The trend of globalization has induced fierce competition among business enterprises within domestic and international markets. The primary quest for the technologies is not limited to the strategic value of an organization but also empowers the organization's work context by utilizing its resources. The knowledge management process deals with the extraction of both implicit and explicit knowledge of the organization for improving the performance of the organization. However, Business Intelligence, on the other hand, gained its importance with constant enhancement in technologies and tools for extracting hidden knowledge and patterns.
Hence it can be argued that both Business Intelligence and Knowledge Management are complementary to each other for extracting and managing the knowledge. Thus government organizations must have an integration of both Knowledge Management and Business Intelligence processes for enhancing the performance of the organization concerning make organization decisions for the competitive environment and utilizing the tacit organizational knowledge. The paper focuses on how BI and KM integration affect the government business organization while discussing its implementation challenges. Current situation of understanding management strategic decision making and the role of knowledge must need to address before proposing any framework for a government organization.
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The study will distinguish between personal and organizational knowledge, as well as whether education is playing a pivotal role in strategic development or not?
Keywords
Knowledge Management (KM), Business Intelligence (BI), Data Mining, Knowledge, Data Warehouse
1. Introduction
In the era of knowledge and technical innovation, it has been accepted that intangible assets of any business organization will be key to its success. Experience is supposed to be the most critical asset of any business organization, which has the most significant influence on competitiveness, strategic development, and growth. Further knowledge can be made accessible to all through the knowledge management process. In the context of business organization, knowledge management is use to acquiring knowledge and experiences it for strategic development.
It has claimed that in an organization, the knowledge not only embedded to document and repositories but also with enterprise routine, process, and practices. Thus, education is recognizing itself as one of the most critical assets of any organization. Knowledge is acquired through the processing of available data of organizations using data mining approaches. Data mining has the potential to use as a powerful tool for business intelligence but yet not fully recognized.
With the proliferation of new technologies, data mining has experienced exponential growth and became an integral part of the Knowledge Management system. Data mining algorithms are applied to explore the underlying data of the business organization, and after processing, it determines the effectiveness of knowledge. The paper aims to find how government organization managers adopt both KM and BI processes in the public sector. The study aims to find out the interrelationship between Knowledge Management and Business Intelligence, and utilize it for strategic development and decision making.
In the government-based organization, there is an extensive amount of data that is used within the organization for business policy management, organization decision making, and growth & development of the organization.
With the increased amount of data, the correlation between data also changes; it means the relationship between the application system also changed this extracted knowledge can be utilized for decision-making business intelligence. Primarily in the Knowledge management process, the knowledge discovery process needs to apply data mining algorithms. Varieties of algorithms are available in data mining, such as genetic algorithm, decision making, neural network, and fuzzy logic. The fundamental purpose of the paper is to discuss the need to integrate KM and BI for exploiting structured & unstructured raw data, implicit information of the organization, and its challenges.
2. Literature review
Most researchers and practitioners agreed on the practical implication of knowledge as one of the critical assets of any organization. Knowledge Management and Business Intelligence are the two major areas of the researcher's concern. Knowledge management is a tool for empowering the knowledge within the organization, and useful for decision making. However, Business Intelligence has affected the business world the most for transforming raw data into an experience. It can be used for prediction analysis. Dearth research has been performed to explore Knowledge Management, Business Intelligence, and its applicability within various application domains.
There is a need for a common platform for the organization where both employer and employee can share the knowledge.
A scheme for transforming Knowledge Management into Business Intelligence has specific parameters for implementing them to the organization for a standard workflow. However, the new or new solution cannot be added directly to the adoption purpose. Tacit knowledge plays a vital role in all the phases of any new innovative process and implementation of implicit Knowledge Management and can help handle new problems.
Memory is a model for linking individual knowledge to management. However, the management of tacit knowledge is a challenging task. Thus, there is a need for a common framework where tacit knowledge can be categorized into various degrees.
Both knowledge management and business bits of intelligence are different from each other in terms of standard foundation. Thus the interrelation between knowledge management and business intelligence needs to be explored. Simply an insight can be concluded that business intelligence is used for transforming data to knowledge. In contrast, Knowledge Management can be used as a tool for knowledge acquisition, knowledge sharing, and to create new awareness.
