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Wayne Eckerson

Welcome to Wayne's World, my blog that illuminates the latest thinking about how to deliver insights from business data and celebrates out-of-the-box thinkers and doers in the business intelligence (BI), performance management and data warehousing (DW) fields. Tune in here if you want to keep abreast of the latest trends, techniques, and technologies in this dynamic industry.

About the author >

Wayne has been a thought leader in the business intelligence field since the early 1990s. He has conducted numerous research studies and is a noted speaker, blogger, and consultant. He is the author of two widely read books: Performance Dashboards: Measuring, Monitoring, and Managing Your Business (2005, 2010) and The Secrets of Analytical Leaders: Insights from Information Insiders (2012).

Wayne is founder and principal consultant at Eckerson Group,a research and consulting company focused on business intelligence, analytics and big data.

September 2013 Archives

In my last blog, I made the case for both classifying and certifying the analytical capabilities of power users. In short, classifying power users helps business intelligence (BI) teams better understand and serve the information needs of power users and shows executives where and how to beef up their organization's analytical talent. And certifying power users motivates them to upgrade their analytical capabilities to achieve greater status, pay, and responsibility. (See "Classifying and Certifying BI Users").

As I mentioned last time, every BI team should classify their users, either using my scheme below or creating one of their own. This takes time but pays big dividends. Tailoring BI functionality and report design to individual information requirements increases the likelihood that users will adopt BI tools. Some BI vendors enforce this discipline by offering named user licenses based on functionality, but this can be restrictive since most users play multiple roles during the course of a day. It's better to tailor BI functionality in the administrative console.

More importantly, business users should be cognizant of their classification when using BI tools and reports. They should see their status (e.g. "Class I: Viewer") in the heading of each report they use and be able to view its description by hovering their mouse over the text. Ideally, users should be able to click on a button that changes their report interface from Class I to Class II or Class III and back again. BI tools that expose and hide functionality on demand drive higher levels of BI adoption. If anyone has done anything remote similar to this, let me know!!

Casual User Classifications

Casual users are business people who use information to do their jobs. They mostly consume information artifacts that others create (i.e. power users.) Figure 1 presents three levels of casual users: Class I Viewer, Class II Navigator, and Class III Explorer.

Figure 1. Casual User Classifications
Casual User Classification.jpg

Class I: Viewer. A Viewer is an executive, salesperson, or front-line worker who views information displayed on a screen and rarely interacts with it. Executives and salespeople may not have the time or inclination to interact with the display, while front-line folks don't have the time. A Viewer is often reared on spreadsheets and prefers a tabular view of data with all information on a single page. If a Viewer has a question about the data, he'll pick up the phone and call an analyst (especially old-school executives.) And he prefers receiving reports via email, although many are now entering the digital age with tablet computers and are now receptive to viewing information on these devices.

Class II: Navigator. A Navigator is typically a manager or knowledge worker who needs to monitor and manage the performance of a team and present the results to executives. Thus, a Navigator is more inclined to drill into the data to view more detail about an issue. She may also pivot dimensions or sort, rank, or add columns in a table or create custom groups (if it's a one-click function) and perform "what-if analyses" (ditto.) She may still call an analyst if she gets hung up after four or five clicks or can't find what she's looking for. A Navigator typically wants to interact with charts and view tabular data when examining detail. She prefers a browser-based interface and is increasingly using tablets as the interactivity of mobile BI displays improves.

Class III: Explorer. An Explorer is the archetypal BI user: a business user who uses a BI tool not only to view and interact with predefined reports and dashboards but explore data presented in a BI semantic layer and create simple reports and dashboards for themselves and colleagues. I've called these folks "super users" in the past: business users who gravitate to a BI tool, become proficient with it, and become the "go to" person in their department to get a custom report. In essence, Explorers are bonafide analysts. In fact, a Class III Explorer (casual user) is the same as a Class I Explorer (power user).

