Difference between business intelligence and knowledge management

The major difference between business intelligence and knowledge management is the scope of activities involved in each area.  Business intelligence focuses solely on capturing data, manipulating the data and analyzing the data.  Whereas knowledge management would perform business intelligence activities while also pursuing the creation of new knowledge.

Table 1 Activities of knowledge management and business intelligence

Knowledge Management Business Intelligence
1. Capture data 1. Capture data
2. Organize data 2. Organize data
3. Analyze data 3. Analyze data
4. Aggregate data 4. Aggregate data
5. Apply data 5. Apply data
6. Create new knowledge 6. No equivalent action!!!!!!
7. Knowledge dispersion 7. No equivalent action!!!!!!

Conclusion

The differences between business intelligence and knowledge management are subtle, they are not readily apparent because both areas of study contain similar processes.  Both business intelligence and knowledge management perform similar activities in collecting data, organizing the data, analyzing data, aggregating data, and applying data to generate solutions to help make business decisions.  However knowledge management includes two other activities that business intelligence lacks.  These activities are the creation of new knowledge and the dispersion of knowledge throughout an organization.  This is where knowledge management encompasses the activities of business intelligence.

The future of these areas is still uncertain; however there are several companies emerging to provide services for both business intelligence and knowledge management. Business intelligence firms, such as The Center for Business Intelligence, Microstrategy, and SAP; sell their services as decision support for executive decision makers.  These businesses sell and implement software that captures data, manipulates it into useful information and applies the information to answer specific questions, show trends, create reports or forecast future events.

Industry offers little in the way of knowledge management services.  Perhaps this is because knowledge management encompasses many activities that are classified as business intelligence.  Bergeron states that “many database companies and reengineering consultants became KM companies overnight by simply modifying copy in their sales brochures.” Despite this, there are a few dedicated knowledge management consulting firms, such as Sveiby Knowledge Associates, that are willing to provide advice, tools (including business intelligence tools) and a certification program to further knowledge management.

Whatever industry offers now and in the future, it is apparent that both business intelligence, and as an extension knowledge management are both areas that will continue to grow.  As long as businesses need to make important decisions that are based upon information that can be captured with information technologies these two areas of study will remain a popular topic in the future.

References

[1]. www.b-eye-network.com

[2]. www.information-management.com

[3]. searchcrm.techtarget.com

[4]. www.scip.org/Publications

Analytics in Knowledge Management

Analytical CRM (business) analytical CRM – Software which helps a business build customer relationships and analyse ways to improve them. Applications provide all the necessary functions companies require for measuring, forecasting and optimizing their customer relationships. With the flood of information that companies collect, store and have available today, the challenge is getting the tight information to the right people at the right time to maximize the efficiency of customer interactions. By using analytics to boost the efficiency of knowledge management, companies have a powerful new tool that uses customer information to extend and deepen their relationships with customers
When applied to knowledge management, analytics helps companies better understand customer needs and preferences to identify recurring patterns.
Of the analytic capabilities in use today, customer valuation is a critical component. When tied to knowledge management, customer valuation can help companies concentrate their limited resources on their best and most valuable customers. Taking into account customer lifetime value, which is a forward-looking view at customer profitability by segment, customer valuation identifies the true value of a customer and ensures proper allocation of resources per customer segment. Customer profitability (CP) is the difference between the revenues earned from and the costs associated with the customer relationship in a specified period.
Knowledge management interpreted through analytics can dramatically improve CRM. By more precisely interpreting and sharing information, companies are better able to identify and segment specific customer groups. With the opportunity to increase sales and profitability through cross-selling opportunities, analytics can also improve customer retention and help prioritize the most profitable customers. By tying analytics closely to knowledge management, companies can equip the right people with the right information to meet individual customer needs, resulting in optimized resource allocation and ultimately, improved customer satisfaction.

Social Business Intelligence

Social BI implies some of the following features

Social BI interactivity: We’ll see growing incorporation of Wikipedia, Facebook, Twitter, and kindred models of user-centric development, publishing, and subscription into the heart of the interactive BI user experience. Accelerating the trend toward pervasive BI, we’ll see more solutions that enable reports, dashboards, charts, and other BI views to be embedded in social media. You can regard today’s collaborative BI mashup offerings, as pointing the way toward this style of self-service team-based development, as do BI solutions from Lyzasoft, Tableau, JackBe, and other social-focused vendors.

Social BI content marts: We can expect to see more BI solutions that support extension and/or replacement of traditional data marts with vast user-populated pools of complex, mashed-up, subject-oriented analytic content and applications. It’s not inconceivable that what we call “social marts” will incorporate and build on content repositories that many enterprises have built on platforms from today’s enterprise Content Management (ECM) vendors.

Social BI information integration: Users will be able to choose from a growing range of BI solutions that support discovery, capture, monitoring, mining, classification, and predictive analysis on growing streams of social media content, much of it coming in real-time from both public and private sources. Essentially, this is where advanced analytics features such as social media analytics, social media monitoring, and social network analysis,  will converge into the growing social BI stack.

