Web 2.0 and Business Intelligence – How Do They Fit Together? Part 3

Originally published 5 March 2008

Declaration of Independence
Within this article I will occasionally refer to products and use screenshot examples of various products as an aid to communicating specific points. These examples are chosen at random and in no way represents any endorsement on behalf of the author to any product in the marketplace.
End of Declaration
 
In my first article of this series, I highlighted several Web 2.0 technologies. These include:

  • RSS and ATOM feeds

  • Web services

  • Javascript and AJAX

  • PHP/Pearl/Python scripting

  • JSON

  • Programming Frameworks (e.g., Adobe Flex, DOJO, Ruby on Rails, OpenLaszlo and many others)

  • Folksonomies

  • Mashups

  • Blogs

  • Wikis

So far in the series, we have covered everything on this list as far down as Programming Frameworks. In this article, I intend to discuss folksonomies, blogs and wikis as Web 2.0 technologies to see how they apply to business intelligence and performance management. My next and final article in this series will address the issue of mashups.

Folksonomies

While many of you may have heard of taxonomies to organise information, some of you may not have been exposed to folksonomies. So what is a folksonomy? This rapidly growing concept was pioneered on the public Internet by sites like JotSpot, Flickr, del.icio.us and Technorati (see Figure 1). Nowadays, you see it also on popular sites like Facebook.

Figure 1

The idea here is that the user is free to tag information using whatever tag names they like. On the public Internet, people are tagging web pages, photographs, etc. Using these tags, end users can organise their own content and find that content again by searching for it using the tag names they specified.

Note the tag cloud on the right of the Figure 1 screenshot. Notice that some of the tags here are in larger text than others. The tags in larger text are the most popular, while those in smaller text are less popular. This tag cloud is constantly changing with new tags becoming popular while others fade away.

What we are seeing now is this same mechanism emerging as a means of organising content inside the enterprise. While this capability has not yet materialised in business intelligence (BI) products per se, it certainly is already shipping in latest generations of some collaborative tools, some content management systems and in enterprise portals. As an example, BEA has built tagging into the AquaLogic Interaction Portal. Similarly, IBM has done the same in its new Lotus Connections product with a feature called IBM “Dogear” and Connections integrates with WebSphere Portal Server. Figure 2 shows an example of the tagging process.


Figure 2

Notice the statistics alongside the tags. As the user starts to tag the content, statistics appear against other tags that match the pattern of what the user has already entered at the keyboard as he or she tags. The idea here is that as the user tags the content, statistics may influence them to follow the majority. In this way, the most popular tags materialise and float to the top. It’s like democratically elected tags. This method of organizing content is most effective when the information is changing rapidly. Certain kinds of content such as operational documents (e.g., invoices, insurance quotes, purchase orders, etc.) are more likely to be tagged with a master data identifier (e.g., a customer ID) than a random user tag. This is important and necessary as this kind of content does not change and is often required to be kept permanently as records for many years to comply with legislation and regulation. So, folksonomies are useful within the enterprise, but not necessarily for all content.

How should we expect to see folksonomies apply to business intelligence and performance management? I think the answer is that it will become possible to tag reports and dashboards and even tag individual metrics. In the context of Figure 2, it means that the most popular BI tags will emerge. In the context of Figure 1 this also means that BI tag clouds could appear with the most popular BI report and metrics tags being in large text. By following a BI or performance management tag, you would be able to see the intelligence that others use in the enterprise and find other people to collaborate with over decisions that need to be made at many different levels throughout the enterprise. You could also find expertise in this way by looking at people’s profiles (see Figure 3).

Figure 3

Some vendors are already supporting ranking of BI reports to allow this content to be judged by the user base in terms of usefulness. However, Figure 3 takes this to a new level – socially networked BI and performance management. This is the future in my opinion. This will happen, and it is where we are headed.

Of course, business intelligence can also be categorised into a taxonomy. A taxonomy supports the formal categorisation of content and is particularly suited to slowly changing information and records management. This already happens in several BI portals such as SAP (Business Objects) InfoView and IBM (Cognos Connection). Here, the taxonomy is for categorising BI reports and queries to make them easy to find. My belief is that this will continue but will be accompanied by the introduction of folksonomy tagging and socially networked performance management. If this happens, then clearly folksonomy statistics will be used to “tune” taxonomies so as to keep them in line with users’ preferred category tag names (labels). Ultimately, this could become a closed loop whereby observed usage and tagging of business intelligence will be used to continually optimise formal BI taxonomies. This is shown in Figure 4.


Figure 4


Blogs

Blogs, of course, are already everywhere. I could cite my own blog on the BeyeNETWORK as an example. Blogs are a personal publishing mechanism; and so in the context of business intelligence, it is clear that people can share their interpretations of BI reports, dashboard visualisations and also republish BI content for others to see. This clearly helps with sharing expertise and making people aware of business performance across the enterprise. The only question here will be whether or not people are prepared to share bad news as well as good. Nevertheless, this all helps in leveraging knowledge within the enterprise. Search engines can also crawl blogs and therefore make these opinions and content available to a wider audience. It should be noted that for sensitive BI reports, it may not be appropriate to use this mechanism as clearly publishing such reports in a blog may breech company policy and regulations.

Wikis

Wikis are a group publishing mechanism whereby multiple users can publish content and share it with others. A key difference between wikis and blogs, however, is that wikis allow users to edit content that has been published by others. That means that if BI reports and opinions are published in a wiki, then others can build on that and so help construct a formidable resource of opinion, knowledge and expertise on business performance as it applies to specific areas of business operation or as it applies to more strategic performance management either at the line-of-business level or at an enterprise level.

In my next and final article as part of this mini-series, I will examine mashups and see how these apply to business intelligence and performance management.

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