Power User Classifications

Like casual users, there are three classes of power users: Class I: Explorer, Class II: Analyst, and Class III: Data Scientist. (See figure 2.) It's perhaps more important to classify power users because, unlike casual users, they access data not reports and generate data that others consume. Therefore, it's critical to assess their data, analysis, and publishing capabilities. They are the eyes and ears of the BI team in the business units and must be trusted to accurately gather and display information upon which executives, managers, and others make critical decisions.

Figure 2. Power User Classifications
Power User Classification.jpg

Class I: Explorer. As mentioned above, a Class I Explorer (power user) is identical to a Class III Explorer (casual user.) An Explorer is really a super user who uses a BI tool not only to view and interact with predefined reports and dashboards but explore data presented in a BI semantic layer and create simple reports and dashboards for themselves and colleagues. An Explorer has at least basic knowledge of the business and can use a BI tool to create custom groups and hierarchies and assemble and publish dashboards from predefined objects. In other words, an Explorer knows how to use a BI tool's ad hoc query and publishing capabilities. If motivated, he can easily become a Class II Analyst and do analytical work full time.

Class II: Analyst. An Analyst explores and combines data at a deeper level than the Explorer. An Analyst queries the data warehouse directly, combining the data with local files via custom joins and data scrubbing functions. An Analyst has greater knowledge of the business than an Explorer, having spent three to five years in the industry and one to two years in a specific department learning its people, processes, data, and applications. An Analyst can perform more complex analyses and knows basic statistics and is familiar with statistical or machine learning tools. The Analyst can create dashboards from ad hoc queries and custom views and publish them to various groups.

Class III: Data Scientist. The Data Scientist is the ultimate power user whom you entrust to access data in its raw form in a staging area or source system and create accurate queries, joins, reports, and models from the data. They have deep knowledge of the business, its processes, applications, and data with three to five years of experience in both the industry and an individual business unit. The know how to integrate and transform complex data and use statistical and machine learning tools to create complex analytical models. The best ones can also program queries in a variety of languages to access non-relational data (e.g. Hadoop) and display the results using data visualization software.

Summary. Hopefully, these classifications will inspire you to create a similar set of classifications of your organization's users. Knowing your users is the first step toward delivering BI services that users want and use. And when you publicize these classifications, it may inspire business users to upgrade their data and analytical capabilities, which will reap dividends for the individuals, your BI program, and the organization as a whole.


Posted September 24, 2013 11:25 AM
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Are you getting the level of BI adoption you promised executives? Do you see an initial spike of BI activity when you deploy a new BI tool or report which then trails off? Do you fear that executives will give your BI program (or your job) the axe next fiscal year because the BI program isn't delivering enough bang for the buck?

If so, welcome aboard. You are not alone. But that doesn't make the situation any less dire. Your rate of active user adoption directly indicates the success of your BI program. Low adoption not only means you get less dollars at budget time, it also likely means that business users have abandoned your BI tools and data in favor of some "non-standard" environment. Worse yet, it may indicate that users have given up entirely and no longer seek to use data to make decisions.

Know your Audience

Although myriad problems contribute to low user adoption, the primary one for BI professionals is that they are woefully ill-informed about the business people they support. Most BI professionals have a mass-market mentality: they think all users have the same information needs and requirements. As a result, they provide everyone the same homogeneous stew of data, views, and tools. And then they're perplexed why so few business people use the BI tools and reports they provide. But the reason is obvious.

Any good novelist, painter, screenwriter or marketer knows that to connect with an audience you have to know what makes them tick. Many create a mental or visual profile of their target viewer and keep it front and center while creating their work of art or message. This helps them put the reader or viewer front and central so that whatever they create resonates more deeply and profoundly with their audience. Every artist knows that unless you connect with your audience, your beautiful creation will be ignored at best and lampooned at worst.

Like artists and marketers, BI professionals must know their audience, but even moreso. That's because BI users are a diverse lot. There are many gradations of information requirements. And most business people switch roles multiple times a day. A business person who needs a simple static dashboard to manage one part of his job may need to combine a local Excel file with raw data from the warehouse in another part. Keeping track of user roles is a full time job, but one that is critical to the success of any BI program.

