This is an article from the June 2006 issue of DM Review's Extended Edition. Click on this link for more information on DMR Extended Edition or to download this issue in a PDF format.

A recent survey of senior business executives points to some interesting facts about the state of business intelligence (BI) and knowledge management (KM) in large corporations. The survey, conducted by the Economist Intelligence Unit (a research arm of The Economist magazine), asked executives to assess the importance of various technologies in achieving their company's strategic goals over the next three years.1

The most pressing technology? Two-thirds (67 percent) cited BI/KM solutions as critical to their future success - more than any other business initiative, including customer relationship management (63 percent), supply chain automation (58 percent) and enterprise resource planning (58 percent). In the same survey, 68 percent of respondents agreed that their company's IT systems "generate large volumes of data but insufficient actionable information."

Taken separately, neither of these findings is particularly noteworthy. Much has been written about the data/information/knowledge continuum, and business leaders are appropriately focused on bridging this gap. What's of particular note, though, is the language that the survey used to describe the desired outcome: BI/KM solutions. BI and KM have long been viewed as separate and distinct - notwithstanding their juxtaposition in The Economist survey - and this divide has driven a wedge between data and knowledge.

Only recently has it become apparent that these two disciplines need to be more tightly integrated. Indeed, KM defines the process by which BI tools are used and the context in which their results are interpreted. Author Jessica Keyes describes KM as "the identification and analysis of available and required knowledge, and the subsequent planning and control of actions, to develop these into 'knowledge assets' that will enable a business to generate profits and/or increase its competitive position." She adds that "a variety of business intelligence methodologies and tools are the framework under which knowledge management operates."2

Historically, BI has focused on translating data into information through the development of data warehouses, data marts and OLAP applications - tools that make information accessible to stakeholders throughout an organization. Meanwhile, KM has evolved into a portfolio of tools and processes, organized around the concept of leveraging information assets. As a discipline, KM is more diffuse than BI, encompassing a variety of initiatives, such as knowledge repositories (e.g., centers of excellence, content management systems), knowledge networks (e.g., communities of practice, corporate Yellow Pages), collaboration solutions (e.g., team rooms, project collaboration tools) and search engines.

Industry analysts have recognized the importance of BI/KM integration for years, citing numerous synergies and dependencies. BI needs KM in order to contextualize the knowledge discovery and decision-making process. Without KM, BI leads to the situation lamented by 68 percent of survey respondents - lots of data, but what do we do with it? KM needs BI to provide raw materials for the corporate information factory and carry its outputs to market. Without BI, KM is like a library - lots of knowledge, but how do we apply it? Historically, this dependency has been viewed as either a need to integrate technologies or to change processes (i.e., the decision-making process). We would argue that it is both - and that the two must be integrated to create a win/win for both BI and KM stakeholders.

This nexus of BI and KM is an integrated approach that we call collaborative business intelligence (CBI) - a new breed of processes and products that blends BI for data management, analysis and reporting with KM for knowledge creation, organization, access and use. CBI defines the process by which companies execute on BI and KM. CBI solutions are unique in that they:

  • Leverage and integrate both structured data (e.g., reports, KPIs) and unstructured data (e.g., documents, emails) in support of business decisions;
  • Include collaborative features that are common to office-productivity applications such as annotation, discussion, scheduling and search functionality;
  • Enable interactive analysis so that knowledge workers can explore live data and evaluate alternative scenarios;
  • ntegrate the knowledge workers who use the BI tools into the decision-making process, while allowing them the freedom to use their judgment (and collaborative knowledge) as additional guides;
  • Capture the knowledge that is created and the decisions that are made as workers collaborate throughout a business process;
  • Enhance existing BI tools by embedding KM assets, including tacit knowledge, best practices and decision-making processes.

This article examines five market trends that point to increased adoption of CBI solutions in the future.
Trend #1 Maturity of Traditional Business Intelligence

Adoption of CBI solutions has been slowed in the past by immature capabilities in the foundational disciplines of BI and data management, compounded by a narrow interpretation of BI as a set of tools used to explore data. While these capabilities are still evolving, many organizations have now achieved a maturity level that can support CBI processes. Importantly, the pursuit of BI capabilities remains a primary goal of business executives. A 2005 survey of Fortune 1000 executives conducted by Accenture found that 91 percent of respondents viewed "stronger analytical and business intelligence capabilities" as an imperative for continued economic growth.3

The proper use of BI requires that an organization define the process by which BI activities are conducted and how the results are to be acted upon. This is the primary area where the integration with KM can add value. Organizations that stand to gain the most from CBI are those that: 1) have developed mature data warehouses and OLAP tools but perceive a gap in the business value of these tools; 2) are currently using collaborative technologies to enhance communication; and 3) rely heavily on knowledge workers and are committed to the continued development of their capabilities.

