As technology advances and digitization takes over, there is an expectation that our lives will be more simple. ‘Self-service’ capabilities like Self-Service BI are the manifestation of this expectation within many technologies. For most, ease of use is no longer enough. Now tools must be simple to use, and flexible enough to cater to a wide range of skills and intricacy of analysis.
In fact, our recent report shows that the majority of people expect the following functionality from their analytics tools:
- 61% Customizable dashboards
- 58% Interactivity features
- 55% Self-service analytics
Generic business intelligence or data analytics platforms make lofty ‘self-service’ promises, luring buyers with cheap prices, simple interfaces, and low implementation times. Organizations are promised a ‘one size fits all’ tool that will allow users to ‘drag n drop’ their way to data fluency.
In truth, these tools can satisfy basic data needs, but they struggle to keep pace with the needs of organizations with more complex data structures, multiple systems, and specific industry requirements. Those who embed these tools without doing their due diligence will find themselves mired in IT requests to get self-service BI up and running.
Defining Self-Service BI
Gartner defines self-service BI as “end users designing and deploying their own reports and analyses within an approved and supported architecture and tools portfolio”. Put simply, ‘self-service’ relates to true autonomy. Are your users enabled to generate the outcomes they need, completely on their own?
For growing organizations, it’s a mistake to confuse ‘self-service’ with ‘simple’. The truth is that self-service has no fixed definition and will mean different things to every team and employee based on their exact needs. So, to achieve true self-service BI, you need to embed a tool that can flex to meet the needs of every user bucket within your organization:
- Casual or standard users make up roughly 70 percent of all BI users. Usually, they have a limited BI skillset that corresponds with their straightforward requirements. Therefore, analysis, dynamic reports, and dashboards are sufficient to cover their self-service BI needs in most cases.
- Power users make up around 25 percent of all users (typically much less in larger BI environments). They are skilled BI users who need a lot of flexibility and functionality for their daily work with data to answer their business problems. Suitable self-service tools allow them not only to analyze data but to change existing (or even create new) reports and dashboards from scratch.
- Business analysts make up about bout 1 to 5 percent of all BI users. These are the users with the most advanced BI skills and requirements. They have the highest demand for flexibility and functionality in their self-service BI solutions. For them, self-service must cover tasks like data exploration, modeling, and deploying a sandbox environment for special use cases.
Self-service BI also presents issues and solutions in the embedded BI/analytics world. Many of the same problems exist (users want control so they can customize unique experiences for deeper insights and global themes so that the analytics blend with their application). You want to give users in your application access to seamlessly embedded analytics that offers personalized self-service and interactivity. Does self-service BI deliver this user-friendly solution to your embedded analytics users?
What to Look for in an Embedded Self-Service BI Tool
Given the flexibility of the term, an adaptive or situational approach to embedded self-service is recommended. There are four key elements every modern organization should expect from a true self-service analytics platform:
It Caters To All End-User Roles and Skill Levels
Many organizations take a one-size-fits-all approach to data analytics by embedding 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 only need basic dashboard interactivity to consume information. A second set might require 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.
It Provides 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 the truth.
Organizations seeking self-service must establish 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. By having a solution that can inherit your existing security model, you eliminate the need for redundant security management.
It Integrates Well With Existing Infrastructures and Tools
Often organizations embed 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.
Embed Self-service as Unique as Your Users With insightsoftware’s Logi Symphony
Every user is different. Insightsoftware’s Logi Symphony allows you to tailor analytics features to different skill levels or roles. Inspire your users to generate their own actionable insights. Logi goes beyond simple visualizations and offers control over the self-service experience including resizing, layout, filters, links, and creating cross-source connections, empowering self-service users to build their own dashboards with the level of sophistication that matches their needs.
You can drive value 4x faster with Logi Symphony so you can speed up your roadmap, without detracting from innovating your core features. Go to market sooner so you can set yourself apart. Additionally, you’ll notice greater adoption of your data analytics tools and happier, more data-driven end-users.