Self service analytics (also called self-service business intelligence, or self-service BI) is a term commonly used among analytics vendors and organizations adopting BI, often in the context of being the next big thing in driving more people to use data to find insights.
But what is self service analytics?
How does self service analytics work?
And why does self service analytics matter?
In this blog, we explain the meaning of ‘self service’, defined in the context of business intelligence solutions, to help you understand its differences, purpose, and benefits.
What is the meaning of self-service analytics?
Self service analytics is an approach to business intelligence (BI) that enables all analytics users the ability to access, analyze and share their data, discover opportunities and extract actionable insights - without requiring expertise or skills in data or analytics.
Today, there is an increasing reliance on data and analytics to inform business strategies, and requirements for data reporting are changing rapidly and becoming more fluid. This has driven an important shift away from traditional IT-centric reporting to decentralized, self-service tools.
The role of self-service analytics is to democratize access to data across an organization, not just to those who are knowledgeable in data reporting. It aims to empower the average person to participate and have a greater impact on the analytics process so they can improve business outcomes, and use data regularly for informed decision-making.
What makes an analytics solution ‘self service’?
The key difference between traditional business intelligence and analytics solutions and BI solutions that tout self-service analytics is that the latter provide analysis tools that can be used to explore, visualize and share data quickly and easily, regardless of the user's existing data knowledge or skill level in data and analytics.
Self-service analytics tools are typically characterized as containing the following:
- Augmented analytics capability that streamlines analysis (via automation and AI)
- Flexible and straightforward data connectivity (ample APIs and connectors support)
- Drag and drop UI with a low code interface (and code mode for more advanced users)
- Simplified data querying (using machine learning and natural language technologies)
- Straightforward dashboard and report building (via drag and drop design canvas)
Whether it’s building a dashboard, reading a report, visualizing data into a graph or chart, or sharing insights with others, self-service analytics tools make the process of basic data analysis as accessible and powerful for the average person as it is for a trained analyst.
What are the benefits of self-service analytics?
Self service analytics fill the gap caused by the shortage of trained analysts that many organizations face today, by ensuring the benefits of data analysis are available to more people who need it for decision-making. Adopting a BI solution with tools that truly are ‘self-service’, such as Yellowfin BI's Guided NLQ capability, brings a number of advantages to your business or user experience of your software, including:
Better decision-making: Providing self-service analytics improves accuracy, agility and efficiency for decision-making by delegating analytics tasks directly to the business users who are experts in their field or sector, and who have a deeper understanding of the data in question. It also fosters better communication and collaboration among business and technical people.
Data-driven cultures: Analytics solutions that provide true self-service data visualization, dashboards and other analysis tools allow more people to read, manipulate, share and evaluate their data without having to rely on experts to create and/or explain reports for them. The emphasis on simplicity and streamlined tools means the average analytics user can explore and use their data for decision-making more quickly than with traditional solutions, avoid analysis paralysis, and improve their data literacy and analytical skills for better data-driven workflows.
Reduced reliance on experts and intuition: Providing your users or customers access to self-service dashboards and reporting means they won’t need to request assistance from a centralized analyst or IT team every time they need to build a report or find answers in their data. Making the process of data analysis as simple and accessible as possible means they won’t rely on intuition for their decisions, and more likely they will consistently engage with and use the analytics solution you provide.
Speedier analysis and reporting for analysts: Self-service analytics also helps your advanced or technical users (analysts, IT, developers) because it frees them up from having to explain data or build reports, so they can focus on more important strategic initiatives such as strengthening data security and data governance, or driving innovation elsewhere in the business or software product lifecycle. Additionally, when they use your new self-service analytics suite, they can leverage its streamlined capabilities to the fullest to speed up their typical data cleansing, report building and analysis processes.
What are the challenges of self-service analytics?
By 2023, overall analytics adoption will increase from 35% to 50%, driven by augmented analytics solutions that make these tools self-service and accessible for more people, according to Gartner.
However, while self-service analytics solutions are driving the value of BI platforms and data analysis to more people than ever before, realizing all their benefits can only come after your organization or product team are able to meet several considerations.
Clean data: Your average end-user will only be able to find actionable insights with their self-service BI tools if what they explore is accurate in results and reliable for decision-making, making the process of cleansing and curating data to prepare it for analysis an essential process. Typically, this involves preparing a data catalog or semantic layer to map complex raw data-sets into easily identifiable categories for the average business user to explore with ease.
