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Blog / How to Implement Data Democratization

How to Implement Data Democratization

Most modern organizations understand that being able to make data-driven decisions is a massive competitive advantage. Data insights help to optimize marketing activities and increase ROI. However, unless your organization has a well-thought-out system to democratize its data, then this can become a major challenge. 

If employees don’t have a way to gather relevant data about their business from a single source of truth, one of two things will happen:

  1. IT departments and analytics teams end up overwhelmed with requests for data queries and reports, leaving less time to focus on more value-adding activities. 
  2. Employees spend their time pulling and formatting data for reports and queries themselves. Besides being time-consuming, this kind of maverick reporting often leads to errors in data and differently formatted KPIs which can’t be directly compared across the business. 

So, if you want your business to follow a data-driven strategy, then you need to think carefully about whether you’re actually giving the right people enough access to the right data, in the right format. This blog will look at what steps you can take to build a roadmap around data democratization, and some of the key considerations you’ll need to bear in mind for a successful data democratization strategy.

To find out more about what data democratization means, you can check out our blog here.


Not sure what data democratization is? Check out our video!

How to Implement Data Democratization:

1. Understand your business setup

First things first — before you even start, it’s important to get a lay of the land. This means understanding what tech you already have, what data streams you’re going to be working with, and how your business is set up in different teams and regions. Some companies are so big they don’t know off the bat who has governance over what, how data is connected, or who the end users are. This is all key information to gather and understand before you scope out the project.

2. Define the business goals that you want to meet

Now you need to figure out what goals you want to meet, and how data can help track and support meeting these goals. Make sure to consult with people across the organization in this phase. The aim is to create a framework that considers local needs while laddering up to a unified set of goals.


3. Understand your audience

If you’re a large organization, then it’s important to realize that everyone doesn’t need to see everything all the time. Being too generic can make people tune out, so figure out what goals and challenges different employees will have, and what sort of data view people in different teams, regions, and roles will need in order to best meet your wider business goals. Bear in mind that not everyone is a specialist, and some users will require high-level insights while others crave the detail, and training needs to be tailored to each group. This is the time to start building proper data governance into your strategy. Decide who should be allowed privileges into sensitive data, and how permissions can be defined to conform to this. 

4. Build backward from these needs to workshop data architecture

Now it’s time to workshop solutions. Think about what architecture you need to give employees access to the data they need in order to meet business goals. By now you should have enough context to answer more concrete questions, like which tech you want to use for data storage, how you want to process and harmonize data, how you’re going to display and specify for different communities of users, and whether you require any training programs or technology change management frameworks to ensure adoption.


Check out our agency guide to data storytelling!


Why tech alone won’t drive data democratization

While theoretically data democratization could be done manually, this is resource intensive and non-scalable option. It takes too long and opens data up to too many errors. Without the right tools to take on the growing mountains of information coming from countless platforms, it’s easy to find yourself drowning in data. However, investing in tech alone isn’t enough to deliver the benefits businesses can get from data democratization.


"You might have the greatest tech stack in the world feeding you business-changing forecasts on your ad spending and delivering real-time performance data, but if this isn’t translating to actions, then it’s useless."

Alexander Igelsböck, CEO, Adverity

If you’re overwhelmed by data, then it might seem like a sophisticated enough data platform is the silver bullet. In fact, recent research has shown that the main barrier stopping CMOs from getting value out of their data is a data-driven culture, while one in five CMOs say that getting the right people with the right skillsets on their team is the most challenging.

Building a data-driven culture is one the biggest challenges for CMOs

Building a data-driven culture is one the biggest challenges for CMOs

For investments in data democratization to be successful, there must be a harmonious mix of budget allocated toward the ‘hard’ IT factors such as hardware and software, as well as ‘soft’ IT factors such as upskilling and training its human capital. Skewed investments lead to either having digital systems that few know how to use or feel confident acting on. The diagram below shows the limitations brought about by investing in just one or two of these key factors, instead of all three. 


A mix of people, technology, and culture is needed to become data-driven

A mix of people, technology, and culture is needed to become data-driven

What are some of the ‘soft IT’ factors you’ll need to democratize data?

Companies can’t aggregate and compare data like KPIs if each region, department, or team is cutting their data slightly differently when calculating those KPIs. Data needs to be standardized so that every employee within an organization is reporting on the same information from the same source. But what, besides good data architecture, does this require?

Data culture: building trust in a single source of truth

If getting a single source of truth has previously been an issue within the company, then it may take some effort to get everyone on board with data democratization. 

Without a supportive culture, skeptics might be hesitant to act on new data insights. This is why investing time and effort in comms to support a data-driven culture will be important from the start.

Carefully consider your audience's perspective, and then get employees engaged. Whether that’s through interviews, surveys, or workshops. The goal is to map out a global standard and make sure people understand what that looks like. You can do this by sharing how the systems around data democratization are being used, and the benefits that other employees are seeing. Show your organization the standard of what good data democratization looks like. 


To have users adopt a system of data democratization, it needs to work for them. If it doesn’t, your employees will go back to pulling data manually. This is why it’s important to review each team’s needs early on in the process of data democratization. By taking these into consideration, you can think strategically about the way your data is mapped so users get what they want. But ensuring adoption goes beyond the conception and rollout of democratized data.

To make sure that data is actually driving better decisions, organizations need to measure if users are acting on insights. Here are a few questions that you’ll want to keep asking:

  • Are employees downloading data?
  • Are actions being taken based on that data?
  • What kind of decisions are being driven by data?

And if the answers to these questions indicate low adoption rates, then it’s time to consider why this is. If it’s not an issue with the data architecture, or with the data culture, as discussed above, then it’s time to consider if employees need additional training.

Employee skills and data literacy

Data literacy is another consideration that needs to be made both at the conceptual stage, and thereafter. Data literacy, according to Gartner, is “The ability to read, write and communicate data in context, with an understanding of the data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case application and resulting business value or outcome.”

It could be that a lack of data skills is stopping people from accessing and acting on data insights. If two employees have the same view of a dashboard, but only one of them can understand it, then they’re not getting the same information, ie. data has not been properly democratized. 

There’s a balance to be struck here — organizations must figure out how to show relevant information to less data-literate employees — but inevitably there will be some employees who need training to understand even the more basic views of their data.

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