Six BI Industry Experts on the Past and Future of Self-Service BI

Six BI industry experts on the past and future of self-service BI

Business intelligence (BI) isn’t a new concept; in fact, it predates computers. But, as with many things, technological change has redefined the meaning of BI. Applications that were once known as Management Information Systems (MIS) now serve the needs of users outside of the C-suite.

Technological improvements (faster databases, modern browsers, web architecture and APIs) make this possible. As a result, application end users can access self-service analytics to make data-informed decisions. But do they? Can organizations accelerate a shift to self-service BI, or is it destined to remain the bailiwick of data scientists and analysts?

The Experts

We spoke with six analytics industry experts to get their thoughts and ideas on what the future holds for self-service business intelligence.

What is Self-Service Business Intelligence?

The definition of self-service BI varies from one source to another. TechTarget calls self-service BI “an approach to data analytics that enables business users to access and work with corporate data even though they do not have a background in statistical analysis, business intelligence (BI) or data mining, allowing end users to make decisions based on their own queries and analyses.” While this definition hits on a lot of major points within self-service BI, it still does not completely encompass how self-service BI is viewed in the industry.

To Kevin Smith, self-service analytics are a subset of a data product. According to Smith, data products are “productized analytics used to generate revenue or increase engagement of your customer base. It is a tab inside of SaaS application or even better, analytics that is part of the workflows built into the product.” Self-service BI allows end users to “explore different data and modify existing visuals to answer business questions.”

BI expert Wayne Eckerson sees self-service BI implemented across a spectrum of solution types. He notes it is truly “in the eye of the beholder and it is different for every individual” but in general it falls into three categories:

    • Self-service: the power users, the 10% of your organization that can start from scratch. This includes departmental analysts who want a visualization tool to connect data and blend it, as well as the data scientists who really go after the raw data using programmatic languages.
    • Silver service: the other 90%. Silver service is for the mass consumer who just wants to view and use a report or dashboard. This also includes the data consumer who wants to take a report or dashboard and edit it, open it up, modify it and maybe even add some of their own data to meet their needs.
    • Embedded service: purpose-built analytics applications, where the analytics is part of a larger application and larger process that people are doing day in and day out, and analytics supports that process.

Even with the diverse definitions of self-service BI, Rich Ghiossi notes, “the number of people that actually have products that meet the definition of self-service BI is a lot smaller than the people who are marketing it that way.” It is essential to find a vendor whose product meets the needs of all users, technical and business, and provides them the level of self-service that makes doing their job more efficient.

User Adoption of Self-Service BI

Historically, rates for user adoption of self-service BI have languished at about 30%. Many experts attribute this to the fact that it typically isn’t intuitive enough for the average end user. Kevin Smith notes that conceptually, the promise of self-service analytics is great. End users get to take this tool, use whatever data they want and mash it up with other data to answer one off questions on their own time. “While this sounds great, it’s a little more complicated than that,” Smith explains. “Unless you understand what that data means and how it is structured, you’re going to have difficulty understanding and applying it.”

Part of the problem is that most BI solutions are geared towards power users who can start from a blank slate and create reports and dashboards from the ground up. “The vast majority of business users can’t do that, don’t want to do that, and shouldn’t do that because it’s not part of their job,” says Eckerson.

For the majority of end users, integrating self-service analytics into daily workflows solves the adoption problem. Ghiossi notes that how seamlessly embedding self-service BI in a user’s workflow can improve user adoption rates and daily usage. When a solution is “easy for them to use, integrated in with their workflow and they are able to create value from that environment, self-service BI will be successful.” Self-service BI is ineffective if it is not delivered in a way that is relevant to the job your end user performs.

To Jen Stirrup, user adoption isn’t struggling, it is just a difference in the way you define self-service, especially in relation to Excel. “It’s something that people have been doing for a long time anyway, we have just happened to start calling it self-service business intelligence,” Stirrup explains. “Before, people were using Excel as the tool of choice for self-service BI. In Excel, it really has high rates of user adoption, the problem is how do you get users out of using Excel into a different tool?”

