Data democratization is the unsung hero behind the success of hundreds of today’s leading companies. Organizations that do it right see rewards across all areas of the business, from improved customer experiences to greater revenue and a stronger bottom line.
So what is data democratization?
Data democratization definition
Data democratization is when an organization makes data accessible to all employees and stakeholders, and educates them on how to work with data, regardless of their technical background.
Plainly put, the “data” in data democratization is any information you could potentially gather about your business or organization. It’s for everyone: from the administrative assistant who’s planning a birthday party for a manager to the engineer who’s researching new potential product features. Data is ubiquitous, and has the potential to streamline every corner of the business. It can be simple (your current customers’ email addresses) or complex (how many employees in the accounting department have completed a specific training module within the past two weeks).
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Data democratization: How did we get here?
Before data democratization became widespread, business users would waste considerable time searching for data, accessing the data, and waiting for approval to use it. Historically, IT teams owned most or all of the data. Business users would go to IT to ask for access to a certain data set and IT would hand over a large, unruly spreadsheet in return.
This process created a roadblock for business analysts and prevented the entire company from becoming data-driven. It meant data only sat in one area, and the rest of the company had to fight to access it.
Even though we’ve come a long way, many organizations still take an ad hoc approach, where IT owns the data (or at least some of it). This process creates roadblocks for business analysts because they can not easily access and use the data to make data-driven business decisions.
Some data leaders fear democratizing data because of security concerns: they worry about the misuse and mistreatment of data, especially sensitive personal data about customers or employees. But in reality, most data-driven organizations train their employees to use data the right way and to make informed business decisions with this data.
What is the primary purpose of data democratization?
Data democratization has several different outcomes, all of which lead to greater efficiency, profitability, and success for the business. Data democratization serves different purposes for different departments or roles across an organization. Here are some examples:
- Sales: Sales reps use data to quickly gauge the value and status of different opportunities in their pipeline.
- Marketing: Marketing teams use data to test different campaigns, and variations on copy and graphics within those campaigns, to reach their target audience more cost-effectively.
- Customer service and support: Whether helping a customer over the phone or in person, support teams use data to quickly gauge the facts about a customer to provide better service. It makes all the difference when support can quickly (and accurately) pull up customer data, including past activity or purchases, to get a big-picture view of what’s really going on.
- Human resources: Managers and recruiters can use data to quickly find and send the right messaging to potential candidates, as well as categorize and analyze the volumes of resumes they receive.
- Research & development: Innovation teams can use data to gauge which features or benefits are in highest demand, and adjust the product to match consumer expectations.
- Executive leadership: The C-suite can use data to quickly get a 360 degree view of the business and determine which strategic initiatives are providing the most ROI.
Why data democratization is crucial to your business
Companies who don’t give their employees adequate access to data (or education on how to use it) face many inefficiencies. These inefficiencies are caused by:
- Data silos: When data is siloed across the organization, stored in different enterprise applications, data warehouses and data lakes, it is nearly impossible for a user to know where to find the data they need to do their job.
- Controlled access to data: While controlling access to data is important from a governance point of view, if only IT has access to the data, it can create a bottleneck. In this scenario, once a business user finds the data she needs, she must go to IT for approval to use the data, which slows down time to insight.
- Insufficient tooling: Many data intelligence tools are not designed for self-service analytics. In some organizations, only IT has access to data and data intelligence tools. This means that business analysts must go to IT to get the data they need, rather than having the ability to access data at a level appropriate to their role.
These inefficiencies are all caused by a lack of access to the data. Data democratization solves this problem by giving all users within the business access to the data they need to do their job. It eliminates the “middleman” of the IT department by giving employees secure access to self-service analytics that help them make data-driven decisions.
What is the benefit of data democratization?
To talk about the benefits of data democratization, it helps to look at an example. Imagine you’re a sales rep at a software company with a list of 100,000 leads. You need to figure out which ones are most likely to result in a sale.
It would be nearly impossible (and incredibly wasteful) to call each contact individually, so you would want to figure out which prospects are serious and deserve one-on-one attention. Your organization has invested heavily in data democratization, so it will be easy for you to get the info you need. You simply log into Salesforce, filter your lead list based on:
- Past behavior — just leads who are actively using the free trial version of your product
- Interactions with your company — just those who have spoken to or emailed a rep already
- Deal size — only those deals that are potentially over $100k
This whittles your list down to just seventeen leads to follow up with. You can now focus your sales efforts on nurturing these high-value leads, and it all happened with a few clicks.