Obviously, social BI is far from a mature marketplace. The industry is groping for a common approach toward which to evolve. BI vendors are still trying to get their collective heads around the vision of social BI. Just as important, vendors are, in their various ways, striving to differentiate through innovative new features that are aligned with the sorts of capabilities many of us enjoy through our personal dabblings in Twitter, Facebook, and the like.

Business intelligence as a component of knowledge management?

If we approach this question from the perspective that the end product of Business Intelligence is Opportunity Analysis and that Opportunity Analysis, once classified, becomes an organisation’s Intellectual Capital, then the concept of defining Business Intelligence as Knowledge would appear to be a logical inference. This deduction is reinforced by the interpretation of Intellectual Capital. Thomas Stewart defines Intellectual Capital as:

“Intellectual capital is the sum of everything everybody in a company knows that gives it a competitive edge.”

If we apply Stewart’s rationale of Intellectual Capital to Knowledge Management and Business Intelligence we cannot fail to recognise the common goal – competitive advantage. Therefore it is difficult to argue against the notion that Knowledge Management and Business Intelligence are one and the same activities. Yet there are those who would argue that there is a fundamental difference between the two. One argument finds its roots in the principle of knowledge sharing. As a former intelligence officer, the concept of Knowledge Management and the principle of sharing knowledge is in direct contradiction with the rule of applying the ‘need to know basis’ that intelligence managers have traditionally applied to the sharing of intelligence. This is a factor that some competitor intelligence specialists may refer to when defending the need to uphold Competitor Intelligence as a profession apart from that of Knowledge Management. But this argument is surely not sufficient grounds for making the case. What then are the factors that differentiate KM from BI while recognising that both activities strive for identical goals?

Business intelligence (BI) has always had a “pipeline” orientation—in other words, a primary focus on the one-way flow of data, information, and insights from “sources” (e.g, your customer relationship management systems, enterprise data warehouses, and subject-area data marts) to “consumers” (e.g., you). But we all know that this pipeline orientation—also known as “simplex” information transfer—doesn’t describe the predominant flow of mission-critical intelligence in our lives. Quite often, the most important insights are those that issue from other people’s heads, not from our companies’ data marts. Many real-world intelligence flows are full-duplex, many-to-many, and person-to-person in orientation. This fundamental truth will continue to drive the spread of “social” architectures in core BI and advanced analytics.

Issues between KM and BI

One of the key issues around Knowledge Management and Business Intelligence is the point at which information is recognised for its knowledge worth. By this we mean at what stage in the process of converting information to intelligence and knowledge does the analyst or manager of that knowledge perceive its value to the organisation? While we may not be able to actually pinpoint the exact stage in the conversion process, what is evident however, is the need for the analyst or knowledge manager to command an in-depth understanding of the strategic objectives or direction of the organisation. This principle of Knowledge Management and Business Intelligence reinforces the need to understand the relationship between producers and consumers of intelligence and knowledge. It emphasises the significance of the producer-consumer relationship and the necessity for an appropriate business mechanism to manage and sustain the linkage and communication between them.

Key Success of the both

 The key to the success of both intelligence and knowledge within the business environment and in terms of deriving value therefrom is dependent upon the following criteria:

  1. The recognition by the content owner of the relevant association between the knowledge or the intelligence information and the strategic objectives of the organisation
  2. The degree of understanding where that knowledge or intelligence information can be applied towards achieving an advantage.

To date one of Knowledge Management’s biggest challenges is to identify a process or methodology whereby the owners of information are able to apply associations of value to that information and convert information to knowledge and ultimately intellectual capital. One approach is to introduce a process within the organisation’s Project Management discipline whereby project managers, using a template, capture key learnings at the end of all completed projects. A sort of After Action Review (AAR) process. This knowledge then contributes towards the capture and use of ‘best practices’ and replication – a process that can help organisations shorten learning curves and reduce costs through leveraging previously invested resources, time and money. Without the above criteria, the whole exercise becomes nothing more than organising information. The Holy Grail of Knowledge Management therefore is to apply knowledge towards gaining a competitive advantage. In todays highly competitive environment it is the time to market that an organisation takes between innovation and product or service maturity that will enable it to gain an advantage.

Knowledge management as an element of business intelligence

Some researchers see knowledge management as an element of business intelligence. They argue that KM is internal-facing BI, sharing the intelligence amongst employees about how to effectively perform the variety of functions required to make the organization go. Hence, knowledge is managed using many BI techniques. Others contend that a ”true” enterprise-wide knowledge management solution cannot exist without a BI-based metadata repository. They believe that a metadata repository is the backbone of a KM solution. That is, the BI metadata repository implements a technical solution that gathers, retains, analyzes and disseminates corporate ”knowledge” to generate a competitive advantage in the market. This intellectual capital (data, information and knowledge) is seen as both technical and business-related.

Other researchers note that many people forget that the concepts of knowledge management and business intelligence are both rooted in pre-software business management theories and practices. They claim that technology has served to cloud the definitions. Defining the role of technology in knowledge management and business intelligence – rather than defining technology as knowledge management and business intelligence – is seen as a way to clarify their distinction.

