Business Analytics in the Era of Artificial Intelligence
Keywords:
Business Analytics, Artificial Intelligence, Business IntelligenceAbstract
According to the increasing demand of businesses in driving their organizations with data,
it is highly essential for them to have capable analytics tools which can generate more actionable
insights. Business analytics today is not only performed by data scientists or analysts but also
performed by business users who possess deeper understanding of the data content but are lack of
data analytics skills. Consequently, artificial intelligence-enabled analytics tools that are easy to
use may have crucial impacts on generating more useful actionable insights and making business
users more productive. This article main purpose is to create an understanding of major functions
of artificial intelligence-enabled analytics tools in supporting business users’ decision making and
data analysts’ working with models.
References
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Sedrakyan, G., Mannens, E., & Verbert, K. (2019). Guiding the Choice of Learning Dashboards Visualizations: Linking Dashboard Design and Data Visualization Concepts. Journal of Visual Languages and Computing, 50(2019), 19-38. doi: 10.1016/j.jvlc.2018.11.002
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BrightPoint Consulting Inc. (2019). Dashboard Design: Key Performance Indicators and Metrics. Retrieved August 7, 2019, from BrightPoint Inc. Website:
https://www.brightpointinc.com/key-performance-indicators/
Carlsson, K., & Gualtieri, M. (2019). The Forrester New Wave: Automation-Focused Machine Learning Solutions. The Forrester Report, 2019(2), 1-19.
Capgemini. (2015). Big & Fast Data: The Democratization of Information. Retrieved July 4, 2019,
from Capgemini Website: https://www.capgemini.com/wp-content/uploads/2017/07/big_fast_data-_the_democratization_of_information.pdf
DataCamp. (2019). Democratizing Data Science in Your Organization. Retrieved August 9, 2019, from Training Industry Website: https://trainingindustry.com/content/uploads/2019/04/Democratizing-Data-Science-4.18.19.pdf
Davenport, T. H. (2018). From Analytics to Artificial Intelligence. Journal of Business Analytics, 1(2), 73-80. doi: 10.1080/2573234X.2018.1543535
Delen D., & Ram S. (2018). Research Challenges and Opportunities in Business Analytics. Journal of Business Analytics, 1(1), 2-12. doi: 10.1080/2573234X.2018.1507324
Eckerson, W. (2019). Three Technologies Reshaping the Face of Business Intelligence. Retrieved July 15, 2019, from Oracle Website: https://www.oracle.com/a/ocom/docs/corporate/analystrelations/eckerson-group-reshaping-business-intelligence.pdf
Edge, D., Larson, J., & White, C. (2018). Bringing AI to BI: Enabling Visual Analytics of Unstructured Data in a Modern Business Intelligence Platform. Retrieved July 10, 2019, from Microsoft Website: https://www.microsoft.com/en-us/research/uploads/prod/2018/04/BringingAItoBI.pdf
Ereth, J., & Eckerson, W. (2018). AI: The New BI: How Algorithms Are Transforming Business Intelligence and Analytics. Retrieved August 1, 2019, from IBM Website: https://www.ibm.com/downloads/cas/M7VMLOPY
Evelson, B., & Carlsson, K. (2018). AI Unblocks the Business Intelligence in BI. Retrieved August 2, 2019, from Squirro Website:
https://squirro.com/wp-content/uploads/2017/10/AI_Unlocks_The_Business_I.pdf
Gartner Inc. (2016). Citizen Data Science Augments Data Discovery and Simplifies Data Science. Retrieved July 5, 2019, from Gartner Website: https://www.gartner.com/en/documents/3534848
Gartner Inc. (2017). Augmented Analytics is the Future of Data and Analytics. Retrieved July 20, 2019, from Gartner Website: https://www.gartner.com/en/conferences/emea/data-analytics-germany/why-attend/gartner-insights/research-augmented-analytics
Gartner Inc. (2019). Magic Quadrant for Analytics and Business Intelligence Platforms. Retrieved July 22, 2019, from Microsoft Website: https://info.microsoft.com/ww-landing-gartner-mq-bi-analytics-2019.html?ls=Website
Ghosh, P. (2018). Business Intelligence and Analytics Trends. Retrieved August 1, 2019, from Dataversity Website: https://www.dataversity.net/business-intelligence-analytics-trends-2019/
Google Cloud. (2019). Cloud AutoML. Retrieved August 5, 2019, from Google Cloud Website: https://cloud.google.com/automl/
Halper, F. (2017). Advanced Analytics: Moving Toward AI, Machine Learning, and Natural Language Processing. TDWI Best Practices Report, 2017(3), 1-40.
Hasan, W. (2019). Oracle Analytics Cloud Investment Plan. Retrieved June 19, 2019, from Oracle Website: https://www.oracle.com/webfolder/s/assets/ebook/analytics-cloud-investment-plan/pdf/analytics-plan.pdf
Hossin, M., & Sulaiman, M. N. (2015). A Review of Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process (IJDKP), 5(2), 1-11.
Kale, V. (2017). Big Data Computing: A Guide for Business and Technology Managers. Boca Raton: CRC Press.
Kotu, V., & Deshpande, B. (2015). Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Waltham: Morgan Kaufmann.
Macintyre, J. (2018). Power BI and Azure Data Services Dismantle Data Silos and Unlock Insights. Retrieved July 3, 2019, from Microsoft Azure Website: https://azure.microsoft.com/en-us/blog/power-bi-and-azure-data-services-dismantle-data-silos-and-unlock-insights/
Microsoft. (2019). Your Roadmap for a Digital-First Business: Transformation at Microsoft. Retrieved June 20, 2019, from Microsoft Website: https://info.microsoft.com/rs/157-GQE-382/images/dynamics365-en-digital-transformation.pdf
Microsoft Power BI. (2019). Self-Service Data Prep in Power BI. Retrieved August 15, 2019, from Microsoft Power BI Website: https://docs.microsoft.com/en-us/power-bi/service-dataflows-overview
Naous, D., Schwarz, J., & Legner, C. (2017). Analytics as a Service: Cloud Computing and the Transformation of Business Analytics Business Models and Ecosystems. In Twenty-Fifth European Conference on Information Systems (pp. 487-501).
Sedrakyan, G., Mannens, E., & Verbert, K. (2019). Guiding the Choice of Learning Dashboards Visualizations: Linking Dashboard Design and Data Visualization Concepts. Journal of Visual Languages and Computing, 50(2019), 19-38. doi: 10.1016/j.jvlc.2018.11.002
Stanek, R. (2018). Artificial Intelligence Is the Future of Business Intelligence. Retrieved July 10, 2019, from GoodData Website: https://www.gooddata.com/blog/artificial-intelligence-future-business-intelligence
Stodder, D. (2018a). BI and Analytics in the Age of AI and Big Data. TDWI Best Practices Report, 2018(4), 1-39.
Stodder, D. (2018b). AI for BI: Six Strategies for Augmenting BI with AI and Machine Learning. TDWI Checklist Report, 2018(9), 1-7.
Zinsmeister, S., Yeung, A., & Garrett R. (2019). AI-Driven Analytics: How Artificial Intelligence Is Creating a New Era of Analytics for Everyone. CA: O’Reilly.
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Published
2019-12-23
How to Cite
แซ่จึง เ. (2019). Business Analytics in the Era of Artificial Intelligence. Business Administration and Management Journal Review, 11(2), 157–177. Retrieved from https://so01.tci-thaijo.org/index.php/bahcuojs/article/view/215820
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