Come and Get It! Making Business Intelligence More Consumable, Part 1 The New Information Worker

Originally published October 27, 2009

Part 1: The New Information Worker

We are currently studying information “consumability”; that is, a study of how business users employ the vast amounts of information generated from their business intelligence (BI) environments. We found that BI users are faced with the usual suspects as problems, but on a scale hardly imagined just a few years ago. These include huge volumes of data (petabyte and exabyte); increasing numbers and disparity of the information stores; problems with the quality, accuracy, consistency and timeliness of the information they receive; and, finally, the overall complexity of the decision-making systems they use. The environment they face is akin to a fire hose of information being pointed at them.

Responding to these problems means understanding who the information workers are, what technologies work best for them and how to change their BI environments. This, the first in a series of three articles, describes the history of information workers and discusses their characteristics. The next article will describe how to leverage the latest BI technologies, and the final article will address how to improve information consumability.

History and Characteristics of “Information Workers”

Business intelligence technologies have long touted their “ease of use.” Indeed, ease of use has improved in terms of GUI, ability to generate SQL, graphing capabilities, etc. Use characteristics, however, are not necessarily equivalent to easy to consume characteristics. To understand the difference, it is necessary to understand the evolution of the information worker.

The first introduction of the term came from Peter Drucker in the 1960s. He defined the new “knowledge worker”1 as:

“One who works primarily with information or one who develops and uses knowledge in the workplace.”

He continued, asserting that the knowledge worker was someone who adds value in workplace by processing existing information to create new information to define and solve problems.

The knowledge worker did not produce something effective by itself2; that is, he/she does not produce a physical product. Rather this new worker produces knowledge, ideas, or information that someone else uses for input and converts into his own output. Drucker stated that the “greatest wisdom not applied to action and behavior is meaningless data,” hence the origin of the claim that BI analytics have minimal value unless they result in actionable information.

Peter Drucker continued in his book that knowledge workers are responsible for a “contribution that materially affects the capacity of the organization to perform and to obtain results.” This worker takes responsibility for his contributions and is usually better equipped to make right decision than others due to his involvement with information. The defining characteristic of knowledge work is that it is not defined by the quantity of the information used or by its costs; rather it is defined by its results.

A few years later, IBM expanded on this idea with their research into the collaborative knowledge worker3. Their researchers found that knowledge workers were among a corporation’s top talent, and that ways to improve their effectiveness were needed. These workers rely on the ability to work collaboratively, to leverage relationship capital, and to deliver new solutions from these collaborations. Obviously, understanding how they work and what their needs are is the critical step toward creating tools that enable them to perform efficiently. By improving the technologies and understanding their work practices, we BI implementers can impact the knowledge work component of many jobs.

In IBM’s research, they found that knowledge workers occasionally use some formal company-wide business processes, such as expense reimbursement and procurement systems, but most thought these process tools were inflexible and not easy to use. They also thought the tools were too complicated for use in their daily tasks. IBM found that knowledge workers go outside of “official channels” to find relevant data and create their own informal systems to place the data and analyses in proper sequence. The biggest challenge seemed to be creating a technical environment that supported these workers’ collaboration.

Still later, Microsoft did their own study of how people use information and came up with the term “information worker.” They define the information worker as follows4:
   
“When we use the term, information worker, this isn't simply another name for knowledge worker. The information worker is the superset of 3 classes of worker with different information and technology usage characteristics.

The three classes of information workers are:
  • The knowledge worker – this class of information worker works with ideas and manages teams. They want to develop and improve processes and forms by encouraging collaboration and creating workspace environments. They need to create, consume, transform and analyze data but tend to work in an unstructured, free-form way, usually starting with ideas that are collaborated upon and built into document/report/form/business process. Examples include middle/senior managers, consultants and marketing executives.

  • The structured task worker – these individuals tend to work only with data and information, not ideas. They create and consume information but don't transform or manage it. They typically must find facts quickly, create documents based on the information found, and edit, write and even process data. Microsoft believes that this category makes up 80% of the business user base in most organizations. Examples include a bank clerk, call center operator, nurse and people in supervisory roles such as a shop manager, bank manager and nursing supervisor.

