A Comparative Analysis of Data Integration and Business Intelligence Tools with an Emphasis on Healthcare Data
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Joseph George, Dr. M.K Jeyakumar
|DOI : 10.14445/22315381/IJETT-V68I9P202|
MLA Style: Joseph George, Dr. M.K Jeyakumar "A Comparative Analysis of Data Integration and Business Intelligence Tools with an Emphasis on Healthcare Data" International Journal of Engineering Trends and Technology 68.9(2020):5-9.
APA Style:Joseph George, Dr. M.K Jeyakumar. A Comparative Analysis of Data Integration and Business Intelligence Tools with an Emphasis on Healthcare Data International Journal of Engineering Trends and Technology, 68(9),5-9.
The heart and soul of any Business Intelligence suite is its ETL (Extraction Transformation and Loading) capability. Business Intelligence (BI) helps the organizations to take informed decisions with the help of properly formulated, known and unknown facts. Integrating data from multiple sources and transform it into a holistic data view for analysis is the core feature of any BI platform. There exists many tools and technologies to facilitate ETL and Data Warehouse activities. These tools vary a lot in terms of maturity and usability. ETL is expensive in terms of computing resources and time. A poorly designed ETL steps can take the entire BI solution into a toss.
In current scenario, the term ETL is getting diminished and the term Data Integration Tool (DIT) is getting more popular and widespread. In this paper we will treat both terms synonymously. In an ETL or rather Data integration effort, business processes and domain knowledge are vital. This research paper is doing an analysis of the popular Data Integration tools with an emphasis on healthcare Data Warehouse domain. Healthcare industry is unique thus the data associated with it is much more unique than any other sector. This study is the primary step towards the end to end development of a healthcare business intelligence or Clinical Business Intelligence model.
 J. Desjardins, “World Economic Forum. How much data is generated each day?,” 2019. Accessed: May 09, 2020. [Online]. Available: https://www.weforum.org/agenda/2019/04/howmuch- data-is-generated-each-day-cf4bddf29f/.
 Bernard Marr, “Here’s Why Data Is Not The New Oil,” www.forbes.com, 2018. https://www.forbes.com/sites/bernardmarr/2018/0 3/05/heres-why-data-is-not-the-newoil/# 3d4745013aa9 (accessed May 11, 2019).
 C. Ballard and D. M. Farrell, "Dimensional Modeling : In a Business Dimensional modeling for easier data performance", 1st ed., vol. 1, no. 1. Menlo Park, CA.: IBM Redbooks, 2006.
 M. R, Kimball;R, "The Data Warehouse Tool Kit - Dimensional Modelling", 3rd ed. 2013.
 B. Wieder and M. L. Ossimitz, “The Impact of Business Intelligence on the Quality of Decision Making - A Mediation Model,” in Procedia Computer Science, 2015, vol. 64, doi: 10.1016/j.procs.2015.08.599.
 W. Eckerson and C. White, “Evaluating ETL and Data Integration Platforms,” The data warehouse institure Journal(TDWI Research), vol. 1, pp. 1– 38, 2013, [Online]. Available: http://download.101com.com/tdwi/research_repor t/2003ETLReport.pdf.
 P. Ponniah, "Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals", vol. 6. 605 Third Avenue, New York: John Wiley & Sons, 2001.
 M. R, Kimball;R, "The Kimball Group Reader", vol. 1, no. 1. Crosspoint Boulevard Indianapolis, IN: John Wiley & Sons, 2016.
 Gartner, “Magic Quadrant for Data Integration Tools,Gartner Reprint,” Stamford, 2020. Accessed: May 01, 2020. [Online]. Available: https://www.gartner.com/doc/reprints?id=1- 1OA35PNQ.
 N. Biswas, S. Chattapadhyay, G. Mahapatra, S. Chatterjee, and K. C. Mondal, “A new approach for conceptual extraction-transformation-loading process modeling,” International Journal of Ambient Computing and Intelligence, vol. 10, no. 1, pp. 30–45, 2019, doi: 10.4018/IJACI.2019010102.
 J. P. A. Runtuwene, I. R. H. T. Tangkawarow, C. T. M. Manoppo, and R. J. Salaki, “A Comparative Analysis of Extract, Transformation and Loading (ETL) Process,” IOP Conference Series: Materials Science and Engineering, vol. 306, no. 1, 2018, doi: 10.1088/1757-899X/306/1/012066.
 R. P. Deb Nath, K. Hose, T. B. Pedersen, and O. Romero, “SETL: A programmable semantic extract-transform-load framework for semantic data warehouses,” Information Systems, vol. 68, pp. 17–43, 2017, doi: 10.1016/j.is.2017.01.005.
 KLAS Research, “2020 Best In KLAS Healthcare Business Intelligence & Analytics,” 2020. Accessed: May 01, 2020. [Online]. Available: https://klasresearch.com/best-in-klasranking/ healthcare-business-intelligence-andanalytics/ 2020/97.
 MicroStrategy, “Architecture for Enterprise Business Intelligence,” p. 443, 2012, [Online]. Available: https://www.microstrategy.com/Strategy/media/d ownloads/products/Analytics/MicroStrategy- Architecture-for-Enterprise-BI.pdf.
 P. Russom, “Next Generation Data Integration,” TDWI Research, p. 35, 2011, doi: 10.1145/1216993.1216994.
 T. C. Ong et al., “Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading,” BMC Medical Informatics and Decision Making, vol. 17, no. 1, p. 134, Dec. 2017, doi: 10.1186/s12911-017- 0532-3.
 HHS.gov, “HIPAA for Professionals | HHS.gov,” www.hhs.org, 2017. https://www.hhs.gov/hipaa/forprofessionals/ index.html (accessed May 16, 2020).
 B. Bergeron, "Developing a Data Warehouse for the Healthcare Enterprise: Lessons from the Trenches", 3rd ed. Healthcare Information and Management Systems Society (HIMSS)., 2018.
 HIMSS, “Adoption Model for Analytics Maturity | HIMSS Analytics,” HIMSS Analytics, 2020. https://www.himssanalytics.org/amam (accessed May 15, 2020).
 HIMSS, “HIMSS Analytics,” AMAM, 2020. https://www.himssanalytics.org/work-withcertified- organizations/amam (accessed May 15, 2020).
 Microsoft, “Data Factory - Data Integration Service,” Azure, 2020. https://azure.microsoft.com/en-us/services/datafactory/ (accessed May 16, 2020).
Data Integration Tools, ETL, Business Intelligence, Data Warehouse, Clinical Business Intelligence, Data Visualization, Data Mart