Features
One size does not fit all
Tailoring different BI tools and approaches to the appropriate users produces better results.
by Wayne Eckerson
In the ongoing search to better manage risk, predict customer behavior and determine corporate strategy, businesses are increasingly looking for quick, easy access to analytic intelligence. To that end, many are focusing on self-service analytics and business intelligence (BI) to enable casual users as well as power users to rapidly gain critical insights when making key decisions.
However, analyzing data is not easy:
- Analytical literacy is woefully lacking in many companies, and business analysts are scarce.
- Preparing and packaging data so businesspeople can access and trust it is difficult, time-consuming and expensive.
- De facto analytical tools—spreadsheets, desktop databases and reporting tools—are rudimentary at best and haven’t changed much in decades.
Fortunately, emerging tools and technologies can improve business analyst productivity and preserve information consistency throughout an organization. For a company with an enterprise data warehouse (EDW) as its foundation, opportunities abound to empower all users with analytic capabilities by providing the appropriate BI tools and technologies to specific groups of users.
Optimal fit
On a basic level, all BI involves analysis. Businesspeople use reports and dashboards to understand the operation, analyze root causes and guide future activities. The question isn’t whether reports and dashboards are analysis tools, it’s whether these tools are best suited to the types of people performing the analysis.
For executives and managers, reports and dashboards are optimal analytical tools. But for others, such as data-savvy analysts, technologies with richer functionality are required. A common mistake that organizations make when purchasing BI tools, though, is to straddle the fence—that is, they buy tools that are too complex for casual users but not sophisticated enough for power users.
One key to a successful BI deployment is to understand the business users—the roles they play, the information they need, and the manner in which they consume and analyze that information.
While organizations can choose from a plethora of analytical tools and technologies, this cornucopia makes it difficult to know which ones to provide to which users. The problem is further complicated by the fact that most employees play multiple roles in the organization, and each has different information requirements and styles of consuming data.
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These patterns can vary by industry, geography, corporate culture and personal preferences, so no surefire scheme exists for outfitting users with the appropriate tools. Ultimately, BI needs to be customized to people’s roles and personalized to their individual tastes.
The best way to start this process is to inventory users by classifying employees according to some logical scheme, such as business titles, departments or data-usage patterns. Categories should be based on how business users consume information.
Casual versus power
Information consumers—or casual users—leverage the output of information producers and represent about 80% of all employees. They generally include customers, suppliers, executives, managers and employees. Information producers—or power users—create reports, dashboards and models for themselves and casual users. While power users represent about 20% of employees, they have an oversized impact on the BI environment because they create the content, design the BI roadmap and influence the selection of tools.
Power users fall into four subcategories:
- Business analysts. Data- and process-savvy business users who identify trends, solve problems and devise plans
- Super users. Technically savvy departmental business users who create ad hoc reports for their colleagues
- Analytical modelers. Business analysts who establish statistical and data-mining models that quantify relationships and can be used to predict behavior or conditions
- IT report developers. IT developers, analysts or administrators who build complex reports and train and support super users
The two main categories—casual and power users—each exhibit unique characteristics when it comes to consuming or producing information. A good classification technique includes describing these characteristics and assigning a fictional name and photo to each group to crystallize requirements and features. The user classification then maps them to the types of BI output they consume or produce. (See figure.)
Mind the gap
During the creation of a user classification, tension exists between the tools and approaches currently used to analyze information and the ones that could or should be used. Many BI deployments get shipwrecked on the shoals of over-fitting users. Although executives could benefit from performing multi-dimensional analysis, most won’t. The same holds true with other types of users.
BI teams must construct a bridge from the current analytical environment to a more optimal environment. A small group of users will cross immediately and ask why it took so long. The majority will initially resist change, but, as good corporate citizens, they will slowly cross the bridge. A small minority will fiercely resist any and all changes.
Reality check
During an expansion of analytics capabilities, a common mantra among BI managers is “self-service BI.” In theory, casual users will be empowered to create their own reports and views rather than rely on others, and they will get exactly what they want much faster. Unfortunately, the reality is often quite different. That’s because many fail to realize there are two types of self-service BI: ad hoc report creation, which is geared to power users, and ad hoc report navigation, which is geared to casual users.
While power users represent about 20% of employees, they have an oversized impact on the BI environment because they create the content, design the BI roadmap and influence the selection of tools.
Report creation involves making queries against databases, manipulating the results and formatting the output. This is what power users do every day. With report navigation, however, users traverse established pathways through a pre-defined data set, starting with a dozen high-level metrics or key performance indicators and drilling down through more detailed views of the metrics. Both types can be used to analyze information, but report creation requires considerably more technical savvy.
Report navigation tools won’t satisfy power users’ need to explore the data without restriction. Conversely, giving casual users report creation tools will only increase the backlog and heighten dissatisfaction. Given that almost 80% of employees are casual users, it’s imperative to apply self-service appropriately.
In the end, driving toward a self-service environment specifically tailored to both casual and power users requires best-in-class analytic tools integrated with a powerful analytic EDW. This foundation delivers a single view of the business and the flexibility to meet diverse corporate and technology requirements throughout the organization.
Editor’s note: This article is based on a TDWI Best Practices report titled “Beyond Reporting: Delivering Insights with Next Generation Analytics” by Wayne Eckerson.
Wayne Eckerson is the director of TDWI Research at The Data Warehousing Institute. He is an industry analyst, consultant and educator who has been working in the data warehouse and BI community since 1995.
Photo illustration by Randall Nelson