How Self-Service Analytics Improves Operational Performance, Application Value

Self-service analytics - insight on demand

Self-service analytics has been sought by users tired of waiting for IT, data scientists or other specialists to produce variations of reports and dashboards. Expectations for business intelligence and analytics have changed. Users expect real-time data to examine KPIs and performance metrics that matter to their businesses more than ever before. They won’t wait for IT to deliver a custom report or dashboard. If you give them yesterday’s data or send them to another application for analytics, they’ll just go back to relying on their gut instinct.

In the days of static reporting the minute a report was produced it was already out of date. Consumers of the information had new questions they needed answered based on the information provided and they were right back where they started. Frustration built as the time to answer was delayed by the number of hands involved and time spent waiting for the information you need to be produced. This lead to the idea of providing self-service to users so they can produce their own variations of existing reports and dashboards and interact with information provided. Users need simple, intuitive, and powerful self-service reporting to increase user adoption of your analytics solution.

Is Self-Service Analytics Different than Self-Service BI?

Business intelligence commonly gets described as a process that answers the question, what is happening to your business? Self-service analytics goes past that answer to enable you to determine why it is happening and what’s likely to happen in the future. Most often people use the terms interchangeably, particularly when discussing self-service data products.

Why Users Aren’t Adopting Self-Service

Problem – Self-Service Adoption Rates are low and BI projects fail to deliver value.

Wayne Eckerson, a principal of the Eckerson Group, calls user adoption the Holy Grail of BI. But he said adoption rates still hover between 25 and 30 percent. And at the 2017 Izenda Embedded BI User Conference, he put some of the blame on the slowness of some analytics solutions. According to Eckerson, 90 percent of end users have a short attention span and won’t use a self-service analytics solution if it’s not as fast as Google.

An organization with high levels of self-service can count on a greater number of employees actively using analytical tools. When they can get data for analysis instead of having to wait for IT staff to create new data connections and the new reports, dashboards and visualizations they need, they’ll most likely use the analytics platform in their day-to-day operations.

Adoption is low because users are frustrated by the following limitations:

Reason – Self-service analytics capabilities are often limited (change filters and view) true report and dashboard creation is not possible

Reason – Self-service analytics often still remains IT driven, with users over reliant on IT for data exploration. This over-reliance might lead to the company having reports, dashboards and data visualizations that are disconnected from what line of business users want and need. (Source: Today’s Self-Service Analytics: Your Data, My Insight, Our Progress, Aberdeen Group, March 2017) If end users just wait for someone to deliver information to them, such as creating reports, the chance for actual insight into the business disappears.

Reason – Self-service analytics is often tool driven rather than process driven.  Inside of every large organization, there are multiple BI tools, each requiring a unique approach. The tools and the data they analyze are distinct from the workflow of daily users so employees are doing analysis in one place and applying it in another. Nucleus Research reports that toggling between a business application and analytics takes up to two hours of a worker’s productivity each week. If they can’t access the data or have to wait for IT, they’ll quit asking IT for new reports and just abandon analytics. That leaves your company with everyone from the line of business users to the CEO relying on their “gut” for all the answers, and they’ll never know they are wrong until too late.

Each request for a new report or dashboard customization ties up developer time. Self-service analytics frees developers to work on your business application by empowering application users to make data driven decisions.

An organization that invests in self-service analytics finds the most success when it’s integrated into their day-to-day workflow within their business application. That helps build a culture of data-driven decision making. Users gain a sense of ownership in the data, the analytics solution and the insight they gain through their analysis. They can freely explore the data to unearth hidden insights. They start asking questions that only arose when the data exploration began. Adding self-service data preparation capabilities enables them to examine data from many angles using many data sources.

Reason – Self-service analytics does not provide all the information needed to make a decision. Each distinct system only provides a portion of the information users need to make decisions. Some data might sit in an operational data warehouse, with other data on someone’s computer in a different department. Each department in the company might have a tight hold on its own dataset, even in different databases. Without improved operational transparency through data accessibility and visibility, business users are unable to make informed decisions.

Reason – Users cannot rely on the accuracy and completeness of the organization’s data. Problems with duplicated information, incomplete or corrupted data make users leery of using their analytics solutions. Analytics platforms that pull their company’s data out of databases and into a proprietary database introduce another level of concern.

“Missing fields, multiple versions of the truth, and corrupted data can all wreak havoc on the ability to process data into usable insight. Self-service users reported significantly more accurate and complete data records,” according to Michael Lock, VP & Principal Analyst, Aberdeen Group, in his March 2017 report Self-Service Data and the Customer Connection.

In addition, the lag in updating the analytics vendor’s database increases the likelihood of decisions made with out of date information. The alternative – making business decisions based on gut instinct – isn’t something companies can afford to do and still remain competitive today.

Reason – The speed of delivery – or rather, lack of speed – of data in reports, dashboards and visualizations for analysis interrupts the workflow.

“The evolution of business analytics has left the typical line-of-business user in a situation in which they know what they want, and they’re not interested in waiting for it,” said Lock in his report Self-Service Data and the Customer Connection. “In other words, the days of a static report being delivered regularly, with the end user accepting it as gospel, and acting accordingly, are fading fast. Users want to apply their own creativity and analytical thinking in concert with their own business expertise, and that doesn’t happen in a disconnected world of IT and business.”

Reason – Users find the analytics tool difficult to use. Implementing intuitive interfaces to simplify analytics reduces the need for IT involvement. But less than half of users even in high self-service organizations think their analytics tools are easy to use. Rising frustration cuts into user adoption. Interfaces like Google and Apple created are known for their ease of use, which increased user satisfaction and drove up numbers of users. A self-service analytics platform UI needs to follow that kind of simplicity and ease of use in its design.

