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Big Data & Business Analytics

Big Data Analytics

POST GRADUATE PROGRAM IN BUSINESS ANALYTICS AND BIG DATA BY REPLACING “PGP – BIG DATA & BUSINESS ANALYTICS”

Why Business Analytics

1. The future – Industry have so much of data available now generated during the digitization phase in last 2 decades. Now is the time to utilize this data for executive management and strategic decision making.

2.Biggies like Tesco, Wal-mart, American Express, New York Stock Exchange etc have already Big Data implementation on the way.

3. McKinsey Global Institute’s Big data: The next frontier for innovation, competition, and productivity estimates that by 2018, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effective decisions.

4. 90% of fortune 500 companies will have big data initiatives underway by end 2015.

Benefits of Big Data Analytics Course

1. Analytics using Big Data can be leveraged across all industries including Pharma, Telecom, Manufacturing, Retail, Health care, Public Sector Administration.Big Data & Business Analytics

2. Estimate of over 2.5 lakh big data analytics jobs to be in there by mid 2015 in India alone.

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 applicability of Big Data analytics using cross section of tools and technologies that can be used tomorrow.

Course Contents

Module 1 : Basics of Business Analytics & Business Mathematics

Module 2 : Predictive Analytics and Advanced Techniques

Module 3 : Business problem solving using advance analytics

Module 4 : Analytics in the age of Big Data

Module 5 : Big Data implementation across domains

Module 6 :Big Data Architecture

Module 7 : Data management using big data

Module 8:Benefits and business implementation of big data over the conventional

data management

There exist several myths surrounding the big data platforms. One of them is that if all the data sources are streamlined together, the most accurate of business insights may be procured. But this isn’t necessarily true, as several data sources just can’t be streamlined together due to the diverse policies, security requirements and related restrictions. Another myth is that with data all at one place, one can focus most on the business analytics. Instead, data needs to be stored on catalogue basis. The third myth is that industry technologies need to be used up. But more than the kind of technology, an agile structure is needed. People also believe in hoarding data, as more will apparently help us answer an increasing number of questions. In reality, it is about the quality of data and the tools being used, and not the extent of inputs available. The final myth is that there aren’t enough of data scientists to perform the tasks necessary for the analysis and intelligence gathering processes. Truth be said, more of capabilities need to be utilized by the data- driven solutions providers.

Source:https://boozallen.com/s/insight/blog/5-myths-of-big-data-platforms.html

Uploaded Date:24 October 2019

Predictive modeling and business analytics is of course supposed to be cold and unbiased. But it is an oxymoron to say that there are inherent biases that predictive modeling is laden with. This is because the data warehousing done, followed by the data set understood for analysis, is chosen as per human selection. This selection process is full of biases, unknown to many. A method has now been developed by a statistics professor. It specifically tackles the issue of race- based biases. Criminal justice for instance is one area where the use of algorithms needs some form of extra precaution now. These algorithms must be considered primarily for providing us the inputs, on which we may process out information.

Source:https://knowledge.wharton.upenn.edu/article/removing-bias-from-predictive-modeling/

Uploaded Date:14 October 2019

Most organizations these days want to make full use of the data warehousing being done. This is primarily to forecast trends, but also to understand their customers and the markets. That is why the new generation of business analytics makes insights discovery so much more efficient. This branch of analytics comprises of four sub- categories which are- predictive, prescriptive, descriptive and diagnostic. Each have their own sets of advantages. Descriptive is to understand “what happened”, while diagnostic is to assess “why did it happen”. Predictive is to provide foresight, while insights can best be gauged using prescriptive analytics. The greatest power comes from the combo of prescriptive and predictive analytics together.

Source:https://www.govloop.com/community/blog/predictive-analytics-and-insights-discovery-were-made-for-each-other/

Uploaded Date:14 October 2019

The platform economy, that we are all a part of now, requires a different mentality to do work in. There are now a number of companies that are actually fake, and use their reach to fraud customers. To be careful, the area of talent recruitment also needs a change in game plan. As several freelancers and part- time workers are part of many companies now, they need to be sourced, but with caution. For this, Upwork is the best platform. It uses big data to connect recruiters with the potential talent. Uber’s success with drivers can well be replicated by Upwork, when it comes to tech and content professionals. Three kinds of platforms are now in vogue. These are for transaction, innovation and a hybrid model. Upwork falls under the first category. The utility of Upwork is best in that it can work across verticals.

Source:https://hbr.org/podcast/2019/06/in-the-platform-economy-upwork-searches-for-better-matches-in-the-cloud

Uploaded Date:20 July 2019

Tech companies have often borne the brunt of diversity champions. As a result, too often in order to please this lobby, such companies have used data- driven business analytics to ensure a greater diversified talent pool. A number of these initiatives can get awry thanks to the presence of a small sample of numbers to work with. Some steps have been identified which these tech companies must work on, to solve this conundrum. For a start, one needs to make claims with whatever sample sizes are available. One needs to dig deeper within this data set. Managers need to be deeply involved in it. The talent recruitment process must not however fade away, simply to the presence of some numbers. One needs to up the sample sizes eventually.

Source:https://hbr.org/2019/04/the-mistake-companies-make-when-they-use-data-to-plan-diversity-efforts?utm_source=twitter&utm_medium=social&utm_campaign=hbr

Uploaded Date:26 June 2019

There is a growing trend to over- emphasize on the importance of data analytics. A lot of corporate leaders nowadays, instead of taking decisions themselves, like to leave it all to this data- backed business analytics. This is wrong, as instead the analytics must aid business. Too much of time now gets wasted on methodologies, rather than the actual work. First of all data needs to be purpose driven. To get the best out of the data warehousing done, the right questions need to be framed right at the start. One needs to take a strategic look at the bigger picture. But at the same time, intricate details cannot be missed out. Taboos cannot be completely driven out. So, the top team needs to embrace them, and work around their limitations. Once the minute details are out through analysis, the top management needs to connect the dots, to understand the bigger context. Besides merely focusing on the outputs and productivity, one has to concentrate on action. Crucially, the team built for this purpose, too needs to be multi- skilled.

Source:https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/making-data-analytics-work-for-you-instead-of-the-other-way-around

Uploaded Date:26 June 2019

The field of people analytics has now gone mainstream. It refers to the use of advanced business analytics on the field of people management. Individual HR processes are eased up using this. Data warehousing remains among the top challenges, as without the right inputs, insights cannot be extracted. It is also a must for mapping the talent value chain. To do this, the first step is always to define what is top priority. Wherever there exist gaps in the data collection, those need to be filled urgently. Post the selection process, on boarding is the next critical step in the right direction. Daily management processes need to be put in place. The behavior and interactions of key stakeholders, especially of employees needs to be mapped out. A lot of the insights that data delivers us, challenges the conventional wisdom understood. Frontline employees typically fall under four different categories. The first are the socializers, who have a high EQ, are spontaneous, and are generally the higher risk- takers. Potential leaders have several of these skills, plus are good at multitasking. The third category are the- entrepreneurial taskmasters. They are good at planning and execution. The fourth ones are the conservative taskmasters. They aren’t risk seekers, nor are they much adept at multitasking.

Source:https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/using-people-analytics-to-drive-business-performance-a-case-study

Uploaded Date:15 June 2019

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