Today’s businesses need data for everything, from deciding who to hire to which businesses to acquire. It’s no wonder, then, that in this digital gold rush for data, organizations have dozens of systems of records integrated into every seam of the business. There are CRMs, ERPs, HRISs, CMS, and many more similar systems that collect data from hundreds of touch points and interactions to create a single source of truth for data. Unfortunately, in most organizations, data access is often sequestered to those with technical expertise, such as the IT and data analytics teams.
Data democratization is the concept of expanding data access for knowledge workers and decision-makers across the organization to help them make more informed decisions.
But is it enough?
After all, raw or surface-level data can only reveal so much. Without the ability to aggregate, explore, and manipulate your data, your insights will be severely hindered.
We believe data democratization is only the first step to truly empowering your business. Analytics democratization is the practice of providing users across the business with data and self-service tools to uncover deeper insights and make smarter, faster decisions. In this blog, we explore the three dimensions of analytics democratization and reveal how you can help your business maximize its data via self-service tools.
What is analytics democratization?
Analytics democratization is a method of empowering your business by equipping business users across the organization with the analytics tools and know-how needed to uncover insights that aren’t readily available from out-of-the-box dashboards or high-level summaries. Three components comprise analytics democratization: data democratization, access to self-service data analytics tools, and upskilling. Let’s explore each.
Data democratization: Expanding access to data
In our 2024 decision intelligence report, we surveyed 2,800 enterprise business leaders to find out how they’re using technology to make decisions. The overwhelming majority of respondents (80%) said access to data improved their decision-making.
You might be wondering if expanding data access to more business users is practical. It’s a terrifying prospect for many IT leaders or data engineers responsible for protecting the data to open the proverbial floodgates to the rest of the org. The more people who get to access and use it, the more things might go wrong. Security risks. Cloud bills. Data integrity issues. You name it.
That’s why it’s common for governance and democratization to appear to be at odds. Yet the more people who access and use data, the more things can go right for your business. Higher user adoption. Improved data quality. And more relevant business insights.
Democratization and governance aren’t an either/or situation.
Governance is not about security restrictions or blocking users. Instead, governance is a big-picture framework that companies can use to scale data access responsibly. Effective data governance includes procedures like:
- Determining data access policies: Do the right roles have the right level of permissions?
- Establishing data quality standards: Is the data in a consistent format across the organization? Is it accurate? How will it be used?
- Meeting data privacy regulations: Does the data respect individual privacy and third-party regulations like GDPR?
Governance is all about ensuring when more users access data, they do it in a smart, secure, and safe way. When combined with democratization, you can create exponential value by helping business users achieve their goals.
How to improve data democratization
As you increase access to data, make sure you’re maintaining the right levels of permissions. In addition, look for tools that don’t just make it easy for the end user to access data. The right tool will also make it easy for the administrator in charge of adding licenses to monitor data usage, give/restrict permissions, and ensure things are running correctly. Prioritize governance capabilities that make these responsibilities easier:
- Visibility: Is it easy to see what users are doing? Can you quickly view what jobs are running and which ones are scheduled?
- Scalability: Can you build pre-approved apps that allow thousands of users to ask their own questions without touching the underlying data or workflows? Is role-based access control built into the data solution so different users have the appropriate permissions?
- Data lineage: Can you track what happened to the data at each stage and get a simple summary of workflows? Or are you digging through messy SQL and local spreadsheets to understand what’s going on?
- Your preferred UI: Your business users might be doing their data tasks in a more business-friendly, no-code platform — but that doesn’t mean you have to be on their platform. If you spend your time in Snowflake, can you monitor activity from other data tools or from within Snowflake?
Expand access to self-service analytics
It’s a tradeoff as old as time. IT and the data analytics teams only have so much bandwidth to help business users find data insights and build dashboards, so they have to prioritize who to help first.
Meanwhile, data pros are experts in data engineering, analytics, and data science may not be as well versed in monthly cashflow forecasting or inventory planning, meaning they may not understand different functions as well as the business experts. In organizations where IT is the de facto source of data insights, either not every business team is getting the timely insights they need or the insights may be suboptimal for the end users and how they make decisions.
Enter self-service analytics.
These intuitive, code-free, and code-friendly tools are designed to give everyday business users access to data-driven insights even with limited technical know-how. With self-service analytics platforms like Alteryx, marketers, salespeople, and domain experts across the org can dig into their data, ask deeper questions, and steer their business with confidence.