Showing the value and convenience to users is one way to get them to use a different tool. When analytics are embedded into application workflows, you reduce the time end users are wasting on context switching. If analytics are out of context for the end user, like in Excel for example, employees are doing analysis in one place and applying it in another. Nucleus Research reports that simply toggling between business applications and analytics takes up to two hours of a worker’s productivity each week. Especially in a mobile-driven world, if users can’t access data anywhere, anytime, they are going to end up wasting even more time.

Traditional “self-service BI” via Excel takes analytics out of the context of user’s daily workflow and makes it difficult and time consuming for users to explore and share. It also presents challenges as privacy laws and data security requirements become more stringent. Putting data inside of Excel or any external solution presents a potential legal exposure for the organization and additional complexity and cost to try to minimize or eliminate the risk.

While embedding self-service BI into user’s daily workflow can help improve user adoption, compliance and other data governance issues may require limiting user access to certain information. With only weeks left to follow the EU’s General Data Protection Regulation (GDPR), Ron Powell emphasized the importance of the relationship between data governance and user adoption. “Organizations are really having some difficulty in allowing end users to be able to access the data without some sort of protection and security and governance of the data. So that whole data governance aspect is very very key,” he explained. Embedded BI solutions that adopt existing security models and provide UIs to simplify administration of user roles and access rights are essential when finding the balance between data governance and the level of access needed to support self-service.

Even with additional regulatory complexity, the value produced by data driven decisions will drive increased user adoption. As John Myers notes, “We are seeing a cultural shift in the way organizations work. In legacy cultures, there is not a whole lot of encouragement or adoption. In a data-driven organization, we are starting to see that culture change where executives and employees want more accessibility to data and analytics. It’s a matter of culture and also having that data available to everyone.”

Balancing the need to drive self-service adoption with the needs of efficiently managing data governance, security and regulatory compliance puts renewed focus on picking the right BI solution and partner.

What Should You Look for Before Buying a Third Party BI Solution?

Given the time it takes to develop self-service BI functionality in an application, many organizations turn to third party BI solutions. There are several key things to consider when partnering with a vendor. First and foremost, cautions Eckerson, “You need to know what you’re getting into. It’s a Pandora’s box and if you don’t know what you are doing, it will wreak havoc on your organization.”

Before evaluating vendors, you need to understand what your users’ needs are, their various personas, what data they need to make decisions, and how a potential solution will be integrated into each user’s daily workflow.

In order to get the most out of your BI solution, it’s crucial that your data is clean and reporting-ready. “When you embed a self-service business intelligence solution, you are probably not thinking about the cleanliness of the data,” says Jen Stirrup, “so if the solution is embedded and the data and analytics are embedded, it is the closest you can get to truthful data.”

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The robustness of the offering is also an important consideration when choosing a BI solution. The solution should “allow your end users to connect any data in any place, any time but also in an easy to use manner,” advises Kevin Smith. “You want to have a way to connect to any of this data and bring it together easily, and then make this data reusable (and) the ability to merge together curated data from the organization with data from outside sources.” Whether an employee is in the field, working remotely or in the office, it should be easy for them to access the data anywhere, any time.

Two essential features to look for in a BI solution are scalability and seamless integration. John Myers talks about how if you are looking to embed a BI solution and your application takes off, you could be dealing with a scalability issue. “The only worse thing than not having it, is having it perform badly. It’s important that the solution has the ability to integrate fully into something. If you have something that uses an iframe or you have to put a lot of work into it, that’s not integrated, that’s just mashed together.” Myers elaborates, “when you can find something that allows you to make a smooth integration, whether it be from an operational platform that needs analytics in it or an enterprise that is building your applications and want to put analytics in it, making sure you have scalability and a seamless integration… are critical because they are what lead to adoption of what you are trying to do.”

