Ideally, it would be as easy for business users to find and use the right data as it is for them to shop online or find a movie to watch. Data democratization is about guiding the right data between the guardrails and putting it at users’ fingertips.

That means users would have a self-service shopping experience that includes features like these:

  • Browsing with sensible parameters until they find data of interest
  • Getting more information on the data — what it does and does not contain, and how it is derived
  • Seeing related data — “People who used this data also used this other data”
  • Using a shopping cart that shows the data you want and when you can expect to receive it
  • Joining a community of people who have used the data and can tell you more about it
  • Taking part in an entire ecosystem of business users instead of IT professionals
  • Seeing whether the data has been encrypted, in case you want to transport it
  • Determining whether the data has been anonymized so you don’t run afoul of privacy laws

That’s the ideal state.

Now, let’s be honest: It’s a lot less fun to shop for data than it is for power tools, hair care products and red slingback pumps. So, until we reach that ideal state, here’s another take on what data democratization looks like.

You start with this:

unorganized data sources to showcase data democratization

It’s the hodgepodge of modeling tools, report generators, cloud providers, ERPs and relational databases that business users in any enterprise have to sort through. Layered on top of them are hoops that users must jump through — the rules, regulations, standards, codes and auditing requirements associated with using the data.

Data democratization, on the other hand, looks more like this:

data democratization ideal state

On the left, database professionals can find the physical data systems and structures that make sense to them, and on the right, business analysts can find the entities they need, with the guardrails of policies and governance.

The combination enables data democratization — seeing how the data flows through the organization, where you can pick it up and which enterprise usage policies apply to it.


Through data democratization, you can deal with the abundance of data by giving more people what they need to make business decisions. With tools that demystify the structure and relationships among data points, users can analyze and make decisions as close to the data as possible.

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About the Author

Danny Sandwell

Danny Sandwell is an IT industry veteran who has been helping organizations create value from their data for more than 30 years. As a Technology strategist for Quest, he is responsible for evangelizing the business value and technical capabilities of the company’s enterprise modeling and data intelligence solutions. During Danny’s 20+ years with the erwin brand, he also has worked in pre-sales consulting, product management, business development and business strategy roles – all giving him opportunities to engage with customers across various industries as they plan, develop and manage their data architectures. His goal is to help enterprises unlock their potential while mitigating data-related risks.

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