  • The data entry worker – this final class of information worker creates and consumes information, but rarely transform or manage it. They want easy access to information, like standardized processes and forms, and list management. They do not tend to do free-form document creation and typically work in administrative, secretarial or receptionist roles.”
Our research with various companies and several surveys has uncovered that many information workers still have significant problems finding needed information. A 2007 Accenture survey5 of 1,000 middle managers, for example, shows that managers spend up to two hours a day looking for information and that more than 50 percent of the information they find has no value to them. Almost sixty percent of those surveyed said they miss information that might be of value to their jobs because they simply cannot find it. From this research, it is clear that poor access to information has a significant productivity impact. This problem can only get worse given the challenges described at the beginning of this article.

In researching how business users work with information today, we discovered that there are two types of information workers6, each having very distinct characteristics.

Information Consumers

Information consumers must use discovery techniques to do their job. These workers use information discovery to locate, retrieve, filter and organize the data for navigation and understanding. Information consumers are found in all levels of enterprise from senior managers to call center staff. Accessing corporate information and using IT information-handling tools is not a major aspect of their jobs. Information certainly helps them do their jobs, but is not the sole focus of their job. These workers tend to be casual users of information and are passive about gathering it; they prefer that the data gathering be done by someone else. They represent the majority of information workers and are the least satisfied with current capabilities for discovery.

Information Producers

Information producers create information to be used by the consumers. They use information creation and discovery tools plus analysis techniques to enhance, aggregate and report on the gathered information. Information analysis extends discovery by applying the producers’ knowledge and expertise to the discovered information, putting it into business context. Such enhanced information is easier and faster to use by the consumers. Information producers’ characteristics include the ability to quickly locate and explore data, a good understanding of data meaning, and the ability to perform simple to complex analyses. These users tend to be IT professional or casual but very active business users. However, information producers and IT can be a bottleneck for consumers. These information workers are represented by BI specialists, business analysts and skilled workers for whom the discovery process is easy due to detailed understanding of information.

We still see many data discovery and analysis tools assuming that the business user has a detailed working knowledge of the data involved. Perhaps the biggest challenge in business information discovery and analysis is matching the available techniques and tools to the skills of the users. Skilled information producers usually know where to find the information they need, and they understand what this information means from a business perspective. They may also have the expertise to explore the information and drill down to get more detail. Some are able to navigate and explore information by following IT-defined relationships between different information stores.

Summary

To survive in today’s competitive environment, BI implementers must recognize that every employee becomes a decision maker at various points during their day. Everyone is a form of information worker. To ensure optimal decisions, they must have the right information at the right time. Supporting both information consumers and information producers is perhaps the most difficult requirement for us to accommodate. Due to a failing here, information workers tend to develop their own strategies and techniques for getting work done in such a complex, dynamic environment – especially if their BI technologies are inflexible and cannot be changed as situations unfold during the day.

In the second article in this series, we will propose a scheme and appropriate set of technologies to support these important information worker populations.

End Notes:
  1. Peter Drucker, The Age of Discontinuity, Harper and Row, New York (1969).

  2. Peter Drucker, The Effective Executive: The Definitive Guide to Getting the Right Things Done, Harperbusiness Essentials (1967).

  3. From Ethnographic Study of Collaborative Knowledge Work. by Sandra Kogan and Michael Muller, http://www.entrepreneur.com/tradejournals/article/print/155568052.html

  4. From “What's in a Name? The Information Worker, The Knowledge Worker and The Structured Task Worker" by Mark Bower, http://blogs.msdn.com/bowerm/archive/2005/01/06/347803.aspx.

  5. “Managers Say the Majority of Information Obtained for Their Work Is Useless,” Accenture press release, January 2007.