Classifying Business Users

For years, I've written and spoken about two camps of BI users: casual users and power users. This is the most basic classification scheme, but it adheres to the 80/20 rule. Understanding the differences between casual and power users delivers 80% of the benefit when rolling out BI tools and reports. The basic difference is that casual users require structured access to predefined sets of data through interactive reports and dashboards tailored to individual roles, while power users explore data in a variety of systems in an ad hoc fashion.

Although most BI managers understand the differences between casual and power users, most don't act on their knowledge. Their biggest blunder is trying to gather requirements for power users. Ha! That never works because power users will simply say, "Give me all the data." Nonetheless, many BI programs continue to bang their head against that requirements wall.

But this is beside the point. Even if an organization understands and acts on the differences between casual and power users, they still may not achieve a high degree of user adoption because they've failed to comprehend the remaining nuances of the way their customers consume information. Consequently, they never achieve the final 20% of benefits from their BI initiatives that results in high-levels of adoption and user satisfaction.

Classifying Power Users

To help BI professionals create a more nuanced view of their audience, I've created a classification scheme for one of their key group: power users. (In future blogs, I'll present classification schemes for casual users, BI professionals, report writers, and ETL developers.) Traditionally, I classify power users by their business role: super user, business analyst, statistician, and data scientist. That's a fair classification but I've never elaborated on the nuances of how each type uses information.

Figure 1 defines three classes of power users by four dimensions: business knowledge, analytical skills, data integration skills, and publishing skills. This is a good start to a formal classification scheme, but it needs further refinement to be useful. Please send me your feedback and perhaps we can create an industry standard scheme that benefits everyone.

Figure 1. Power User Classifications
Power User Classification.jpg

Certifying Power Users

More important than the content of the classification scheme is how organizations use it. My hope is that BI programs will collaborate with their human resources departments to create a formal certification program based on this (or a similar) classification. In a certification program, each power user receives a rating or classification based on some formal yardstick, such a training class they've taken, test scores they've achieved, or a real-world project they've managed, or some combination of all three.

Power users receive a certificate or badge when they achieve a new level in the classification scheme. Call this the "gamification" of BI, but I think it will provide greater clarity around power user skills and requirements as well as motivate analysts and their managers to upgrade their analytical capabilities. And who doesn't want a badge to wear or display in their office showing their professional accomplishment and status with the organization?

Although establishing a certification program may seem like a lot of work, it offers numerous benefits:

  1. Customer Knowledge. BI teams can understand the types of power users in their organization, better anticipate their needs, and tailor access to their requirements.
  2. Departmental Deficits. Executives know which departments or business units lack the power users required to support various types of analytical initiatives.
  3. Self Knowledge. Business analysts understand where they stand in the spectrum of analytical capabilities and it gives them a concrete set of objectives to pursue to move up the ranks.
  4. Training. Executives will be motivated to establish and fund training programs and career paths to improve the analytical capabilities of the organization.

A formal classification scheme becomes a palpable way to accelerate adoption of your BI environment and increase your organization's analytical maturity. By delineating types of users at a granular level and formalizing their existence through a certification program, your BI staff will better serve the information needs of its audience and accelerate adoption. Moreover, a certification program will encourage analysts to upgrade their analytical skills and show executives when and how to invest in upgrading their organization's analytical capabilities.


Posted September 23, 2013 9:39 AM
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One of the biggest paradoxes in business intelligence (BI) is that self-service BI requires a lot of hand holding to succeed. That was the predominant sentiment voiced in a recent Webcast panel I conducted with Laura Madsen, a healthcare BI consultant at Lancet, Russell Lobban, director of BI and customer analytics at Build.com, and Brad Peters, CEO and co-founder of the multi-faceted BI vendor, Birst. (The Webcast will soon air on SearchBusinessAnalytics (date TBD.)

Training required. The panelists iterated the need for significant training and support to ensure the success of a self-service BI initiative. Madsen said companies should implement multi-modal training (i.e., Web, classroom, self-pace) on a continuous basis. Peters said many of his customers are effectively using social media to grease self-service BI wheels; specifically, discussion forums that enable users to share experiences and answer each other's questions. Lobban said it's critical to offer an integrated data dictionary that defines data elements used in the BI tool.

