Buildingorganizational capacities based on knowledge management processesrequires relevant procedures to identify, capture, share, and reusedata creatively. Intra-organizational sharing of explicit data in theform of words, numbers, and diagrams is critical for creating newknowledge (Delak, 2014). The chaotic disorganized and repeatablestages represent the initial data mining process. However, themanaged level indicates when an organization develops based onknowledge management (KM) procedures that include monitoring,evaluation, and feedback. Eventually, optimization is the final stageof categorizing data as usable to achieve the business objective.Business intelligence (BI) is the consistent consolidation ofstandardized technology through analytics in order to deliverinformation at the right time (Brooks, El-Gayar, & Sarnikar,2015). Integrating the analytical step with geographical informationsystem and data mining realizes relevant BI. Moreover, BI supportsdecision-making based on knowledge created from organizationalinformation to streamline strategy and enterprise operations (Martin,Maladhy, & Venkatesan, 2011).
Businessanalytics (BA) seeks to analyze numbers to generate knowledge andintelligence that guarantees the use of data-driven strategicdecisions (Cao & Duan, 2015). Businesses that apply the strategicdecision-making process improve their performance since every action,resources committed, and operations follow a rational approach basedon created knowledge. Evidence suggests that application ofdata-driven conclusion improves business performance andprofitability (Provost & Fawcett, 2013). I concur with articlethat management should hire experts with the professional skills toapply business analytic tools in the daily running of organizationaloperations. Organizations with IT skilled experts have a higherchance of selecting the best hardware and software that ensures thesustainability. Management of the BI systems requires consistentdefinition of needs through information gathering, processing,analysis, dissemination, and utilization with immediate system’sfeedback (Sanger & Lahad, 2013). Hence, business analytics toolsare critical in understanding external and internal environment andfactors that affect organizations from a data-driven perspective.
Brooks,P., El-Gayar, O., & Sarnikar, S. (2015). A framework fordeveloping a domain specific business intelligence maturity model:Application to healthcare. InternationalJournal of Information Management,35, 337-354.
Cao,G., & Duan, Y. (2015). The Affordances of Business Analytics forStrategic Decision-Making and Their Impact on OrganisationalPerformance. PACIS2015 Proceedings, Paper255.
Delak,B. (2014). How to identify knowledge and evaluate knowledgemanagement in organization-case studies report. OnlineJournal of Applied Knowledge Management,2(2), 162-171.
Martin,A., Maladhy, D., Venkatesan, V. P. (2011). A framework for businessintelligence application using ontological classification.InternationalJournal of Engineering Science and Technology,3(2), 1213-1221.
Provost,F., & Fawcett, T. (2013). Data science and its relationship tobig data and data-driven decision-making. MaryAnn Liebert Inc.,1(1), 51-59.
Sanger,A. B., & Lahad, N. A. (2013). Critical factors that affect thesuccess of business intelligence systems (BIS) implementation in anorganization. InternationalJournal of Scientific & Technology,2(2), 176-180.