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

Billions have been invested over the past few years, in the area of data science. This has had major repercussions in every segment. Many companies though aren’t yet getting the value out of all the data warehousing they are consequently doing. This could be due to the lack of clear verdict on what data is useful and what constitutes junk. Data scientists often fail to convince the decision makers about their genuine findings. This is often due to the jargon like language used in their presentations. A lot of data scientists present the truth as is convenient for them from a certain angle. Sometimes, even if they present the right facts, the ones listening may interpret according to their comforts. In order to build a more relevant data science operation, one needs to define the role of each team member, and not just merely recruit them. The talent recruitment team must select for different skills sets such as project management, data analysis, data wrangling, design, storytelling and subject expertise. A portfolio of varied talent needs to be staffed for. Each of the team members need some sort of exposure to skills they do not yet possess. The projects needs to be structured around the personnel and the talents they possess.


Uploaded Date:26 June 2019

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

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