// BY CINDI HOWSON VISUAL DASHBOARDS FOR SELF-SERVICE BI

Self-service business intelligence (BI) offers an enticing vision of empowering business users with direct access to data and removing IT as a bottleneck. This article highlights why visual data discovery tools have grown in popularity, the role of dashboards, and best practices for success.

WHY THE HYPE?Millions of jobs were lost in the great economic recession. Household names across multiple industries, from Lehman Brothers to Borders, went belly-up. The Wall Street darling of gaming, Zynga, struggled, while competitor King successfully issued an IPO. The pace of business has accelerated. The margin for error has shrunk. Success and failure both seem only a few clicks away. Data has become the new competitive advantage; it’s survival of the smartest. Companies that can mine their data better and faster outperform those that are stockpiling useless bits and bytes.

New technology and new business forces have given rise to a new type of BI tool: visual data discovery. These tools reduce the time to insight by using visualizations, best practices in visual perception, and easy exploration. They support business agility and self-service BI through a variety of innovations that may include in-memory processing and mashing of multiple data sources. Specialty vendors were the early innovators in this segment, but now every leading BI platform vendor offers visual data discovery capabilities.

This new category of BI tools is more visual, agile, and smarter than earlier BI solutions.

VISUAL It’s important that BI be “pretty” in order to engage new classes of users beyond technical power users. BI adoption has remained stubbornly flat for the past eight years, with an average of only 22 percent of employees using BI (see Figure 1).

Figure 1. BI adoption rates remain stubbornly low, but visual data discovery

and mobile tools have higher adoption rates. (Source: “2014 Successful BI

Survey” from BI Scorecard)

If we want to move BI out of the back room and into the board room and front lines, first impressions matter—a lot. A picture paints a more powerful insight than a dense page of numbers. As a case in point, compare the typical annual report (Figure 2, taken from Starbucks) back in 2000 versus the graphical presentation from 2012 (Figure 3).

Figure 2: Typical table of results. (Source: Starbucks annual report for 2000)

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60%

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BI ADOPTION RATES

Perc

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25% 24% 22%29%

35%42%

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Figure 3: A graphical report from 2012. (Source: Starbucks annual report for

2012)

Not only is the revenue trend chart more engaging visually, but it also shortens the time to insight by making the upward trend more apparent and the current year (in green) more obvious. According to researchers at Mindlab International, 19 percent fewer cognitive resources are used when information is presented graphically.1 Unfortunately, TDWI research shows that 65 percent of the time, users are viewing reports that are dense pages of numbers and text.2 For some reports, a tabular display may be important (parts listing, account balances), but often it’s not the most effective display and simply reproduces how things have been reported before.

The visual aspect to this new class of tools helps with visual appeal and time to insight. It can also lead to a more immersive user experience. A number of vendors are taking design concepts (and talent) from game developers to make the BI experience more gratifying. When BI is fun, the reward part of the brain lights up, and understanding and retention improve. Perhaps it’s not surprising, then, that BI adoption at companies that use visual data discovery tools is significantly higher (35 percent of employees) than the industry average of 22 percent

of employees. BI adoption is not the endgame, though; the goal is to put data in the hands of every decision maker.

AGILEVisual data discovery tools have also grown in usage because of their agility. With production-style reporting, IT developers usually custom-code a report based on specific requirements. With business query tools, IT first designs a data warehouse, then models a semantic layer or business view based on subject areas in the data warehouse. These tools continue to play a role in the full BI spectrum, especially when requirements are known and fairly static.

Visual data discovery tools, meanwhile, often allow users to bring their own data sources—whether modeled or not—into an exploration environment. Data may come from a data warehouse, and when possible, still have a semantic layer that may be optional and auto-generated as the data is loaded. In this way, if a company wants to bring in a new data source (whether big data from sensors or Web logs or small structured data from a supplier or distributor), the business user can do so without IT intervention. Adding new data sources in a traditional data warehouse and BI environment may take months; in a visual data discovery environment, it takes just minutes.

Agility also comes from the ability to manipulate the data, dynamically creating bins, defining new groupings, or displaying values as percentages. More tools are providing lightweight data cleansing and transformations that business users can perform on their own.

SMARTER With the volume of data increasing exponentially thanks to the Internet of things, simply accessing and presenting that data is unwieldy. BI needs to be smarter to help reduce the time to insight and reveal the data that matters most. What are the outliers, the anomalies, and the hidden opportunities? Visual data discovery packs more BI smarts in two main ways.

First, charting engines make recommendations and auto-adjust to pick the best chart type based on the data. When a decision

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1 Fiona Graham, BBC News, “Pretty pictures: Can images stop data overload?” April 16, 2012. http://www.bbc.com/news/business-17682294 2 Wayne Eckerson and Mark Hammond, Visual Reporting and Analysis: Seeing is Knowing, TDWI Best Practices Report, 2011. http://tdwi.org/bpreports

maker is analyzing sales by time, the engine uses a trend line; when the analysis is by region, the engine switches to a map.

The types of charts also move beyond the more common bar, line, and bubble to include such types as:

• The trellis chart (also called matrix and small multiple) to more readily compare values across multiple categories. Figure 4 shows a trellis chart of order volumes by product and country.

