Data Warehousing & Business Intelligence

 Data Warehousing


                                             Data Warehousing & Business Intelligence

Why DW & BI

1. Business intelligence is new management reporting and dashboarding.

2. Data warehousing and business intelligence can help the organizations both proactively and reactively managing areas of concerns and identifying areas of potential.

3. Most of the large setups in India and across the globe have BI systems either implemented or are in process of implementation.

4. BI systems have seen proven benefits in businesses across the industries like airlines, public administration, health care, FMCG, manufacturing, financial services etc.

Benefits of DW & BI Course

1. The demand for BI experts is increasing at the rate of approximately 25%

per year.

2. This course is meant not only for IT professionals but for industry experts as well since BI is more of a business function now rather than IT function.Business Intelligence

3. Learn from the experts – We have engaged the best brains in the industry with a sea of practical and implementation experience in the field to give you the best available knowledge and share the real life case studies and experiences across different domains to prepare you for the real world.

4. Learn and understand the functionality various tools and technologies for DW & BI.  

Course Contents

Module 1 : Basics of Data & Data Management

Module 2 : Data Warehousing – The Basic Understanding

Module 3 : Data Warehouse : Design & It’s Implementation in Business

Module 4 : Tools & Technologies of DW

Module 5 : Business Intelligence – Origin & Foundation

Module 6 :Designing BI systems for Business Use

Module 7 : Tools & Technologies of BI

Module 8:Project Work

Personalization of content and offerings has become central to all marketing activities now, especially when on the web. For any operating model to work for personalization, there are four major factors that must successfully play on. First of all, there must be a foundation for data warehousing, so that the right data may get captured, for future use. Once data is mined, the decision- making process must be smooth so that people may act on the business intelligence captured. The right offers and communication module must be communicated to the customer base. Once this insight has been captured, it needs to be distributed to the right channels. For developing this personalization strategy, first of all, there has to be full commitment to the process devised. A proper data governance policy too needs to be set in place. The people in the team need be given the proper tools and technology so they may be empowered in the process. The Key Performance Indicators too need to be outlined so that both the major, as well as intricate expectations may be defined. Long- term talent development has to be done and the leadership must be invested in this personalization strategy.


Uploaded Date:29 December 2018

A McKinsey report recently outlined how the era of simple experimentation is over for the field of business analytics. Instead, companies themselves need to become analytical, rather than anecdotal as is the trend presently. Data is very much the fuel to the entire needs of the process. A report by Forrester claims that the present year’s Big Data footprint alone would be worth US$ 31 billion. For a start, all analytics tools need to be used to solve genuine business problems, and not just check functionality or features. Strategists must understand that analytics is an investment for a long- term solution. So, the process needs to be an ongoing one. This analytics needs to be embedded across the business areas within the organization. The analytics- driven culture will only arrive when the executives are on board. So, they need to be doing their part in it.


Uploaded Date:28 December 2018

There is a lot of talk now on the use of Artificial Intelligence (AI), deep learning and machine learning being applied to various business processes. One area where the use of AI needs to be ramped up is in talent recruitment especially at the executive level. A venture capital firm in Hong Kong – Deep Knowledge Ventures- used AI to direct its investment portfolio after it realized that there were too many of its investments that had gone awry. So, a suite was assigned to its board of directors to aid them, and now the firm holds AI is crucial to bringing it back from the brink. Even the healthcare industry is making use of a gene-editing tool by CRISPR to solve its problems. AI has to be used to reduce the time required in the hiring process. But for this to happen, the firm must hone its data warehousing processes, as data is the fuel behind all this use of AI. Once this is done, a pipeline has to be curated that is based on succession data and the hiring trends. The tools must then be deployed to predict or suggest the right compensation figures and the most qualified candidate fits.


Uploaded Date:03 December 2018

While data has become essential to the businesses in the ongoing digital age, it must be treated as more than a mere technology. A data culture is needed to leverage the insights gained. Several business leaders have pitched in with their suggestions on how to best develop one such. One view is that data culture and decision culture are related, as decisions can best be take only post detailed data warehousing. Another is that this data culture must emanate straight from the top, from the level of the board and the C-suite. The flow of data has now become democratized. But companies need to beware about the risks associated in case of any privacy or security leaks. And that is why another view states that companies need to be really careful while sharing any data beyond the company premises. Each organization needs to identify a set of people who will act as the culture catalysts. They will make sure that the right processes are followed, and people will look up towards them. Ultimately no data-driven work will succeed without the right people. So, the entire talent recruitment drive and subsequent onboarding has to be made with the belief that this data culture gets aligned to the people being hired.


Uploaded Date:15 November 20185

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