IMPORTANCEOF KNOWLEDGE MANAGEMENT IN BUSINESS INTELLIGENCE AND DECISION SUPPORTNarendra Kumar Sharma1, Subhash ChandraMaurya2, Anand Vardhan Shukla3 1ResearchScholar, Amity University, Lucknow UP – India2ResearchScholar, MGCGV, Chitrakoot UP – India 3Dept.of Computer Science, Integral University, Lucknow UP – India EmailID: 1narendrasharmauim@gmail.
Knowledge has becomethe key considered quality for the twenty first century and for everyorganization that values knowledge it must invest in developing the beststrategy for identifying, developing and applying the knowledge assets it needsto succeed. Knowledge Management (KM) is a compliancethat improves the ability of organizations to solve problems better, adapt,evolve to meet changing business requirements and survive disrupting changessuch. The present paper focuses on the importance of Knowledge Management in Business Intelligence andDecision Support System. This paper will also provide an introduction to theincreasingly important area of Business Intelligence & data mining andexplain how knowledge is benefited for organization and decision support. Keywords:Knowledge Management, Data Mining, Business Intelligence, Decision Support System.I. INTRODUCTIONKnowledgeis an important asset to any organization and what an organization does withthis knowledge can be a critical component in their success. Knowledge alone can give one organization adistinct competitive advantage in the market.
Knowledge encompasses far morethan intellectual property such as processes and methods that might be unique toan organization. II. KNOWLEDGE MANAGEMENTAccording to Gartner, “Knowledge management is adiscipline that promotes an integrated approach to identifying, capturing,evaluating, retrieving, and sharing all of an enterprise’s information assets. Theseadvantages may incorporate databases, reports, strategies, systems, and alreadyun-caught skill and involvement in singular specialists.
According to Knowledge Management Tools, knowledge typicallyfalls into one of three categories:1. Explicit knowledge – Includingdocument management, intelligence gathering, and data and text mining.2.
Tacit knowledge – Includingsurveys and questionnaires, information from individual and group interviews,focus groups, network analysis, and findings from observation.3. Embedded Knowledge –Referring to knowledge that’s not immediately obvious or available on thesurface, including analysis from observations, reverse engineering, modelingtools to identify knowledge that may be stored within procedures and the like. III. KNOWLEDGE MANAGEMENT ASPECTKnowledgemanagement is acollection of systematic approaches to help information and knowledge flow toand between the right people at the right time. KM is essentially about gettingthe right knowledge to the right person at the righttime. KM may also include new knowledge creation, or it may solely focuson knowledge sharing, storage, and refinement.
Implementing knowledge management thus hasseveral dimensions including:Figure1: Dimensions of KM1. Strategy: KMstrategy must be dependent on corporate strategy. The objective is to manage,share and create relevant knowledgeassets that will help meet tactical and strategic requirements.2. OrganizationalCulture: The organizational culture influences the waypeople interact, the context within which knowledge is created, the resistancethey will have towards certain changes, and ultimately the way they share orthe way they do not share knowledge.3. OrganizationalProcesses: The right processes, environments, and systemsthat enable KM to be implemented in the organization.
4. Management& Leadership: KM requires competent andexperienced leadership at all levels. There are a wide variety of KM relatedroles that an organization may or may not need to implement.5. Technology: Thesystems, tools and technologies that fit the organization’s requirements properlydesigned and implemented.6.
Politics: Thelong-term support to implement and sustain initiatives that involve virtuallyall organizational functions, which may be costly to implement (both from theperspective of time and money).III. BUSINESS INTELLIGENCEAccordingto Gartner, Business Intelligence (BI) as a set of all technologies that gatherand 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 andeconomic data.IV. STRUCTURE OF BIBI is formed by a set of varioussoftware technologies as Olszak & Batko, 2012: Data warehouse (DW), datamarts, data mining, online analytical processing (OLAP), extraction transformload (ETL) and other reporting applications. Figure2: Structure of BI 1. DW: Itis an integrated collection of the summarized and historic data, which iscollected from internal and external data sources Radonic, 2007. It is thesignificant component of BI, and subject oriented and integrated. It supportsthe spread of data by handling the numerous enterprise records for sintegration,cleansing, aggregation and query tasks. 2.
Data Marts: Theseare small sized DWs, usually created by individual departments Khan, 2012.These are a collection of subject areas organized for decision support based onthe needs of a given department. These help business experts for the analysisof past trends and experiences Inmon, 1999.
3. Data Mining: Itis a method of finding patterns, correlation, generalizations, regularities andrules in data resources and trends by modifying through the large amount ofdata, which is stored in the warehouse. Muhammad& Ibrahim, 2014. 4. OLAP: Itis the technology that enables the user to interact, analyze, report andpresent the data in the DW.