• The network chart to show the relationship from one point to another, such as in a social network or manufacturer-distributor chain.

• The decision tree, which shows how each segment flows into a total. Figure 5 shows a decision tree for cause of death, segmented by disease and blood pressure.

• The tree map or heat map, which compares a subtotal to whole. Tree maps use both color and square size, while a heat map uses color intensity. Figure 6 shows a tree map of STEM degrees awarded by major at the University of Texas System.

In addition to smarter visualizations, advanced analytical capabilities are beginning to be included in or accessed by visual data discovery engines. The depth and breadth of the capabilities vary significantly from product to product. At the low end of the spectrum, a tool may provide a simple forecasting algorithm. Others may include multiple forecasting algorithms, segmentation, clustering, or integrate with externally developed models, whether open source R or SAS.

SELF-SERVICE DASHBOARDSDashboards, much like production-style reports, were once designed exclusively by IT based on predefined requirements. Each visual and interactivity, whether a drill or filter, had to be programmed in advance. Second-generation dashboards brought a greater degree of reusable components and out-of-the-box interactivity but were still largely developed by IT. Today, most visual data discovery tools support user-assembled dashboards and story boards.

These visual data discovery tools are not solely for the data scientists and power users who want to explore new data sources; they also allow business users to assemble their own dashboards, bringing self-service BI to a broader class of users. Self-service BI doesn’t have to mean starting with a blank screen; it can mean navigating and exploring within a well-

Figure 4. Trellis chart example. (Source: SAS)

Figure 5. Decision tree example. (Source: SAS)

Figure 6. Sample tree map. (Source: University of Texas System,

http://exploredata.utsystem.edu)

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defined starting point. Dashboards provide this starting point by focusing all workers on the KPIs and business metrics that matter most.

The degree of out-of-the-box interactivity varies significantly across user-assembled dashboards. Features to look for include:

•Filters. Users should be able to select filter values based on the data currently displayed and other available dimensions. Can the filters apply to all components on the dashboard or only to specific visuals? Can the filters be complex selections across multiple dimensions, or only simple checklists?

•Sorting. Most tools will allow users to re-sort one column, either alphabetically or by value, but few support multi-level sorting. For example, users should be able to sort first by region, then by descending values within the region.

•Lasso. This tool allows users to visually select items (often by circumscribing them with a pointing device) on a chart and use them as filter criteria. For example, users might select multiple bubbles on a scatterplot rather than using a drop-down menu of dimension values.

•Drill down or pivot. In addition, users may want to choose whether the drill is a drill-and-filter or drill-and-expand. As content is drilled, does the display automatically adjust to appropriate chart types or windowing?

•Expand/zoom individual object. When multiple components are displayed on the dashboard, look for the ability to expand one component to a full page or screen.

•Animation. Before the release of the iPad in 2010, most animation in dashboards was delivered via Adobe Flash, a technology not supported by Apple. Much of the industry is now moving to HTML5 for animation, and some tools use AJAX. Thus, the animation technology may dictate which browser versions and tablets are supported.

Most products support these capabilities to varying degrees. However, the following interactions are still a work in progress for many:

•View detailed data. Drill down allows a user to drill to the next level of data; viewing detailed data allows a user to see how the current metric was aggregated.

•What-if analysis. With OLAP tools, what-if analysis was often accomplished by write-back to an OLAP cube. With the rise of in-memory engines, calculations on data can be performed on the fly, allowing detailed data to be recalculated almost instantly. To perform a what-if analysis, users may use sliders or input variables to recalculate metrics.

•Change display type. A dashboard should be a starting point users can customize to their own interests, requirements, or available data. Consider whether dashboard consumers—not just dashboard designers—have the ability to change a tabular display to a chart or to change the chart type.

•Collaborate and annotate. Social networking (such as Twitter, Facebook, and Pinterest) has influenced BI tools. Look for capabilities that allow users to rate content, pin components, annotate trends, and discuss content in the dashboards.

•Global search and faceted navigation. With the ease of use of Google and retail websites, BI vendors have been looking for ways to make BI equally easy. Faceted navigation allows users to navigate and explore their data by available dimensions and progressive filtering. Natural language processing lets users enter search strings and keywords such as “sales for New York” to create an initial report.

© 2014 by TDWI, a division of 1105 Media, Inc. All rights reserved. E-mail requests or feedback to info@tdwi.org.

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RECOMMENDATIONSMost companies have BI capabilities that provide production reporting and ad hoc or business query capabilities. Visual data discovery tools provide a greater degree of agility, visual analysis, and smarts than these initial modules. Here are ways to tap the competitive potential of your data:

• Embrace visual data discovery as part of your BI tool portfolio.

• Recognize the role of dashboards in providing an important starting point for aligning workers and delivering self-service BI.

• Don’t let the need for agility obviate the need for consistent, cleansed data, as well as a single version of the truth. Even with dynamically generated semantic layers and decentralized data sources, there must be governance and processes to support broader and repeated usage.

• Evolve your BI capabilities to move beyond simple monitoring and analysis to advanced analytics that can predict the future and reveal the hidden patterns in your data.

Cindi Howson is founder of BI Scorecard.

© 2014 by TDWI, a division of 1105 Media, Inc. All rights reserved. E-mail requests or feedback to info@tdwi.org.

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