Four steps to democratizing your data
So, how do you actually implement data democratization? What does the process look like? Here’s a rough overview that you can adapt to the specific needs of your organization:
- Take inventory of your data and compliance landscape. Where is data housed? On-premise, in the cloud, or a mix of both? Which software tools and technologies are you currently using to capture, store, and analyze data? If you’re an enterprise organization, you may want to enlist the help of an IT consultant to provide a complete gap analysis.
- Gauge how data-literate your employees are. Depending on the needs of your organization, this may require something as simple as a quiz or as complex as an assessment from a reliable third-party consultant.
- Assess potential data solutions. Use technology review sites, ask your colleagues, and book demos of potential business intelligence tools to see what might work best for your organization. Consider budget, customer service reputation, scalability, and market prominence of each potential tool.
- Invest in proper training and ongoing education. Once you’ve implemented a new data democratization toolset, don’t leave your team in the dark. Ongoing training and check-ins are just as important as initial onboarding to guarantee you see a strong return on your investment, and begin to reap the benefits of data democratization.
5 best practices of data democratization
Use software to help you.
Even small companies produce a considerable amount of data every single day. These organizations look to use this data to innovate and grow. However, with data spread out across the organization and trapped in silos, it can be difficult to make data-driven decisions. As a result, many large organizations look to tools, such as a data catalog, to enable data democratization across their business.
Opt for agnostic products and solutions.
Agnostic solutions are interoperable among different systems and platforms, meaning they provide you with more flexibility as your organization grows and changes.
Think long-term.
While there may be temporary cost savings with some potential solutions, study the features and functionalities of your potential data solutions to ensure they’ll still work for you five or ten years down the line.
Involve your employees.
It’s important that you choose tools and processes that your employees will actually use. There’s no better way to gauge buy-in from your team members than to involve them in the process.
Consider user experience when evaluating solutions.
Data science can be complicated, but your analytics platform shouldn’t be. Book demos of different tools and include your team to determine which solutions make it easy to navigate, analyze, and run reports.
What are the pros and cons of data democratization?
Pros
- Better collaboration. Different teams and departments can speak the same “language” around data and address business concerns faster, at scale. When everyone is data literate, teams communicate better and innovate more. An abundance of new, data-informed perspectives leads to more creative and diverse problem-solving.
- Data is more reliable. When you use the right tools, everyone who needs access to data can quickly obtain the most accurate and recent data.
- Time saved. With a reduction in manual effort required to obtain and disperse data, employees get hours back to focus on more important initiatives.
- Money saved. Though data democratization requires an investment upfront (often in tools, technologies, and change management) it ultimately leads to more efficiencies across the business.
- More data-driven decision making. Data democratization makes it easier to focus business efforts in the right areas and justify strategic plans.
Cons
Although data democratization is ultimately more beneficial than not, it does raise some concerns regarding data ethics, misuse of data and compliance. Here are the key drawbacks to data democratization:
- Potential misuse of data. Many organizations house sensitive and personal data in their systems. Depending on the organization, this data can range from medical history to financial history, to someone’s home address and beyond. This data is private and must be treated ethically. With limited oversight and lack of access controls, people across the organization could access this sensitive data and use it how they please.
- Data security concerns. In many organizations, IT serves as the gatekeeper of an organization’s data. This creates a “system of checks and balances” that ensures that the data is always being used properly. Without IT in this role, other users can potentially access data and put it at risk of a security breach.
- Compliance challenges. Regulations such as GDPR and CCPA protect personal data from being misused. If everyone in the company has access to the data, there is a higher probability of someone using the data in a noncompliant manner. This can lead to regulatory fines and penalties.
Can democratizing data lead to a fair digital economy?
One of the most promising benefits of data democratization is its impact on the global economy. Open access to information grants people from all different backgrounds the ability to bring new value to their communities and the world. In an MIT Business Lab podcast episode, Parminder Singh argues:
“Data is a non-rival resource. It’s not a material resource that if one uses it, others can’t use it. If all people can use the resource of data, obviously people can build value over it and the overall value available to the world, to a country, increases manifold because the same asset is available to everyone.”
To democratize data at scale while ensuring your data is secure and compliant, you must invest in a data catalog with embedded data governance like Collibra Data Catalog. With our governance capabilities, data stewards can create the policies needed to protect an organization’s data from misuse. And through ML-powered automation, these policies are applied to data sets without manual effort from a data steward.
Collibra Data Privacy provides policies, guidelines, and purpose limitations to guarantee that data is only accessed by the right people and used in a compliant manner. These capabilities serve as the “checks and balances” needed for data democratization, but eliminate the bottleneck of having IT
