What’s keeping your application from providing a richer, tailored data analytics experience? End users require a customizable analytics solution that fits their unique roles, skills, and data needs. This principle extends to self-service analytics capabilities. With self-service analytics, users have the power to explore data and generate reports on their own without extensive IT involvement or heavy data modeling. When choosing your next analytics solution, keep in mind these pillars of self-service analytics.
#1: They cater to all end-user roles and skill levels
Many organizations take a one-size-fits-all approach to data analytics by purchasing a tool that doesn’t exactly meet their users’ needs. It might be too complicated for some who want only to review a dashboard with high-level key performance indicators (KPIs). Or it might be too simple for others who prefer to interact with and analyze data to uncover new insights on their own.
To achieve success with self-service analytics, understand how your end users’ data needs vary across skill and job levels. One set of users might need only basic dashboard interactivity to consume information. A second set might need higher-level reporting to filter, sort, and group data about teams, departments, or locations. And a third set might need a deeper level of data access and insights to drive your organization. Whatever their needs are, provide your end users with tailored self-service capabilities for a more productive, engaging, and satisfying data experience.
#2: They provide both control and governance over data
Data governance and control are critical to balancing your business needs for data access with the IT team’s need for appropriate data security. The key is finding the right balance.
Some organizations tightly control access to their data, which can frustrate users who want to run their own queries to combine data sets or create dashboards from a single set of data. Others set up their data analytics with no control over their data. Users can pull data from their cloud-based apps, Excel, and other sources. However, with all these data sets floating around, they no longer have a single version of truth.
By choosing self-service analytics, you can establish the essential security controls and auditing measures to ensure users have the right data access privileges. Be transparent with your IT team so they understand what data your users can have access to. Also, by having a solution that can inherit your existing security model, you eliminate the need for redundant security management.
#3: They integrate well with existing infrastructures and tools
Often organizations set up different tools to meet the various data needs of their users. Problems occur when they adopt separate solutions that don’t work together and are difficult to maintain. These solutions can have connectivity issues with existing data sources, fail to adhere to the current data structure, or lack the ability to scale on existing server environments.
Give all users access to the data they need in one solution. By embedding self-service analytics into your application, you leverage your existing IT infrastructure and security framework, as well as connect easily to your data sources. You save significant time by not developing your own solution or maintaining multiple solutions. Also, self-service analytics scale to meet the data needs of your users and organization as they grow and transform.
#4: They focus on the needs of your application team, end-users, and your business
Analytics aren’t just about what IT wants but what the business needs as well. Solutions that focus only on IT—not on the business—result in lower user adoption rates and exhaust IT teams to provide the custom reports that business teams need.
For a successful data analytics implementation, create a balanced deployment team. This way, the IT and application teams leverage their technical strengths with the business knowledge and requirements of your business users. Also, include input from your end-users who can shed light on the type of user and data experience they want. By weighing the needs of these three groups, you’ll quickly see how diverse their needs are and how essential a self-service analytics solution is in meeting them.
#5: They’re intuitive, easy to use, and require less training
A big mistake when searching for an analytics solution is choosing one that’s difficult to navigate and requires extensive training. In a recent survey from Hanover Research, over 85 percent of respondents reported user-friendly analytics and easy navigation as “very important.” Less than half of them reported that their current solution has these capabilities. Self-service tools should be intuitive, giving users the ability to easily access the data they need, create business insights, and generate reports.
Empower your end-users with self-service analytics that are intuitive to learn and easy to navigate. Your end users can take advantage of eye-catching visualizations that make it easier for them to gain insights and greater value from your data. Also, by integrating them seamlessly right in your application workflow, you’ll see increased user adoption, higher engagement, and improved user satisfaction.
On your quest for a new analytics solution, hold to these five pillars of self-service analytics. By doing so, you can trust your application will deliver the analytics that your application team, end users, and business demand. Plus, you’ll notice greater adoption of your data analytics tools—not to mention happier, more data-driven end users.