Embedding Self-Service Analytics Puts Data and Decisions in Context

Context is King: Business users must have access to the data they need when they need it. In our experience, software companies find that embedding self-service analytics into application workflows provides real-time data for insight at the point of decision. Izenda customers tell us that embedding BI into their applications puts self-service analytics in context for their end users, which improves decision making.

“Many people simply lack the skill set or desire to familiarize themselves with a separate standalone application for business intelligence (BI) or analytics, leaving a yearning for stronger capabilities built within their everyday software tools,” according to another report by Lock. By embedding self-service analytics, customers can get to those elusive answers faster. “74% of top embedded analytics users saw an improvement in the speed of decision-making.” (Source: Insight at your Point-of-Decision: Embedded BI Takes Center Stage, Aberdeen Group).

Data Visibility on the Rise: Business users know the queries they want to make, so it makes sense to put access to analytics into their core business application’s workflow. 83% of self-service users saw an increased visibility into business data. (Source: Aberdeen Group, Self-Service Data and the Customer Connection, March 2017).

Users of a self-service solution who don’t have to rely on IT for insight gain greater access and visibility to the data they need. They retrieve information and generate insight faster. Research shows that self-service users were able to drive improvements. With increased visibility into the company’s data and a shorter time spent in search of information, better business decisions can be made.

Empowering End Users: End users want an intuitive analytics platform to analyze, visualize and share data and insights in real time. The right solution requires giving them a true self-service analytics solution that a non-technical user can engage with to create their own reports, dashboards and data visualizations.

But that solution needs to give end users access to only the data that’s appropriate to their role at the company. An embedded analytics platform like Izenda’s enables an organization to tailor the analytics experience for the line of business users, business analysts and executives, each matched their roles and abilities. Mike Ferguson of Intelligent Business Strategies advises that businesses should avoid overloading their users with complexity if they want to increase user adoption, further empowering the analytics users.

Ferguson emphasized end users’ ability to utilize self-service to be effective in specific tasks in operational business processes. To accomplish this, organizations need to empower end users with the ability to “create their own self-service reports, dashboards and data visualizations inside of a business application without needing support from developers or data scientists.”

Creating Data Visualizations Helps Users Uncover Business Trends

Looking at row after row, column after column of data can make anyone’s vision blurry. By creating their own data visualizations from the data revealed in their self-service reports, end users can discover trends in the ever-increasing volumes of data. Giving them the ability to drill down and through the data visualizations into the underlying data enables them to determine the next question needed to discover what’s causing these trends.

With industry and business-specific KPIs, color conditional formatting, and drill down features give end users the tools to make better data-driven decisions. The right tool should write the query and provide a drag-and-drop interface using terminology they know.

97% of companies with strong ease-of-use reported an improvement in data trust. (Source: Easy Breezy BI: Simplicity Drives Success, Aberdeen Group, May 2015).

Gain Business Insight without a Data Scientist

Some organizations may hesitate to commit to a modern self-service analytics solution because they’ve heard without a data scientist they can’t understand their data. In a perfect world, every organization would have data scientists – and the budgets to afford them. But that doesn’t have to stop the pursuit of insight from your data.

Many of the fundamental questions for which businesses need answers are basic and don’t need a lot of explanation. What’s our revenue? What expenses did the business incur?  What orders did we receive and what inventory do we have? Answering these questions or those appropriate for another industry gets us started with business intelligence. By equipping business users with self-service analytics, they can explore the data to gain insight from those answers, and drill down into the data to analyze underlying data to answer the question, “Why?” Why were sales up (or down)? Do we need to order or manufacture more product to increase our inventory? What part of the country is our biggest sales or service area? Is that because the sales executive with that region is a better sales person? Or does that region have more need of our product? That’s easy to understand if we’re selling snow shovels – northern climes have a bigger call for snow removal equipment, for example.

Integrating self-service embedded BI solutions give end users the tools necessary to make informed decisions in real time. Give users direct value on the platform and they’ll be much more likely to use the analytics tools.

Izenda Helps Businesses Advance Maturity of Analytics

If we apply Eckerson’s model of self-service analytics maturity, we can quickly find where an organization stands with its existing analytics product. If internal users can interact with a standalone tool, they’re likely using a legacy BI solution. That’s the first level in Eckerson’s model.

External users get their first taste of analytics at the second level of the self-service analytics maturity model. But these come in the form of a spreadsheet or pdf, which limits their ability to drill through the data.

As stated earlier, embedding analytics directly into the application’s workflow brings us to the third level and puts the analysis in context.

Wayne Eckerson says embedding analytics takes place in the third stage of self-service analytics maturity. “So it’s no longer something that we send separately and we consume separately from the process that we are trying to optimize. It’s embedded right into the application,” Eckerson explained during the 2017 Izenda Embedded BI & Analytics User Conference. This enables analysis in context since end users can access analytics without leaving the business application.

In level four, this analytics can be further enriched by mining the data for insights, alerts and benchmarks valuable to end users. In this level, the business can mine data and then send it back to customers in the form of benchmarks, alerts and recommendations. This capability provides a big value-added service to customers and makes your product much more valuable.

The fifth stage of this progression is directly selling data or data-related capabilities and services. Methods include selling it as a syndication service, selling the analytics as a separate capability or add-on capability, or selling data analytics services.

Organizations typically embed self-service BI because they can’t keep up with reporting requests or with their competition, but generating additional revenue streams also factors in the decision. When they do embed, most companies face common pitfalls. Eckerson identifies these as typical pitfalls: cost, customization issues, integration issues, lack of functionality and lack of end user adoption.

How can a business succeed when it comes to delivering analytics? Among Eckerson’s recommendations are understanding your users and mapping analytics to their needs, as well as picking the right embedded BI product.

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