3. Knowledge management
3.1 Knowledge
Knowledge is defined as the mixed frame of facts, expectations, skills, and a combination of relevant information collected through experience, study, and reasoning, for enhancing the ability of decision making and evaluating the right context. However, data, information, and knowledge are the key terms which are the set member of knowledge management and may be used interchangeably. Several arguments were made by the researchers about these terms, and defined as:
Data can refer to unprocessed, unstructured collections of random facts; Information refers to structured and processed data having some sense to the user, whereas knowledge applies to the most refined and highly useful data for decision making and problem-solving.
Various researchers have proposed several classification methods for classifying knowledge. The classification of experience is helpful to the organizations for processing and managing their various available knowledge resources. The most widely accepted classification of knowledge is Explicit and Tacit knowledge.
Explicit knowledge contains the knowledge, which has already been processed in the form of visual, text, diagrams, tables, manuals, and specific documents. Acquisition of explicit knowledge is easy since it is in the form of a table, manuals, and document; so as easy to manage too.
3.2 Knowledge Management
Knowledge management is an essential part of any process management system and highly applicable to business organizations. It is an integral part of linking knowledge to business process management. Several authors have proposed various definitions of knowledge management. Knowledge management can be defined as the paradigm which is used for exploring knowledge resource, exploiting, and sharing all knowledge resource for enhancing the performance of any organization. Knowledge management provides a framework and tools for knowledge acquisition, sharing, and creating knowledge for aiding end users for problem-solving, and decision making.
The knowledge management system can be defined as the system for managing knowledge within the organization for creating, acquiring, and sharing of knowledge.
Figure 2: Overview of KMS
Researchers and practitioners have discussed challenges and barriers that may cause performance degradation using a knowledge management system.
3.3 Knowledge Management Models
Limited processes, procedures, and structured approach lead the development of a more structured approach for managing knowledge resources within an organization. Knowledge management modeling is used for creating and managing knowledge to overcome these challenges. Models provide an accessible presentation of a real system using its main features. Modeling helps give structured methods for understanding, implement, and evaluating knowledge management systems. However, researchers argued over the advantage and disadvantages of these models while applying the organization.
Generic Knowledge Management Models
Many generic models, methods have been developed by the researchers for enhancing knowledge management. Some of the well-known models are the SECI Model, knowledge map, Ontology-based knowledge management, Activity-based knowledge management, and knowledge management models.
SECI Model
SECI model is used for creating knowledge using four different modes known as Socialization, Internalization, Combination, and Externalization, as represented in figure 3.
The creation of new knowledge is a specific activity where each user in an organization acts as a knowledge worker, and it begins with each individual.
Externalization is used for transforming tacit knowledge into explicit knowledge. A combination process is used for combing various available explicit knowledge sources for creating new awareness.
Activity-based Knowledge Management Model
The activity-based model is proposed, especially for activities of construction projects. The information and knowledge from all sources are classified and stored as an activity unit, hence named activity-based modeling. The primary aim of this model is reusing expertise and easy knowledge acquisition. Activity-based knowledge divides the process into a top-level and sub-level phase. High-level phases are such as knowledge acquisition, knowledge extraction, knowledge storage, knowledge sharing, and knowledge update.
Knowledge Map
Using an activity-based knowledge management model as the base model. This model is used for acquiring and representing the knowledge known as a knowledge map. A knowledge map is the schematic graphical representation that tells what knowledge resource is available and missing in knowledge management. It's an easy way for the user to find the required knowledge. This model uses a previous knowledge map of a similar activity to form a new one. Authors have proposed knowledge management architecture for describing components of knowledge management. This architecture consists of four different layers as an Interface layer, Access layer, Application layer, and database layer
Tacit-Explicit Knowledge Continuum
A model has been proposed for transforming tacit knowledge into explicit knowledge in the organization; there should be a clear understanding of the dynamic nature of education [15]. The experience can also be harmful to organizations if it is invalid, misleading, discouraging, and unsatisfactory for the organization. Learning is dynamic as it changes with time in a place to extract knowledge from users; it should be more productive, like creating a knowledge culture for sharing knowledge through face to face communication.
However, various other knowledge management models such as IMPaKT, e-COGNOS are proposed by authors depending on different organizations. Reviewing the literature and analysis helps in identifying the characteristics, their relationships, and components for knowledge management modeling related to government-based organizations.
