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Come and Get It! Making Business Intelligence More Consumable, Part 2 What Information Workers Need

Originally published November 17, 2009

Part 2: What Information Workers Need

In Part 1 of this series, we reviewed the evolution and roles of information workers and discussed two main types of information worker – the information consumer and the information producer. This time we take a look at the information requirements for these two types of information workers and outline approaches that can help satisfy those requirements while at the same time making information more consumable.

Information Consumer Requirements

Information consumers are task-oriented business users that support the day-to-day business operations of an organization. Many of these users do not have the time, experience and/or inclination to analyze information for decision making. They simply want information that either tells them what to do, recommends what actions can be taken, or provides a guided workflow on how to make a decision in any given situation. As someone put it in a recent interview with a healthcare management company, “We’d like to eliminate reports for our information consumers, and just send them recommended actions instead.”

Information consumers are underserved by today’s decision support environment, which is overly complex and not easy for information consumers to use to discover the information needed for making decisions and taking effective actions. For information consumers, business analytics should be as easy to consume as applications are on the Apple iPhone. To solve this problem, business analytics need to be put into a business context and made actionable through the use of alerts, recommendations and guided decision-making workflows.

Information Producer Requirements

Information producers explore and analyze information, then produce and use these business analytics for improving business operations through their tactical and strategic decisions. They also personalize these analytics and make them actionable for use by information consumers.

Even though information producers may constitute less than 10% of information workers, they are nevertheless responsible (through IT services or direct publishing) for the majority of the analytics used in an organization. Information producers represent an organization’s business innovators. It is these business users that enable companies to be agile and creative as well as compete effectively in today’s tough business climate.

Some information producers are frustrated by the decision support environment provided by IT, and often create their own applications instead. The issue here is that IT groups do not know how to encourage or support information providers to become more self-sufficient or add value to the decision support environment. Instead, information producers have to live within a rigid IT environment that often stifles creativity. To solve this problem, information providers need a collaborative decision support environment that promotes self-sufficiency and enables users to employ their experience and expertise to add value to business analytics.

It is important to point out that information producers are also information consumers depending on the role and activities within that role that they are performing at any given moment. At a minimum, they consume the analytics created for them by the IT group. Some motivated information consumers may also become information producers by adding feedback and best practices, and improving the information they are supplied. Current decision-support environments must change to provide sufficient flexibility to allow innovative business users to use their expertise to become information providers.

Making Analytics More Consumable

Analytics need to be put into a business context and made actionable by information producers if they are to be used by information consumers. Creating the business context involves adding a business taxonomy and glossary to the information architecture and relating business analytics to business goals and plans (using performance management, for example). A collaborative decision support environment allows information providers to add business metadata such as a folksonomy (through social tagging) and role-based information so that analytics can be tailored to specific job roles and activities.

As already discussed, analytics can be made actionable through the use of alerts and recommendations (intelligent agents) and guided decision-making workflows. Adding lineage metadata (date sources used, currency of the source data and analytics, data quality metrics and transformations performed) helps in ensuring that decisions are made on accurate and up-to-date information.

A collaborative decision-making environment would allow information providers to add qualitative metrics (annotations, opinions and ratings) and best practices to the actionable analytics.

Adding Value to Analytics

Vendors and analysts have been talking about collaborative business intelligence for a while. Often this simply means providing collaborative tools that permit business users to deliver and share analytics through email and interactive interfaces such as instant messaging, office software or a collaborative portal. The use of social computing technologies could also be used to support capabilities such as social tagging and the ability to annotate and rate analytics.

However, a true collaborative decision-making environment involves more than simply adding basic collaborative and social computing capabilities to the analytical environment. Such an environment should track and collect business knowledge about the information paths followed by information workers to create analytics and make decisions. It should also enable information workers to locate experts, document expertise and publish collections of information to communities with similar interests. This type of support can only be achieved by tightly integrating business intelligence (BI) into the collaborative environment, rather than by trying to add collaboration features to BI tools.

A Collaborative Decision-Support Environment

Figure 1 shows how information flows through a collaborative decision-making environment from initial information discovery through to analysis and the final delivery of actionable analytics to information consumers. One of the key features that collaboration adds to this environment is the ability to synthesize information by combining business analytics with other types of decision-making information and tailoring the results based on business users’ experience, expertise and opinions. The figure also shows key technologies that can be used to build such an environment. Many of these technologies have been discussed in detail by Colin White in a six-part series of articles on the BeyeNETWORK entitled “Using Enterprise 2.0 for Business Intelligence” (Part 1,  Part 2, Part 3, Part 4, Part 5, Part 6).

 
 

Figure 1:
Technologies for Collaborative Decision Support

Next month we will look at this information flow in more detail and show how it supports the needs of both information consumers and information providers. We will also describe a multi-step maturity model that outlines how organizations can evolve from a basic business intelligence system to a full collaborative decision-making environment that supports the information flow depicted in the figure.

  • 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 December 29, 2009 by Kenneth Hansen kenneth.hansen@indicium-europe.com

A good thought provoking article but I consider it is a very immature enterprise if 10% of information workers are information producers. Enterprises with datawarehouses often have automated data feeds requiring few DW operational staff and teams of 3 to 6 power data users supporting hundreds of information workers. If the percentage is more than 3% then I suggest that either the information is not being shared as it ought to be or the DW & BI infrastructure is sub-optimal.

I certainly agree that DW & BI systems have to allow business users to enhance the data and share their results with others in the enterprise 

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