Smooth integration and platform scalability can help ensure that user adoption of your application is going to increase. When that happens, the pricing model of an embedded analytics solutions is key. If the pricing is not cost effective for your application, then you could end up overpaying per user or customer for BI. With an application that scales rapidly, analytics could become too expensive to maintain. “If you are successful with self-service BI then you are going to have massive adoption. If you have massive adoption but you have a licensing model that is contrary to the adoption, then you are in a catch 22 situation — you are going to limit your adoption based on your cost model. As you negotiate your cost structure, think of it in terms of mass adoption. A cost structure that increases with your adoption, that’s not going to work,” warns Ghiossi.

How Does Self-Service BI Improve Operational Performance?

In the past, business intelligence was handled by the IT department or reserved for power users within a company. The goal of self-service BI has always been to “allow people on the front lines, people who are actually making day-to-day operational or strategic decisions, to have access themselves to the information that they need to do their job the best,” Rich Ghiossi says. By making business intelligence increasingly self-service, businesses can see improvements in different areas of their organization such as “operational improvements, thinking about how to respond to customers in a more creative and unique way and how to satisfy the customers better,” Ghiossi notes.

Jen Stirrup adds that “most organizations are using self-service business intelligence to try and find answers to business questions to improve performance. Instead of waiting for the IT department to deliver data to them, business users are taking ownership of their own business data and trying to mash it together. So we are overall seeing improvements in the ability to get the data we want, when we want it.”

By giving power to the average end user, self-service analytics eliminates the middleman, the IT department, to give end users data when they want it, how they want it. “Self-service promises to liberate data from the hands of IT and give the data back to the users so that they can have a conversation with it in an iterative manner,” says Eckerson. “To ask questions of the data at the speed of thought and then generate the insights and take action. This is the promise of self-service, and for many companies this has worked.”

When going through the IT department for analytics, end users typically experience a time lag from when they want to see the data to when they actually receive a report. Ron Powell notes, “When comparing self-service BI to traditional BI, which is getting a report, getting something from a dashboard, and it not being very timely, performance is improving because it takes less time, doesn’t involve IT, and is much easier within the applications for the end users to use the information.” Combining self-service BI with the ability to embed analytics within an application, brings BI and analytics into the future.

The Future of Self-Service BI Is Embedded

The ability to embed functionality inside of existing applications has been key to making self-service BI more pervasive and easier to use. Embedding BI in an end user’s application workflows, explains Eckerson “makes more sense to the user, it makes them more productive, it makes the analytics much more usable. As a result, they actually use the application more because it’s purpose-built for a very specific application. So in many ways embedded analytics is going to increase BI adoption significantly. End users don’t even know they are using BI anymore, they just think they are using the application and cool charts and dashboards and ad hoc capabilities are built into it.”

Rich Ghiossi agrees. “This is why embedded BI is such a big move forward. If you embed, you can make self-service BI a lot more effective because there is a context around it and it is integrated into the workflow. On top of that, it has the usability that that particular user needs, different users have different levels of sophistication.”

Ghiossi also points out that when you embed analytics into an application, you are providing true self-service BI. “If we do embedded BI right, we provide environments that are easily integrated into other environments and we make embedded BI so that it is truly integrated into an application. It’s part of that application, it’s part of what somebody does when interacting with the application in reference to determining metrics, determining changes in that workflow and changes in their environment, I think we can enable self-service BI.”

Stirrup notes how embedded BI is moving self-service BI into the future. “The first jump of self-service BI increasing productivity [is] as business users get put in it,” notes Stirrup. “The second generation is moving towards embedded business intelligence because that means they can capture all the data they need, have it next to all of their reports and increase the productivity of the organization.”

Getting Started – From Self-Service to Fully Embedded BI

The future of self-service BI may be embedded, but many organizations perceive embedding analytics into a business application as a significant undertaking. After embedding successfully in over 2,000 applications, Izenda has developed a platform that can integrate rapidly and scale efficiently.

To help organizations move even faster, Izenda can deploy in a matter of hours as a rebranded analytics portal, providing organizations with the benefits of self-service BI, shortening time to revenue and the means to develop a path to fully embed over time.

Deploying first as a portal lets your organization focus on user’s self-service needs, and integrate or fully embed on a schedule that works best for you. To learn more, request a demo with one of our analytics experts.

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