Data democratization and self-serve analytics: tips for product teams

Enter data democratization—the process of making data accessible and understandable to everyone who needs it. 

This article tells you why data democratization is important for product teams, and how it enables you to be self-serving with analytics so you can spend more time focusing on building a product that creates customer delight.

What is data democratization?

Put simply, data democratization is the process of making data accessible to non-technical users within an organization without requiring the involvement of data analysts.

A Mixpanel survey on product metrics that matter found the top three data types leveraged by product teams are sourced from: 

• Internal sales, marketing, and leadership

• Customer conversations

• Product usage 

This data is relevant to various people across your organization, including product teams, who can use it to make better decisions about your product. But, what does data democratization look like in action?

Why is data democratization important for product teams?

Data democratization enables product managers to make data-informed decisions that go beyond preconceptions or gut feelings.

Product managers want to build a product that provides value to users. But even the most analytical product managers are still human, with their own biases, which can present issues when deciding where to spend money, time, and effort developing the next iteration of a product.

To make these decisions more confidently, you need a constant flow of data about how users are experiencing your product.

Here are a few of the benefits data democratization can bring to your product team:

• Make product teams more agile: ready access to data will translate into faster decision-making, making product teams more agile. Democratization will allow product teams to constantly be on their feet and minimize the time between ideation and development, which complements the agile framework’s focus on incremental efforts and improving on the go.

• Make data-backed decisions faster: product management and development is a time-consuming and expensive process. It involves a lot of critical decision-making with no room for guesswork. Here, data availability helps cross-functional teams make data-backed decisions rather than relying on gut feeling, resulting in faster goal achievement and result-oriented actions.

• Create a sense of ownership and responsibility: by making data available to all important product players in the organization, you’re sharing insights and encouraging responsibility and accountability. You can empower your team to make decisions independently based on data insights.

• Helps make customer-driven product decisions:  data from customer conversations and product usage are the two types of data product teams are most interested in. Listening to customer conversations and analyzing how they use your product helps you understand their problems and develop solutions for them. Easy access to this data will help you make customer-driven product decisions directly led by the voice of the customer.

• Helps create the ultimate product experience (PX) for customers: since product teams are most concerned with customer data, all insights can be used to create a more optimized strategy, improvise the roadmap, prioritize features that matter, and design with the user in mind. This will ensure you’re delivering what your customers need and improving their product experience.

Pitfalls of not enabling data democratization

Let’s look at what will happen if you don’t enable data democratization. Here are some of the main pitfalls of not following a data democratization strategy:

• Gatekeepers between you and the data you need: going through data analysts every time you need customer insights will keep your team from getting all the insights they need. And to request data from an analyst, you need to know what you’re looking for—which makes it difficult to make ad hoc queries, which could provide fresh and unexpected insights. 

• A prolonged decision-making process (and bottlenecks): traditionally, any data request in an organization would have to go through a data analyst. This would mean submitting a request to the data team—who may already have a backlog of similar requests from across the business—and then waiting to receive that information. Long decision-making processes cause bottlenecks and lost time waiting for insights. Any follow-up requests based on findings would then be subject to the same process, making it difficult to make speculative queries and creating an obstacle to using customer data to reinforce all your decision-making.

• Keeps the product team unaware: without data, you don’t know what your customers are thinking, if your actions are translating into results, and how users are responding to product improvements. This could result in incorrect prioritization and bad decision-making, directly impacting the product and user experience.

5 best practices to facilitate data democratization 

Just deciding to make data available to product players isn't enough—you need to take the initiative and implement your data democratization plan. 

Let’s look at some best practices to introduce and instill a data democratization framework in your organization:

1. Foster a data-informed mindset among product team members

Traditionally, data in organizations is controlled by those who understand it, while those who need it have no clue how to use or interpret it. This requires a mindset shift where access to data is not limited to the hands of a selected few, and democratized access is recognized as a critical business need more than a choice.

Before you start making data available to everyone on your team, ensure they understand the importance of using data to make decisions and how it should inform every facet of developing a new product.

Encourage product team members to back their arguments, recommendations, and decisions with data. As the product manager, you need to regularly bring data into the conversation so your team understands how you’re using it to influence your decision-making.

2. Decide how to manage your data 

Governing data is as important as making it available in the first place.

For this, you need to take the right data governance approach for your organization, product, and data requirements so it's clear who owns the data, who can view or edit it, and how to use it.

Here’s a 4-step data governance checklist for you to follow:

1. Identify how you want to use internal and external data for product management.

2. Develop a clear vision about how you want the product team to implement data for product-related issues and customer concerns.

3. Outline what you want the product team to achieve with data-informed insights.

4. Decide how you'll bring internal and external data together to make it available for everyone—preferably through a cloud-based data management system.

By creating a data governance plan, you’ll have a clear idea about how to distribute data across the organization and the product team. This will ensure it’s in the right people's hands for the right purpose.

3. Establish specific data roles or cross-functional teams 

Even though you don’t want to restrict data behind gatekeepers, you still need to establish who will be responsible for managing it.