The attraction of business intelligence is that it offers organizations quick and powerful tools to store, retrieve, model and analyze large amounts of information about their operations and, in some cases, information from external sources. Vendors of these applications have helped other companies and organizations increase the value of the information that resides in their databases. Using the analysis functions of business intelligence, firms can look at many aspects of their business operation and identify factors that are affecting its performance.

However, the Achilles’ heel of business intelligence software is its inability to integrate non-quantitative data into its data warehouses or relational databases, its modeling and analysis applications, and its reporting functions. To examine and analyze an entire business and all of its processes, one cannot rely solely on numeric data. Indeed, estimates from various sources suggest that up to 80% of business information is not quantitative or structured in a way that can be captured in a relational database. There is too much verbal or documented information that is unstructured or semi-structured information and, hence, not well suited to the highly structured data requirements of a database application.

BI systems are becoming increasingly more critical to the daily operation of organizations. Data warehousing can be used to empower knowledge workers with information that allows them to make decisions based on a solid foundation of fact. However, oftentimes only a fraction of the needed information exists on computers; the vast majority of a firm’s intellectual assets exist as knowledge in the minds of its employees. Researchers now argue that what is needed is a new generation of knowledge-enabled systems that provide the infrastructure needed to capture, cleanse, store, organize, leverage and disseminate not only data and information, but also information and knowledge that is less easy to codify. That is, systems should be designed that provide a unified communications platform for sharing tacit and explicit knowledge derived from BI and KM systems. In these systems, data, documents, stories, videos, knowledge experts and decision models can be identified, mapped and targeted to address new situations.

BI activities should provide knowledge improvement. This means that the effectiveness of business intelligence should measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s), and how well it improves decision making and, hence, firm performance.  Business intelligence should therefore be viewed as an integral part of KM. This in no way diminishes the importance of BI activities. Rather, it simply places business intelligence into a larger organizational context – BI is one of the many knowledge-based activities creating intellectual capital that can be exploited by a firm.

The-Smart-BI-Framework

The relationship between KM and BI

Many confuse knowledge management (KM) with business intelligence (BI). According to a survey by OTR consultancy, 60% of consultants did not understand the difference between the two. Gartner clarifies this by explaining business intelligence as a set of all technologies that gather and analyze data to improve decision making. In business intelligence, intelligence is often defined as the discovery and explanation of hidden, inherent, and decision-relevant contexts in large amounts of business and economic data.

Knowledge management is described as a systematic process of finding, selecting, organizing, distilling and presenting information in a way that improves an employee’s comprehension in a specific area of interest. Knowledge management helps an organization to gain insight and understanding from its own experience. Specific knowledge management activities help focus the organization on acquiring, storing and utilizing knowledge for such things as problem solving, dynamic learning, strategic planning and decision making.

Conceptually, it is easy to comprehend how knowledge can be thought of as an integral component of business intelligence and, hence, decision making. I argue that knowledge management and business intelligence, while differing, need to be considered together as necessarily integrated and mutually critical components in the management of intellectual capital.

Knowledge management has been defined with reference to collaboration, Content Management, organizational behavioral science and technologies. KM technologies incorporate those employed to create, store, retrieve, distribute and analyze structured and unstructured information.  Most often, however, knowledge management technologies are thought of in terms of their ability to help process and organize textual information and data so as to enhance search capabilities and to garner meaning and assess relevance so as to help answer questions, realize new opportunities and solve current problems.

In most larger firms, there is a vast aggregation of documents and data, including business documents, forms, databases, spreadsheets, email, news and press articles, technical journals and reports, contracts, and web documents.  Knowledge and Content Management applications and technologies are used to search, organize and extract value from these information sources and are the focus of significant research and development activities.

Business intelligence has focused on the similar purpose, but from a different vantage point. Business intelligence concerns itself with decision making using data warehousing and online analytical processing (OLAP) techniques. Data warehousing collects relevant data into a repository, where it is organized and validated so it can serve decision-making objectives. The various stores of the business data are extracted, transformed and loaded from the transactional systems into the data warehouse. An important part of this process is data cleansing where variations in data schemas and data values from disparate transactional systems are resolved. In the data warehouse, a multidimensional model can then be created which supports flexible drill down and roll-up analyses. Tools from various vendors provide end users with query capabilities and a front end to the data warehouse.

KM as an intermix of Business Intelligence

BI and Knowledge Management

Success in business is not only defined by who possesses the most business intelligence, but by also who can best manage it. In this environment, business intelligence knowledge management is an essential skill for remaining competitive.

As knowledge management tools proliferate, it’s important to understand the function business intelligence must play in an organization. Business intelligence knowledge management must enable organizations to be more productive by minimizing the time users spend searching for intelligence. It must access the greatest possible volume of intelligence, but deliver only the most relevant and highly targeted information, filtered for the interests and needs of individual users. It must create synergies with existing information architecture within the organization to provide greater return on previous investments and create a unified business intelligence knowledge management system that integrates information into the user’s daily workflow. Of course, any business intelligence product must hold down costs, or the original goal of remaining competitive will be defeated.

The-Knowledge-Cycle

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