Trend #2 Ongoing Investment in Knowledge Management

Knowledge management has traditionally lagged behind BI in terms of maturity level, but significant strides have been made of late. IDC estimates that the market for KM software and services grew from $2.7 billion in 1999 to well over $10 billion in 2003. In a subsequent (2005) survey, IDC found that enterprises continue to increase spending on KM technologies, including collaboration software, content management and access software, and enterprise portal software.4

A driving force behind KM adoption is the need for improved corporate governance. Almost half of the firms surveyed by the Economist Intelligence Unit are turning to KM as a way to improve the visibility of internal business processes and to exert greater control over the flow and use of data. These trends are driven in large part by recent legislation - including Sarbanes-Oxley and HIPAA in the United States and the Basel II rules in the European Union - which has complicated the already significant challenges of data management.

A related and equally important trend is the increasing recognition that cultural change is needed in order to achieve KM adoption. Leaders must establish communities and instill in their members an understanding of the importance of collaboration as part of their day-to-day routine. Sponsorship, roles and responsibilities, accountability and measurement, and enabling technologies must all be in place before KM communities can generate value.

Continued demand for KM solutions is a certainty, if for no other reason than to manage the ever-increasing volume of BI. BI produces the basic information upon which business decisions are (or should be) based. Without the results from BI, KM would have no foundation for action. KM needs BI as much as BI needs KM. They cannot exist in isolation.

Trend #3 Increased Importance of Decision Management as a BI Discipline

The BI market is moving toward widespread adoption of decision management as a means to generate value from information and analytics.5 Evidence suggests that decision-oriented BI projects offer a superior return on investment, compared with their nondecision-oriented counterparts. A recent study conducted by IDC, based on case studies of 40 BI projects, found that those involving predictive analytics offered a median ROI of 145 percent, compared with 89 percent for their nonpredictive counterparts. IDC attributed the superior ROI of predictive analytics to enhanced operational processes enabled through improved decision-making.

This trend is particularly noteworthy due to the close alignment of CBI with decision management. Of all the business initiatives grouped under the BI heading, decision management stands to benefit most from collaboration. Like CBI, decision management is about leveraging organizational knowledge and deploying it in support of decision-making processes, typically as embedded logic within analytic applications (e.g., business rules engines, optimization models). These applications can dramatically improve the consistency and quality of decision-making throughout an organization - enabling decisions that are based on quantitative input (BI) and organizational best practices (KM), rather than managerial instinct.

CBI enables this alignment by exposing just enough of KM to BI and vice versa. In other words, the touchpoints of the decision process (i.e., KM) need to be exposed to the BI practitioners without diverting attention from their main task: discovering information in the data. Likewise, the BI process itself needs to be exposed to the decision process without overwhelming the decision-makers with the fine points of the analytics behind the summary results.

Trend #4 Emergence of BI Verticals and Horizontals

Increasingly, BI vendors are developing applications tailored for use in specific industries (verticals) and solution areas (horizontals). The emergence of BI verticals and horizontals creates both an opportunity and a challenge for CBI. The opportunity stems from the creation of communities that are natural environments for collaboration. The associated challenge is that these corporate communities can become insular, resulting in a free flow of ideas within vertical or horizontal stovepipes but little communication across the corporation. Indeed, according to respondents in The Economist survey, "internal barriers to cross-departmental sharing" represent the single biggest obstacle to achieving an efficient flow of use and knowledge within an organization.