Know your audience. Another critical success factor is knowing the audience for self-service BI. Peters, for example, said there are two types of self-service: "data self service" for power users and "business self service" for casual users. Power users require ad hoc access to data using visualization tools that enable them to explore application data and local files. Casual users, on the other hand, need structured access to data via a semantic layer that adds business context. Although a semantic layer requires time and effort to build, all three panelists said it is critical to the success of any self-service BI program and helps ensure that users make accurate decisions.

Lobban emphasized the importance of starting small and iterating quickly. He said users often get discouraged when they don't find the data they need. Thus, it's critical for the BI team to add new data quickly to keep up with user requirements. The other major challenge Lobban sees with self-service BI is that business users often misinterpret the data in reports and dashboards. This is especially true for new hires or transfers from other departments. Thus, it's critical new hires and transfers get mentored by experienced BI users, either in person or virtually via help desks, training classes, or online forums.

Which tools? The panel also spent a lot of time discussing the types of tools that are best suited to self-service BI. Most thought the new generation of in-memory visualization tools are great for power users who can navigate their way through existing databases and applications, but inadequate for casual users who need more structured access to data. The panel also discussed the benefits of traditional OLAP tools versus the new visualization tools. The consensus is that OLAP tools still play an important role in BI tool portfolios because they provide robust dimensional views and calculations that new visualization tools don't support.

Finally, the panel discussed the tradeoffs between best of breed versus all-in-one BI suites. Best of breed tools provide the best functionality available or satisfy the parochial needs of individual workgroups or departments, while BI suites provide an integrated experience and architecture that addresses the entire spectrum of BI needs in an organization and is thus easier to administer. Ultimately, the panel agreed that each organization needs to decide which approach best fits their individual requirements and culture.

Summary. Self-service BI is the holy grail for BI professionals, but it has been difficult to achieve. BI practitioners and business users expect self-service BI to be easy when it's not. It requires clean, comprehensive data, integrated metadata, and continuous training and support. Ultimately, self-service BI is the true test of a BI program's overall maturity.


Posted September 23, 2013 9:35 AM
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After several years of listening to the hype about big data, top business and technology executives are now drinking the Kool-Aid, according to a new survey by NewVantage Partners, a Boston-based consultancy that advises business and technology executives how to reap value from data and analytical technology.

Almost two-thirds (60%) of business and technology executives have a big data initiative underway, with one-third (32%) of the initiatives in production and operational. Two-thirds plan to invest at least $1 million in these initiatives, while 19% are investing more than $10 million.

The results are based on responses from nearly 90 business and technology executives, 75% of whom work in financial services and 20% in healthcare and life sciences. Almost half (49%) are either C-suite executives or line-of-business heads, while the remaining 51% head enterprise data or technology programs.

The survey casts a broad net in defining big data:

Big Data is a term used to describe collections of data so large, complex, or requiring such rapid processing (sometimes called the volume/variety/velocity problem), that they become difficult or impossible to work with using standard database management or analytical solutions. For the purpose of this survey, Big Data refers to new database management and analytical approaches developed for analyzing, storing, and manipulating large or complex data. Investments in Big Data include those in human resources (e.g., data scientists), and in business and technology solutions, including database management platforms (e.g., Hadoop, EMC/Greenplum, Teradata/Aster, IBM/Netezza), analytics and visualization capabilities (e.g., Revolution R, Palintir, Tableau), or text-processing and real-time streaming solutions.

Since some of these technologies have existed for more than a decade, it's actually surprising that the adoption and investment rates aren't higher. But using technology to exploit data has never been easy even though the potential payoff is huge. Early data warehousing and business intelligence initiatives have delivered mixed results in many organizations. So, the good news is that executive decision makers seem ready to reinvest in the latest generation of "big data" technology.

Interestingly, sales and marketing departments are leading the big data charge, according to the research. They are followed closely by risk, product, research, IT, and customer departments, all of which cited by more than 62% of respondents as "driving the investment in big data."