It represents a form of a multidimensional andsummarized business data analysis, and is used for reporting, analysis,modeling and planning for optimizing the business Panian & Klepac, 2003.It refers to the way in which business users can slice and dice their way byusing complicated tools that allow for the improvement of business Ranjan,2009. 5. ETL: Itis a set of actions by which data is extracted from numerous databases, applicationsand systems, transformed as capture, and is loaded into target database. It is responsiblefor data transfer from operational or transaction systems to DW Gadu & El-Khameesy, 2014. V. RELATIONSHIP BETWEEN BI AND KMTherelationship between BI and KM perform similar activities in collecting data,organizing the data, analyzing data, aggregating data and applying data togenerate solutions to help make business decisions.
However KM includes two other activities thatBI lacks. These activities are thecreation of new knowledge and the dispersion of knowledge throughout anorganization. This is where knowledgemanagement encompasses the activities of business intelligence. The below tableshows the similar and different activities performed by KM and BI. 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! Table1: Activities between KM vs BIVI.DECISION SUPPORT SYSTEMA decision support system (DSS) is a computerizedinformation system used to support decision-making in an organization or a business.A DSS lets users sift through and analyze massive reams of data and compileinformation that can be used to solve problems and make better decisions.
Thebenefits of decision support systems include more informed decision-making,timely problem solving and improved efficiency for dealing with problems withrapidly changing variables.Figure3: BI and KM for DSSA properly designed DSS is aninteractive software-based system intended to help decision makers compileuseful information from a combination of raw data, documents, personalknowledge or business models to identify and solve problems and make decisions.Typical information that a decision support application might gather andpresent are:1.
Inventories of all of your currentinformation assets (including legacy and relational data sources, cubes, datawarehouses, and data marts),2. Comparative sales figures between oneweek and the next,3. Projected revenue figures based on newproduct sales assumptions.VII.INTEGRATION OF KM AND BI FOR DSSBIand KM provides real technological support for Strategic Management (Albescu etal., 2008).
This integration will not only facilitate the capturing and codingof knowledge but also enhances the retrieval and sharing of knowledge acrossthe organization to gain strategic advantage and also to sustain it incompetitive market (Khan and Quadri, 2012).Figure4: Technological Integration b/w BI and KMBothBI and KM is the scope of activities involved in each area. Business intelligence focuses solely oncapturing data, manipulating the data and analyzing the data. Whereas knowledge management would performbusiness intelligence activities while also pursuing the creation of newknowledge.VIII.
IMPORTANCE OF KM IN DECISIONSUPPORTThereare several reasons why KM is important to implement:1. It ensures all relevant information andresources can be access by employees when they need it2. Important knowledge is kept within thebusiness even after employees move on from the business3.
It avoids duplicated efforts4. Take advantage of existing expertise5. Standardized processes and proceduresfor knowledge management IX. CONCLUSIONEvery organization needs the proper knowledgemanagement strategy for the development of the organization. BothKM & BI are deeply influenced by the culture of the organization,especially leadership, groups and opinion leaders, as well as organizationalvalues. In the last decades thebusiness environment has changed and recently it becomes more dynamic and morecomplex. At present KM is valuable not only for individuals, and organizations,but also for global humanity. X.
REFERENCES1. Nonaka, I., ‘A dynamic theory oforganizational knowledge creation’, Organization Science, Vol. 5 No.
1, pp.14-38, 1994.2. Nonaka, I. and Takeuchi, H.
, “TheKnowledge-Creating Company”, Oxford University Press, NewYork, NY, 1995.3. Gartner, Knowledge Management, 20144. Olszak, C. M., & Ziemba, E. (2006).Business Intelligence Systems in the Holistic Infrastructure DevelopmentSupporting Decision-making in Organizations.
Interdisciplinary Journal ofInformation, Knowledge and Management, 1, 47–58.5. Radonic, G. (2007). A Review of BusinessIntelligence Approaches to Key Business Factors in Banking. Journal ofKnowledge Management Practice, 8(1), 66–71.
6. Ranjan, J. (2009). BusinessIntelligence: Concepts, Components, Techniques and Benefits. Journal ofTheoretical and Applied Information Technology, 9(1), 60–70.
7. Gadu, M., & El-Khameesy, N. (2014).
A Knowledge Management Framework Using Business Intelligence Solutions.International Journal of Computer Science Issues, 11(5), 102–107.8. Albescu, F., Pugna, I. and Paraschiv, D.(2008).
“Business Intelligence & Knowledge Management – technologicalsupport for strategic management in the knowledge based economy”, RevistaInformatica Economic?, Nr. 4(48).9.
Khan,R. A., & Quadri, S. K.
(2012). Dovetailing of Business Intelligence andKnowledge Management: An Integrative Framework. Information and KnowledgeManagement. Vol 2, No.4.10.BusinessIntelligence: Practices, Technologies, and Management, ByRajiv Sabherwal, Irma Becerra-Fernandez11.http://www.
knowledge-management-tools.net/12. http://knowledgehills.com/bikm/difference-between-business-intelligence-and-knowledge-management.htm13. Integration of Business Intelligenceand Knowledge Management – A literature review, 14.
Najibeh Abbasi Rostami, Journal of Intelligence Studies in BusinessVol 4, No 2 (2014) 30-4015. Knowledge management strategy to improve businesssector, Haradhan Kumar mohajan, Annals of Spiru HaratUniversity, Economic Series, Issue 3/2017.