Instead of giving these exclusive rights to senior leadership, your organization might want to create a specific role like a Chief Data Officer, a Digital Officer, or a cross-functional team of analysts, engineers, architects, and marketers who are data experts.

This role or team will be responsible for regulating and supervising data access and ensuring data democratization is used to meet product goals and business objectives.

4. Choose the right tech to store and manage your data

Data democratization is a non-starter if you still rely on analysts to collect, interpret, and store it. 

Modern analytics solutions can help scale any volume of data, manage exclusive rights, offer customizations and flexibility with data management, and give real-time insights on data usage.

A central system like Microsoft Access lets everyone securely access data and get the information they need. It also organizes your data, assigns a name filing system, manages access rights, and allows organizational flexibility in customizations and data access. 

You’ll also need data visualization tools like Tableau or Looker so team members can extract insights in an easily digestible format, rather than expecting them to wade into the labyrinthine world of the database and forcing them to gather insights from raw data.

Of course, you can also use tools like Hotjar (👋) to harness data directly from users as they experience your site and give the product team direct, easily accessible, and digestible product experience insights.

5. Determine what defines success with data democratization

Once you have all the systems in place, you need a checks-and-balances system to ensure that your data democratization efforts are getting the results you expect. 

For this, set KPIs to analyze the effectiveness of your approach. Here are some guidelines and ways to measure your success:

• Use surveys and heatmaps to harness qualitative and quantitative data to validate how accurate and effective the data insights have been to the product team.

• Identify gaps, mistakes, and improvement areas in your data management and democratization approach to improve governance.

• Create an environment of continuous learning and feedback to understand the issues team members face with data, and how best to deal with them.

Analyzing these insights will help drive better-optimized business outcomes, introduce improvements, assess the course of action, and allocate resources. Without this, it’ll be challenging to know how data is being used and if it’s even creating a difference in empowering product teams to make data-informed decisions.

Common mistakes product teams make with data democratization (and how to avoid them)

Data democratization is the key to enabling product teams to use and leverage data to make your product better for the customer’s needs. But it’s still a foreign concept to many organizations, which often leads to mistakes.

Even when there’s organizational awareness, poor implementation can lead to data silos, preventing you from having a clear view or a reliable source of truth. To avoid this, let’s look at some common mistakes with data democratization:

1. Restricting data to only a few people and not making it available across the organization

The entire purpose of data democratization is defeated if data is still only in the hands of a select few.

If you think you’ve democratized access to data but still need an analyst to interpret the data, or your team members still have to log a request every time they need user insights, you haven’t completed the process. 

Set up a system where data can be accessed by a few people in every team in a digestible format that can be directly put to use. This will make the product team truly self-serving, so they don’t have to rely on anyone else for their day-to-day decision-making.

2. Lack of technical and behavioral norms 

Data literacy is no longer a nice-to-have; product teams must understand how customers respond to the product and how they can improve it. 

Although data democratization doesn’t need any technical know-how, it would be best for employees to have basic data interpretation knowledge through training.

Without this, product teams will have access to data but won’t know what it means or how to use it in their daily product activities and decisions. Data illiteracy can act as a bottleneck, drain your efforts, and result in stagnant KPIs for data democratization success. 

The best way to provide this training is to invite data experts who can break down the process and cover the basics of data management. You can also allocate a budget for resource material and courses so team members can learn about it in their own time. 

Encourage your data-literate employees to share their knowledge and help out team members if they face roadblocks along the way.  

3. Distorted organizational leadership and lack of buy-in

Data democratization is an organization-wide initiative that requires buy-in from the product team, founder, individual heads of departments, C-suite executives, and internal and external stakeholders. 

If the organization’s leadership doesn't use data to back their ideas and decisions or isn’t on board with the idea of making data accessible to everyone, it can lead to a massive waste of time and resources.

Ensure everyone agrees with the idea before you start implementing the process. For this, you can get a data expert on board, present a case study, conduct meetings, and outline a result-oriented plan and vision for this data. 

4. Not using advanced analytics to make user-led decisions

Within data democratization, one of the most significant elements is advanced analytics like machine learning, customer behavior analytics, and predictive analysis—which can help product teams look beyond numbers and understand how they can derive business value from this data.

For example, studying website analytics to conclude you have a high bounce rate is not enough. You need to understand how customers behave on your website and interact with your new product elements to learn which problems they’re facing and what’s holding them back from more clicks and scrolls. 

When you combine data with advanced analytics, product teams get further insight into the customer's minds and build desirable product features that lead to positive outcomes.

Final thoughts

Data democratization is an ongoing process that requires long-term consistency to start seeing results. You can’t just make data accessible to everyone and call it a day. 

Your product stage, organization size, and growth trajectory significantly determine at what pace your democratization process will move and which approach you need to take.

However, it’s a crucial aspect of enabling product teams to make data-informed product decisions that matter for the user and help achieve business objectives.

With data democratization, you can visualize, analyze, and leverage data that’s most relevant to your customers, and make customer-centric decisions that assist each phase of the product lifecycle and make results more user-oriented.