This tension between opportunity and challenge, community and stovepipe is evident in the language used by Keyes to describe the integration of BI and KM in the modern economy. Keyes writes, "Business in the 21st century has become increasingly competitive as it has become global. A plethora of new technologies and business processes such as business intelligence, content management, supply chain management, customer relationship management and enterprise resource management has seen the rise of new information types and interrelationships which require the knowledge of diverse areas such as raw materials, customer requirements and manufacturing. Taken individually, this information provides value only to the process in which it exists. However, as a component of a knowledge management system, this raw data is transformed into the knowledge that will provide competitive advantage."6

These vertically and horizontally focused BI toolsets must be integrated into the appropriate business processes. The internal barriers to this can only be broken down if all the players see something in it for them. For the decision-maker, CBI offers the ability to be involved throughout the entire BI process without being immersed in it. For the BI practitioners, CBI offers the framework that allows them to do their job while still exposing and documenting all the steps in the process to other interested parties (e.g., decision-makers or BI practitioners in other parts of the organization).

Trend #5 Rapid Innovation and Convergence around Collaboration Technologies

The technology standards that have survived change in the BI and KM markets are HTTP (for user interface and delivery) and SMTP (for collaboration and notification). BI has largely adopted these standards, with enterprise reporting tools providing Web interfaces and using email to deliver reports. Meanwhile, KM systems typically handle HTTP as a primary information source and use SMTP for communication and notification.

Now that BI and KM speak the same language, the opportunity exists for CBI solutions that help them talk to one another. As BI tools generate Web-ready reports, KM tools can provide a repository for organizing these reports among other relevant information and for collaborating around the information. As BI tools generate customized analysis on topics of interest, KM tools can provide full-text search and subscription by topic (pull) as well as targeted messaging by a defined audience (push).

Microsoft's recent focus on collaboration technologies offers further evidence of market maturity. In March of 2005, Microsoft paid $120 million to acquire Groove networks, an innovative tool that uses peer-to-peer technology to let remote workers collaborate on projects through corporate firewalls. Earlier, in 2003, Microsoft paid $200 million to acquire PlaceWare, Inc., a leader in the Web conferencing space. Collaboration is also supposed to play a central role in the upcoming release of Office 2007, including integration with Groove and Microsoft SharePoint Services.

Implementing CBI Solutions

Leaders who seek to implement CBI solutions should start by evaluating the knowledge landscape within their organization, focusing on questions such as:

  • Which business processes would most benefit from the integration of BI and KM?
  • What changes would allow knowledge workers in these areas to perform their tasks more effectively?
  • Do decision-makers currently have access to all of the data and knowledge that they need?
  • Are data and knowledge being provided efficiently through established BI or KM processes?

The answers to these questions will point you toward your destination. The bullets below offer suggestions for your journey:

  • Start Small. Consider starting with a pilot solution that brings CBI capabilities to a small team with a clearly defined need. In addition to demonstrating value quickly, this approach offers lessons regarding usability of the solution, and it reveals technology challenges that must be addressed before CBI is deployed on a larger scale.
  • Avoid Disruption. To foster collaboration, CBI tools must integrate with an organization's existing BI and KM infrastructure. If users are forced to adopt
    disruptive technologies to accommodate CBI processes, the initiative will struggle.
  • Adopt a Win/Win Strategy. CBI tools must allow employees to go about their job as they always have, with only small incremental changes in their behavior required. Employees need to see the value add for their specific tasks.
  • Plan Ahead. Before starting work, build a multidisciplinary team that includes representatives from BI, KM and IT as well as the knowledge workers who must guide end-user requirements. Resource the project appropriately. Don't let ad hoc efforts create unmanageable chaos.
  • Provide Leadership. Strong sponsorship is essential, particularly in the early stages of a CBI project. Community members must be given a stake in the success of CBI.
  • Anticipate Change. Build solutions that can evolve as you discover better ways to collaborate around BI. Feedback mechanisms must be built into your CBI processes so that knowledge workers can capture, share and respond to lessons learned. Such feedback mechanisms must be embraced as desirable, not as something to be avoided.


  1. Terry Ernest-Jones. "Know How: Managing Knowledge for Competitive Advantage." Economist Intelligence Unit, June 2005.
  2. Jessica Keyes. Knowledge Management, Business Intelligence, and Content Management: the IT Practitioner's Guide. Abingdon, UK: Taylor & Francis, 2006.
  3. Accenture. "Companies Need to Improve Business Intelligence Capabilities to Drive Growth." 23 February 2005.
  4. Henry Morris. "Predictive Analytics and ROI: Lessons from IDC's Financial Impact Study." IDC: September 2003.
  5. For a more detailed discussion of this trend, see "Using Advanced Analytics to Drive Better Business Decisions" in the April 2005 issue of DM Review. (
  6. Keyes.

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