  6. From Composite Software white paper, “Finding What You Don’t Know” by Colin White, BI Research.


  • Claudia ImhoffClaudia Imhoff
    A thought leader, visionary, and practitioner, Claudia Imhoff, Ph.D., is an internationally recognized expert on analytics, business intelligence, and the architectures to support these initiatives. Dr. Imhoff has co-authored five books on these subjects and writes articles (totaling more than 150) for technical and business magazines.

    She is also the Founder of the Boulder BI Brain Trust, a consortium of independent analysts and consultants (www.BBBT.us). You can follow them on Twitter at #BBBT

    Editor's Note:
    More articles and resources are available in Claudia's BeyeNETWORK Expert Channel. Be sure to visit today!

     

  • Colin WhiteColin White

    Colin White is the founder of BI Research and president of DataBase Associates Inc. As an analyst, educator and writer, he is well known for his in-depth knowledge of data management, information integration, and business intelligence technologies and how they can be used for building the smart and agile business. With many years of IT experience, he has consulted for dozens of companies throughout the world and is a frequent speaker at leading IT events. Colin has written numerous articles and papers on deploying new and evolving information technologies for business benefit and is a regular contributor to several leading print- and web-based industry journals. For ten years he was the conference chair of the Shared Insights Portals, Content Management, and Collaboration conference. He was also the conference director of the DB/EXPO trade show and conference.

    Editor's Note: More articles and resources are available in Colin's BeyeNETWORK Expert Channel. Be sure to visit today!

Recent articles by Claudia Imhoff, Colin White

 

Comments

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Posted October 30, 2009 by kenoconnor00@gmail.com

Hi Claudia,

You suggested I follow up on my twitter RT with a comment as to why I think this is a great article - well here goes.

Those of us in the data quality profession need to know and understand our customers - the 'information consumers'.  This article helps us do just that.  I look forward to the rest of the series.    

I believe it is critical that BI and Data Quality professionals co-operate to create a 'bigger pie' for us all.  The GIGO principle tells us that the best BI solution in the world will prove useless if the underlying data is garbage.  Likewise, top quality underlying data is effectively useless without a quality BI solution to make it readily accessible to the "information consumers" that need it.

Before seeking to sell a "World class BI solution" into a new client, one should consider the risk that the overall project will fail due to poor quality data.  To manage this risk, I recommend assessing the status of common Data Governance issues within the target client.  I have provided a process on my blog you can use to do just that - See 

Process for assessing status of common Enterprise-Wide Data Governance Issues

Feedback and questions welcome - Ken

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Posted October 29, 2009 by Eric Rogge

Hi Clauda and Colin,

There's an old joke that goes like this: "Most people use statistics the way a drunk uses a lamp post, more for support than enlightenment."and then there's the Kevin Costner truism "Build it and they will come". Part of the issue with  information is finding the right statistics to tell the story desired. :-) Perhaps info systems should be designed from the report back... A desired outcome is envisoned, then the data is surveyed to find the appropriate support.

And then there's the common problem indicated by this joke: A woman who happens upon a man on his hands and knees under a lamppost outside a tunnel. What is he doing? Looking for his keys. Is that where he lost them? No, he lost them inside the tunnel. Why isn’t he looking there? It’s dark in there; it’s better light under the lamppost. How many times do metric models get designed to find last years problems?

And: Statistics means never having to say you're certain. The risky part isn't analyzing the data, it's taking action. Sometimes that means coming up with new, untried actions to take.

And finally: A statistician drowned while crossing a stream that was, on average, 6 inches deep. Often the metrics lie. So, one has to be careful to not take the data at face value.

my $.02.

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Posted October 28, 2009 by Fernando Labastida

This article is right up our alley - we're glad to see this issue addressed because at the end of the day who are the people who are implementing the business processes that can make or break a company? The front-line employees, the knowledge workers. This is especially true for situations where time-critical decisions can make or break the day, such as in a fast-paced retail situation. Our CEO wrote a post about this very issue, addressing "Who is BI really for?".

Great article, can't wait for Part 2!

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