We’ve all been there before: You need a report done yesterday for an executive presentation, but the data team doesn’t own a time machine, and there are two dozen other requests in the backlog. When you get the report or dashboard created, there are more questions in the data than answers. Now you have to go back to the data team for a different view, but the backlog is still there, and now it’s taken a month to find the answer to your original question.
Self-service analytics is the attempt to break that loop. The concept has been around for the better part of two decades. Initially, self-service analytics was about enabling business users to generate reports. This led to a generation of business intelligence tools enabling point-and-click dashboard creation. The late 2000s saw the rise of these tools with Tableau and later PowerBI leading the pack. Today, the evolving definition of self-service has put the tools of data discovery and insight generation in the hands of the business user. The industry-leading business intelligence tools are trying to chase smaller, more agile companies that are building a core of tools designed from the start with next-generation self-service capabilities in mind.
The benefits of self-service analytics are substantial. With a self-serve model, you can start to truly scale your organization’s data-driven decision-making approach. Self-service can allow organizations to become data-driven by eliminating the bottleneck of report/dashboard creation via the limited resources of the data team. Implementing next-generation data discovery and insight tools like natural language search and automated insight generation allows your organization to go even deeper on next-level answers to critical business questions. Improving access to data and insights across the organization will lead to more informed business decisions and better outcomes. At the same time, this shift to self-service will allow your central IT/data team to focus on more high-value priorities like streamlining data processes and enabling shared best practices across the business domains.
Many organizations exchange the freedom to self-serve new analytics for the control associated with a centrally run program. A centralized approach can often deliver consistency and governance across the organization. One of the primary fears is that giving up control over analytics will lead to poor results because users may not have the ability to build exactly what they want, and the data literacy level can be varied to a wide extent across the organization. There is also the potential to duplicate efforts amongst individual domain groups and create an overwhelming amount of content. In addition, there are privacy and security concerns about who should be able to access what data.
At the end of the day, you can’t just give the keys to the kingdom to everyone in your organization. You need to put best practices in place to successfully achieve your desired outcomes in a self-service analytics initiative.
In This Post
Data governance for self-service analytics
The foundation of a good self-service analytics initiative is data governance. A self-service analytics initiative cannot succeed without properly defined processes, roles, standards, metrics, and policies put in place by the central IT or data team. Privacy and security issues are top of mind in a world with HIPAA and GDPR compliance. You also don’t want Jim the part-time data analyst making changes to a critical table without him understanding the contents of the data.
One of the first major projects of a self-service analytics initiative should be defining roles and level of access for these roles. Data governance helps to define who can access the right data and what it can be used for. It also provides a business glossary of the data available and ensures data consistency, accuracy, completeness, and trustworthiness. Data governance for self-service analytics is also important to help you maintain a single source of truth for the data in your organization. A report verification process may help to ensure the integrity of the data while also maintaining consistency across teams. Additionally, the central IT or data team may want to publish data products for the most popular use cases in the organization.
Intuitive user experience
The value unlocked from a self-service analytics initiative will largely depend on the user experience associated with the underlying analytics tools used by data analysts and business users. Self-service analytics can turn data consumers into data explorers with the right tools available. With the right tools, traditional data consumers can run their own analyses. This will help to unlock critical domain expertise in the analysis process.
An intuitive user experience isn’t only beneficial to business users and data consumers—it’s helpful for data analysts and experts as well. Data analysts can also take advantage of simple-to-use data science tools to grow their arsenal of problem-solving capabilities. Additionally, depending on which self-service platform they choose, data experts will have more (or less) time to focus on high-value priorities. After all, choosing the right tool will go a long way toward unlocking value for the whole organization.
From natural language search to AutoML, there’s a plethora of features in next-generation analytics tools that open data access to more users in your organization. Natural language search allows anyone to search data with a “Google-like” experience. Recommended visualizations take away the guesswork associated with putting together a visualization, while automated insights can easily generate answers to difficult business questions with a click of a button. The combination of these features can provide business users with powerful tools to solve complex problems.
Additionally, tools designed specifically for data analysts can help streamline workflows. Low-/no-code data preparation and pipelining may help make their work more efficient. Natural language code preparation is an easy way to avoid tedious manual efforts for cleansing, joining, and preparing data.
Finally, AutoML can open access to predictive analytics capabilities that were once thought to be accessible only to data scientists. Modern analytics platforms can provide a tremendous amount of value to organizations kicking off a self-service analytics program.
Build an analytics community
The relationship between central IT or your organization’s centralized data team and individual business units is absolutely critical to any self-service analytics initiative. In a self-service analytics model, your organization’s central IT team is turned from a reactive organization prompted by business needs to a proactive organization promoting best organizational practices, focusing on data governance activities, and running other high-value projects. The central IT team must work with business units to gather feedback, publish verified datasets, identify valuable new use cases, and message changes to critical datasets as those changes occur.
Building an analytics community is challenging, but getting started early will help deliver a successful self-service initiative. There are a few simple and effective ways to begin an analytics community.
For starters, creating a channel on your organization’s messaging app where users can collaborate on use cases, share popular datasets, and promote high-value content can deliver immediate value. With data literacy being a concern, kicking off regular sessions providing training and enablement can help ensure best practices across your organization.
Finally, identifying and recognizing top users can help build organizational momentum for a self-service analytics initiative.
Go for quick wins
Successful self-service analytics programs are built day by day and don’t happen overnight. To that end, going for quick wins rather than business-changing wins will help to build momentum for the initiative across the organization. Focus your initial efforts on highly popular use cases that deliver immediate value for the largest number of users. Central IT can look to see which reports or datasets are the most popular and create curated views for business users to explore on their own.
The temptation to immediately try to publish as much information as possible could lead to over-inflated expectations. It is imperative to understand the organization’s immediate needs and start to address those first, and then work on the more strategic areas of opportunity. Starting small can help your organization create a feedback loop where business users and data analysts feel invested from the start of the program.
Choose the right tools
At the end of the day, the most important choice you will make in a self-service analytics initiative is deciding which platforms to deliver analytics to end users. Platform evaluators should create a list of prerequisites identifying must-have features to help make progress in these initiatives.
Does your desired platform need to access multiple data sources in order to create a single source of truth for your teams? Does your team have enough SQL training to effectively query, or will you require natural language search? Are your teams sophisticated enough to generate insights from static dashboards, or will automated insights deliver more value for your business users?
These are questions you, as the platform evaluator, must answer prior to investigating and investing in a new platform from your data ecosystem. You may also want to research multiple vendors and understand how they deliver value in a self-service architecture. There is a sea of potential platforms available on the market today, but only a handful will deliver truly transformational solutions to your self-service needs.
Summary
Self-service analytics has the opportunity to make your workflows more efficient while providing faster and better answers to end users. However, balancing centralized control of data with the freedom of end users to generate their own answers can be a fine line to walk. Following some of the tips above can help put organizations on a path toward delivering value for their entire organization.
For more information on self-service analytics, check out our webinar.