4. Business intelligence
Business Intelligence can be defined as the combination of data analytical tools for gathering and effective use of organization information to improve business management. Some authors have argued business intelligence as an online decision-making process. Authors have suggested that Business Intelligence as a set of components such as data warehouse, data mining, OLAP, and decision support system depicts the input and processing of the Business Intelligence process. The basic description of Business Intelligence components is described in the next subsection.
Data Warehouse
The data warehouse is a system used for data repository and data analysis. Data warehouse stores the data into a repository, where it is processed and organized for strategic decisions. Information in the warehouse is stored in the form of Metadata. Metadata helps the user to understand what and where data is available, and how to access it and when to use data. Data warehouse system is categorized such as data mart, OLAP (Online analytical processing), and OLTP (Online transaction processing).
Data Mart
Data marts are the simple form of a data warehouse which specially focused on a single task such as for any individual departments. Datamart uses the data either from internal operations or from the external data source.
OLAP (Online Analytical Processing)
OLAP is a multidimensional model that supports roll-up and drilling operations. OLAP is a low volume transaction but consists of complex queries. Data mining techniques widely accept OLAP applications.
Data Mining
Data mining is the process of mining the available data to discover the hidden patterns, trends, and correlation among the data. A massive amount of data is stored in a data warehouse and processed using data mining tools and techniques.
ETL (Extraction, Transfer, and Load)
The ETL process is the group of three different methods for extracting and loading into the database. The extraction process refers to data extraction from various sources, including internal and external sources. The transfer process refers to data cleaning for correlating the inconsistent, missing, and invalid data sets. Finally, load refers to load the cleaned data into the data warehouse.
5. Integration of business intelligence and knowledge management
Business Intelligence and Knowledge Management both have shown significant improvement for organizational performance. However, both Business Intelligence and Knowledge Management are different but used for knowledge discovery and decision making. There are various arguments by the researcher for discussing whether Business Intelligence is part of Knowledge Management or Knowledge Management is part of Business Intelligence. Simply Knowledge management deals with both implicit and explicit knowledge, whereas Business Intelligence deals with only explicit knowledge. Integration of Knowledge management and Business Intelligence will broaden the research area of expertise while improving intelligence. The business Intelligence process converts data into information then into knowledge, which finally used for meeting user requirements. However, the primary emphasis on Knowledge management is knowledge and improves the utilization process.
The significant benefits of the integrated framework are
- It ensures to provide the highest quality of services to individual users in the global market.
- Tacit knowledge can be useful for business intelligence.
- It gives both understandings of business context, context interpretation for user benefits.
6. Basic framework for KM & BI integration in government organizations
Management of knowledge is of much importance for the government for dealing with the challenges of the knowledge economy. Government organizations are facing many problems, such as administrative, executive, and fierce competitiveness for achieving organizational goals. Today government organizations need knowledge work and knowledge workers for creating & sharing the knowledge to enhance interpersonal and organizational skills. Knowledge management and business intelligence have the potential to strengthen the effectiveness and competitiveness of government sectors.
Thus there is a need for having a combined integrated framework of Business Intelligence and Knowledge management for achieving this goal. The initial scope represents the possible outcomes and features which are expected from the BI, KM integrated framework. Based on the expected results of an integrated framework, it can consist of several layers.
After processing the unstructured and structured data, knowledge can be extracted as KDD or BI processes. Finally, obtained knowledge can be visualized and integrated with a decision support system. On analyzing the expected outcomes from the framework, it can be argued that there can be possible interaction between Knowledge Management processes and Business Intelligence Processes. The primary aspect of the integrated framework is the inclusion of both explicit and implicit knowledge, which benefits organizational decision goals as well as the work skills of an employee using tacit knowledge.
Extractions of tacit knowledge and its utilization is a challenging task for any organization and have many positive influences.
Conclusion & future work
Potentially this research would assist in the development of an integrated model for Business Intelligence and Knowledge Management, which helps government-based organizations for strategic growth. It will help evaluate the existing knowledge management system. This expanded integration will improve the effectiveness of knowledge at an individual and organizational level.
Implementation of integrated KM and BI framework for government organizations can improve the quality and efficiency of public services. In this paper, a base framework with its possible expected outcomes and detailed literature survey has been proposed. Further research aims to develop a new knowledge management framework integration with business intelligence that enables strategic development, decision making, and resource utilization within a government organization.
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