Data security: As an extension of cleansing data, your IT or development team must be able to maintain data governance over data-sets so that the proper role-based access controls, user permissions and protections can be put in place over what data is made available for reporting. This also has the added benefit of establishing proper data governance and security over what data is available for reporting. Maintaining different self-service capabilities without management and control frameworks to govern self-service analytical content is one of the top reasons for self-service analytics failure, according to Gartner.
Collaboration and communication: Just because the BI solution you offered touts self-service analytics doesn’t mean your users will be able to get up and running with its tools right away. It’s the responsibility of your team to communicate to end-users each facet of the analytics solution, the value it presents and how it simplifies their workloads to ensure they engage with the tools and share data, breaking the data silos of traditional IT-centralized BI.
Poor data culture and data literacy: It’s important to carefully guide the transition from traditional BI to self-service analytics if your existing data culture is prone to resisting change or data knowledge in general is low, as the value of the new technology solution adopted must be made clear and understood by all if you are to guarantee its use among your intended user base.
Why is self-service analytics important?
Everyone needs to be able to explore, consume, share and get value from their data.
For enterprise, this need applies to everyone in your organization - not just your analysts, developers, or technical staff, but also your non-technical business users and C-suite.
For product owners, having self-service analytics is also important for your customers - if they can’t actually use your software application’s in-built analytics, how can they gain value from it?
The point of self-service analytics is to open up the ability to perform data analysis and find actionable insights to more people aside from trained data analysts, and reduce your non-technical users’ reliance on IT or analysts to create or explain their reports for them.
The reality is data is rapidly growing in both complexity and quantity. Self service analytics means you can help make important data more easily available to every type of user.
How do I deploy self-service analytics?
The most important aspect of getting self-service analytics right is to match the type of tools to your intended userbase’s ability. Less technical users have a varied range of analytical ability, for example, meaning your enterprise or product team need to establish the right level of capability, support, permissions, and access rights and privileges offered for each type of user you have.
Building vs buying analytics is relevant to the self service analytics discussion, too. Building analytics tools in-house requires significant investment and expertise, and going another step further to build tools that everyone can use is a big task, which is why we recommend assessing specialized analytics vendors that provide ready-made self-service tools for all markets.
There are typically three analytics personas to cater for when it comes to adopting self-service analytics solutions:
1. Consumer: This is the average non-technical business user who can read, drill down, sort, filter and share analytic content (dashboards, reports, visualizations, etc) that is built for them. Because they typically can’t or don’t build reports, they only require streamlined BI tools that make the consumption of pre-built data, and further analysis of that same data for insights, as simple as possible, and don’t need formal content creation rights.
2. Explorer: This is a business user with intermediate experience with analytics tools and reporting. They use pre-built data models similar to consumers, but can also visually explore data, create dashboards and visualizations, and share data on their own.
3. Expert: This is an advanced user or data expert who uses sandbox environments within analytics tools to prototype new data sets, build data models and create reports for others that aren’t already accessible or established. Self-service analytics tools are still important for this group as it can help them spend less time building data models and reports and more time sharing insights and innovating using data-driven results.
When adopting a self-service analytics solution, it’s important that you ensure collaboration between IT and the business to assign the most appropriate analytics tool-set and data governance for each type of persona. Consumers don’t need more complex augmented tools or privileges to data-sets as other groups, for example, while experts may not automatically need to have access to content with sensitive information just because they’re in the expert bucket.
What self-service analytics solutions exist?
In the past, most BI software was designed around data scientists and technical personas. Now, modern BI solutions, such as Yellowfin, offer sophisticated analytical capabilities and tools that are specially tailored for both regular users and trained experts - the best of both worlds.
The Yellowfin platform currently provides several types of tools, leveraging sophisticated AI, machine learning and natural language technologies, that enable anyone to create reports, from the experienced data professional to everyday non-technical business user. Even people who have no prior experience with analytics can begin using our suite, helping open up data-driven analysis and decision-making to more people.
1. For business users (consumers): Guided Natural Language Query (Guided NLQ)
Why self-service analytics is a must-have
Deploying self service analytics empowers your business and/or software product customers with easy, intuitive BI tools to make better and faster business decisions. It’s a critical element of any organization today who wishes to democratize access to data and insights for everyone.
If your goal is to drive more data-driven decisions and value for your product or organization, investigating how self-service analytics can help you is key to your checklist.