Although functional areas are funding big data initiatives, chief information officers are the primary sponsors, according to 42% of people who responded to the survey. Other big data sponsors include: chief operations officer (10%), chief risk officer (6%), chief executive officer (6%), chief data officer (5%), chief marketing officer (5%), and chief financial officer (5%).

These sponsors recognize the importance of getting involved and guiding these initiatives. Strong leadership is required to keep the focus of big data initiatives on business goals rather than shiny new technologies. The two biggest factors cited by respondents to ensure adoption of big data initiatives are: "executive sponsorship" cited by 83% of respondents and "clear definition of business questions and business objectives" (82%).

Interestingly, more than a quarter of respondents (26%) already have a chief data officer and another 21% are considering establishing the role. This is more evidence that top executives are getting serious about treating data as a bonafide corporate asset .

For more information, go to http://newvantage.com.


Posted September 6, 2013 3:58 AM
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The key challenge in business intelligence (BI), as in life, is to reconcile opposites. In life, we need to know when to follow our head and when to heed our heart; when to resist change and when to accept it; and when to admonish our friends and family and when to comfort them. And so on. Every situation calls for a different tactic, and sometimes, we must apply both tactics at once. This is not schizophrenic; it's just common sense.

Things are not much different in the world of BI. We are often faced with seemingly contradictory needs and desires, such as: speed versus standards; flexibility versus security; business versus IT; departments versus enterprise; and ad hoc access versus highly tailored views. (See figure 1.)

Reconciling these opposites is not easy; in fact, it's quite stressful, both emotionally and intellectually. It's much easier to take a "principled" stand and choose one position over the other. With this approach, the arguments are clear and the rationale valid, but the results are always suboptimal. Fixating on one option in a continuous spectrum may work for a while and gain a lot of attention, but it eventually makes things worse than when you started.

Figure 1. The Primary Challenges Facing BI Programs
BI Challenges.jpg
Every need and constituency has an equal and opposite need. Reconciling these opposing needs is the primary challenge facing BI professionals.

Analytical Silos versus Enterprise BI. For instance, an organization may consolidate all departmental data marts and reporting systems into a central, corporate BI group to eliminate data silos and deliver inconsistent data. But if it's not careful, the corporate BI team can easily become a development bottleneck, causing frustrated department heads to hire their own BI staff under the radar and start building analytical silos. In the end, the organization pays double the amount for BI than previously and still doesn't eliminate analytical silos. In other words, embracing a rigid, single-track approach to solving a problem makes things worse. The remedy is worse than the disease.

Hedgehogs Versus Foxes

In his book "The Signal and the Noise: Why So Many Predictions Fail--But Some Don't", Nate Silver borrows an analogy to describe the way two types of pundits forecast the future.

"Hedgehogs", he says, are "type A personalities who believe in Big Ideas--in governing principles about the world that behave as though they were physical laws and undergird virtually every interaction in society. Think Karl Marx ... or Sigmund Freud...." In contrast, Silver says that "foxes" are "scrappy creatures who believe in ... taking a multitude of approaches toward a problem. They tend to be more tolerant of nuance, uncertainty, complexity, and dissenting opinion."

Research shows that "foxes" are considerably better at forecasting than hedgehogs," says Silver. In fact, hedgehogs' forecasts are barely any better than random chance. But "foxes", who see both sides of the coin and recognize how noisy the data can be, make much better predictions, he says.

The moral of the story is that the more fixed and rigid we are in solving problems, the less we succeed. The hedgehog may claim that he abides by long-held "principles"--which justifies his intransigence in the face of new information that indicate the need for a new approach or way of thinking. A fox, on the other hand, who takes a more flexible and sees both sides of an issue--as difficult and stressful as that is--often fares better in the long run.

Summary

As BI managers, we need to embrace the opposites that threaten our BI programs. Rather than make compromises between conflicting requirements and groups, we need to think outside of the box and reconcile disparate needs with a new approach that is attune with the realities on the ground. Rather than see the world in black and white, we need to recognize the value of black, white, and everything in between. The way to address challenges is to keep an open mind and remember that the world doesn't operate in absolutes, but shades of gray. Opposites do attract.


Posted September 4, 2013 12:22 PM
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