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.BigDataBusinessAnalytics2

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


The 4 mistakes most Managers make with Analytics

There are some common mistakes that most managers commit with the use of business analytics. First of all many do not understand that Big Data on its own is not as useful as its proper understanding and integration with various sources of data to get the complete holistic picture. Having too much of data is also not useful specially when it is in unstructured form. A lot of managers read too much into correlations, even though in reality a lot of them maybe simply coincidences. Machine learning algorithms do develop certain trends which appear to lead to business outcomes but actually spurious findings. More than the data itself, it is its handling that is crucial and thus managers must assemble a good team to work on the data. The right analyst and the right skill level will deduce authentic results out of the data.



Insights- driven Business are stealing your Customers

There are several business these days that are not leveraging the kind of business insights they are obtaining. Insights driven organizations such as Uber, Google, Facebook, Netflix and Amazon are disrupting traditional business. These kind of organizations leverage business analytics to address more informed decision making. Strategic investment is done in analytics with involvement of the top management. These companies are agile and operate in a close loop thus ensuring that their implementation of observations is much faster. They realize that humans are ultimately smarter than machines and thus do not put blind faith on the latter. Before devising algorithms to capture insights, they consult the people with technical knowledge in those domains concerned. Enormous amount of data warehousing is done to conduct the analysis.



How one Company used Data to rethink the Customer Journey

Over the last few years, customers’ perception towards their relationship with companies has evolved. Earlier, any form of intimacy shown by the sellers was seen as akin to stalking, but now customers have also realized that with the vast amount of data available, companies are bound to track that for business purposes. They also understand that eventually this can led to a better level of service. One company SAS, used business analytics effectively to redraw the conventional customer journey. At first level, a need arises. Then some form of research goes in to searching for the top players and options. Then, decision is made to choose one brand over any of its competitors. The purchase then takes place followed by usage of the product. After satisfaction, the customer becomes a brand advocate and starts recommending the same to others as well.



Figuring out how IT, Analytics, and Operations should work Together

IT, analytics and operations are departments within organizations which need to work together. The models of collaboration may be different but the end objective has to be efficient delivery of services. Four models have been revealed. In the first one, operational data and business analytics functions are integrated to one team. Here no corporate IT is needed, instead processes are inbuilt. Then there is the model where analytics and IT are independent of each other. Here it is corporate IT that is used as the engine to power analytics. In the third model, data analytics is embedded within IT. This model avoids digital disruptions but business demands exceed the capacity very often. On the other hand, the last model has the same analytics embedded within operations department. All these models have some advantages or disadvantages over the other ones.



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