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BI with ML and predictive analytics: The art of using and molding business intelligence data into making data-driven decision making in companies
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Journal of Computer Science & Systems Biology

ISSN: 0974-7230

Open Access

BI with ML and predictive analytics: The art of using and molding business intelligence data into making data-driven decision making in companies


Joint Event on 5th World Machine Learning and Deep Learning Congress and World Congress on Computer Science, Machine Learning and Big Data

August 30-31, 2018 Dubai, UAE

Saransh Bhargava

Emirates Shipping Line, UAE

Posters & Accepted Abstracts: J Comput Sci Syst Biol

Abstract :

Business intelligence, delivers concrete data-driven results for businesses to leverage our strategic decision making for the business to operate. It converts raw data into meaningful insights for organizations to make better decisions. As an example, using the available BI tools like Qlik or Tableau, one can build better charts and graphs to simply analyze performance of one business aspect or combine multiple aspects for better results. BI, as a concept, when plugged with machine learning and predictive analytics, has immense applications, to identify patterns across big data and predict best strategies. Utilizing NLP/NLU and NeuralNet for text-based prediction to analyzing patterns across numerical data for revenue prediction are some applications of BI with ML. BI can be derived out of any data, using multiple tools available, like Microsoft PowerBI, QlikÔ??s QlikView, Tableau, SAP BusinessObjects, IBM Cognos and many more. Each and every tool has its own advantages. Major steps followed once BI is incorporated are reporting, analyzing, monitoring, predicting, data-driven decision making. Each step of the process has constant interaction with data warehousing, which in-turn has a previous step of ETL (Extraction, Transformation and Loading) of the data into the desired tool chosen.

Biography :

E-mail: saransh19bhargava@gmail.com

 

Google Scholar citation report
Citations: 2279

Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report

Journal of Computer Science & Systems Biology peer review process verified at publons

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