For example, marketing can optimize digital campaigns and segment customers based on which customer profiles have the highest potential lifetime value. Likewise, sales can score its leads to focus on the highest-impact prospects and uncover new cross-sell and upsell opportunities.
How to implement self-service analytics:
- Prioritize ease of use and scalability: Think of business users first. What do they need? They likely haven’t had years of experience or training in data analytics. Choose a self-service analytics solution that’s easy to use so business users can access data quickly and get started easily.
- Make sure your solution integrates with your existing infrastructure. Reducing the strain on IT and data teams is a major reason behind analytics democratization. So, ensure you choose a solution that seamlessly links up with your internal infrastructure and applications, such as Salesforce, Workday, Netsuite, Tableau, Snowflake, and others. This will help reduce the burden on IT and data teams during the deployment and integration processes.
- Consider future analytics needs, including AI: Self-service analytics capabilities are constantly growing, especially with the advent of generative AI. Consider choosing a self-service analytics solution that has a proven track record of implementing AI in a way that makes it easier and faster for business users to glean insights from their data.
Upskilling: Employees’ need for growth
Finally, workers want to grow and develop. In fact, they need it. The American Psychological Association’s 2023 Work in America Survey revealed that 91% of workers believe it’s very or somewhat important to have a job where they have ongoing opportunities to learn.
This enduring desire for employees to develop coincides with the rapid evolution of skills. According to a report from the World Economic Forum, “Six in 10 workers will require training before 2027, but only half of workers are seen to have access to adequate training opportunities today.”
Upskilling is a differentiator, a cure to both a rapidly changing workforce and a growing talent shortage. It’s also the key to helping your employees make sense of their data via self-service tools. If employees have access to data and the tools to analyze it but not the training, they’ll fail before they begin. While you can approach upskilling in any number of ways, for analytics democratization, we recommend focusing on data literacy and analytics maturity.
Data literacy is the ability to work with, analyze, and communicate with data. Greater data literacy helps workers understand, interpret, and act on data within a business context to optimize business outcomes. It also positively correlates to corporate performance — according to The Data Literacy Project, improving data literacy leads to an increase in enterprise value of $320 to $534 million over organizations with lower data literacy.
Typically, if an organization has more robust analytic capabilities, they’re said to be more analytically mature, but the same can be said of individuals, especially when you approach analytics maturity by capability.
Each stage builds upon the previous and helps users gain incrementally better and more powerful insights from data. However, each stage is a bit tougher than the previous and requires more expertise. While these capabilities take time to build, the payoff is worth it. Just ask the team at DoorDash, who saved millions using Alteryx.
How to upskill your workforce:
There are many approaches you can take to upskilling, such as hosting hackathons or giving your employees access to learning resources and courses through sites like Udemy or the Maveryx Academy. The most important elements, though, are buy-in and commitment. Here are a few ideas to help you get started:
- Commit to your employees’ development: Your upskilling approach should include support from executives, managers, and employees in the form of time commitment and appropriate funding.
- Create a COE (center of excellence): Consider creating an analytics COE with members from HR, the data analytics teams, IT, and other cross-functional teams to improve and optimize analytics across departments. Within your COE, teams and users can collaborate, share resources and best practices, and learn from their peers.
- Partner with experts: At Alteryx, we’ve worked with hundreds of businesses to implement self-service analytics. Our Services Success Bundles give you access to our dedicated team, where we work alongside individuals or the organization to provide training, workflow optimization, and hands-on development of Alteryx in new departments.
- Join a community: Finally, your organization doesn’t have to go it alone. Consider encouraging your employees to join a community. Our Maveryx Community boasts over 400,000 members. You can scour the forum and different threads to see how other data teams are solving their toughest problems. You can even pose your own questions for fast and creative answers.
Empower your business with analytics democratization
Analytics democratization requires access to data and self-service tools combined with the knowledge to use both. With the right analytics solution, this process can become even simpler.
Alteryx is a low-code, no-code enterprise analytics platform that helps everyday business users access data, automate data preparation, build analytics workflows, and find new insights with drag-and-drop analytics. Plus, you can keep your data and your customers’ data fully protected with the latest security standards and certifications.
Give everyday business users the power to solve anything with the Alteryx self-service analytics platform.
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