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Building Data Warehouse from Scratch & AI role in Data Analytics


via Zoom Webinar on Thursday, 20 February 2020 at 08.00pm IST

Data Warehouse Myths and Reality

There are a lot of misconceptions about an enterprise data warehouse, with some even believing that the traditional data warehouse is dead. An effective enterprise data warehouse produces many significant benefits, with the value of a data warehouse solution being directly related to achieving the goals of your business. 

Enhance business intelligence – Data warehouses have a history of providing deep insights to the enterprise, but not always to the right people.  A data warehouse that is built to scale and grow appropriately, can become the fastest way to turn data into insights. With improved access to information, managers and executives will be able to make critical data-driven decisions based on credible facts backed with concrete evidence and actual organizational data.  Enterprise data warehouses have been around for decades, but have become a vital part of an organization’s strategy to improve business intelligence capabilities.

Save time and money – Some companies lose money spending too much time pulling data from multiple sources. 

Increase system and query performance– Data warehouses are specifically constructed with a focus on increasing the speed of data retrieval and analysis. With a solid data model, you can quickly query the data being pulled from a large volume of integrated, quality information that is stored in the data warehouse.  Having these analytical systems constructed differently than operational systems distributes system load across an entire organization’s technology infrastructure. By querying and analyzing data within the data warehouse, organizations can improve operations and enable more efficient business processes, thereby increasing revenue and raising profits.

What is the Agenda?

  • Data Warehouse Myths and Reality
  • Journey of Scattered Data to Information Insights
  • Tools, Technologies and AI role in Data Analytics

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Time & Date: Thursday, 20 February 2020 at 08.00pm IST

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Know more about your Webinar Host

Dr. Prakash Sharma

Global Startup Ecosystem - Ambassador at International Startup Ecosystem

Work Experience: 27 yrs 6 mos

2011 to 2018
Have been building multiple algorithms to predict investor group dynamics, startup brand trends.. and identifying various news alerts through venture intelligence initiative as part of PhD
Designed the entire framework for automating publishing printing process, and analyzed the information and NLP applicability for the process to minimize human interventions… Conducting free sessions on data science using a mix of various tools like R/Python to build awareness.

Presently researching on Robotic Process Automation

1998 to 2003: Architected data warehouse platform to integrate data from multiple tools as well as generate source code… Designed machine learning algorithms to capture profile data and understand group dynamism within employees, worked on email data analytics.

1995-1998: Was instrumental in conceiving Remote Monitoring projects to capture data from various weather information system.. Was monitoring large Automation Projects for Plants, Worked on enterprise search optimization to intelligently capture failure points in Plant Manufacturing Solutions to study engine performances. Worked on an algorithm to capture data from aerodromes for facilitating Aerodrome Mgmt System. Performed Statistical analysis on machine data.

1992-1995: Was involved in data collection, analysis of village data for watershed development, which included studying lineaments for Deccan Plateau using a mix of Space Data, GPS Data, and Survey Data. Mapping the same & predicting locations for watershed treatments. Had conducted training on Ethnography, Library Information System… Transitioned library information systems for WALMI, DIRD and optimized search indexes using PASCAL. Installed Weather Information Systems in remote areas Performed Statistical Analysis using a software program as well as Statistical tools. Built cost prediction model on the basis of data logs from networks using Novel Netware/ Unix.