Big Data & Business Analytics

Big Data 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

By now most companies have realized the importance of Advanced Analytics (AA) to their marketing strategy and decision-making. Unfortunately, as it emerged from a study conducted by McKinsey, less than a fifth of those polled have truly leveraged the vast scope of business analytics. Most firms start eagerly, but without any clear-cut strategy. As a result, they initiate several pilot projects but fail to scale up. While companies are engaged in storing huge quantities of data, due to lacking any end-to-end approach, they fail to leverage the same. Thus, before pursuing any such AA-modeled transformation, companies ought to ask themselves some clear question beginning with the structure of the organization. This involves whether it must have a centralized, decentralized or even a hybrid model. Firm decisions must also be made on whether any part of the work is to be outsourced or not. Finally, one has to decide where to locate the AA unit. Due diligence must be put in while staffing the members at the AA centre of excellence. The right corporate training programmes need to be initiated for the concerned staff members. And likewise, the right strategic partnerships signed. This training must focus on matters beyond the quantifiable, but on the cultural transformation to be effected.


Uploaded Date:14 November 2018

In common parlance the fashion and apparel industry seem to have little to do with advanced business analytics. Apparel makers and retailers have traditionally been stronger on the art side of business, taking decisions based on judgement and intuition rather than on the science of such quantifiable analytics. That would need to change, as demonstrated by Amazon’s use of the same. Some actions have thus been identified which all apparel retailers need to apply in order to improve their business. Firstly, they must know how to prioritize when it comes to markets and segments as not all have equal potential. Data warehousing is to play a massive role, as this vast treasure trove may get analyzed to extract meaningful business insights. Instead of continuously keeping an eye on the calendar, due focus must be made on the value addition. The recruitment has to be well thought out with the right mix of designers representing the art and the data scientists representing the science side.


Uploaded Date:13 November 2018

Major marketing decisions are no longer made on the basis of intuition or experience. Thorough use is made of big data. These data sets are gargantuan enough that traditional data processing systems are unable to cope with them. Google Analytics is the perfect tool to gauge the kind of insights such data sets may provide. It also helps in making digital marketing campaigns more successful by first of all designing better campaigns. The market intelligence received also helps in setting more pertinent pricing. This is especially relevant for the B2B segment due to the singularity of each occasion. This intelligence gathered on consumer needs, helps funnel the right web content as well. Netflix is an example of a company that uses this data to disburse personalized content suited to different market segments.


Uploaded Date:12 November 2018

Machine Learning (ML) iteration is not the simplest tasks due to the fact that the requirements constantly move goalposts. Plus, due to a lack of standardization, there are no adopted benchmarks against which to deploy one’s latest development. Due to this reason, developers must make ample use of the existing tools for its development. Some such common tools include Scikit-Learn, Pandas and Feature-tools. There are three steps that need to be followed in the entire development cycle. To start with is Prediction Engineering. This is about setting up the ML problem. Next up is Feature Engineering which iterates how ML gets powered. And finally, there is the Modeling phase. This is the business end of things where one works with algorithms. This algorithm in turn uses the business analytics model to make predictions from the existing data of possibilities.


Uploaded Date:10 November 2018

The role of digital technologies has been well documented by several publications and reports. But one area where the impact of big data has been underscored is the global food chain. At every level from the farms to the plate, food companies can harness this data to create sustainable solutions. There are several steps in this overall journey, beginning with innovating at speed using soil sciences. This must lead to holistic input optimization. Farming operations then need to be optimized. The supply chain will automatically become more transparent thanks to the presence of data points across the journey, connected digitally. Downstream operations have to be stepped up to improve operational efficiency. There will be infrastructural challenges along the way, especially in emerging markets such as in Africa. Business analytics can help identify these bottlenecks in infrastructure before providing ideal warehousing location inputs using geospatial model. Once the cycle is completed, the supply chain company must anticipate waste. Granular data needs to be collected for this from waste streams before proving solutions from the retail level onward.


Uploaded Date:10 November 2018

Professional social networking platform LinkedIn has now launched Talent Insights. Developed by Microsoft, Talent Insights is a business analytics tool to identify talent. This simply reiterates the importance LinkedIn is paying to human resources software. The tool will provide real time insights on people. It will help managers engaged in talent recruitment and retention. To help such professionals, LinkedIn creates a Talent Pool report which will break down the performance of employees as well as potential candidates across factors. These factors include location analytics, attrition rates, workforce planning and product management. This suite is not yet directly a competitor to SAP Success Factors or Workday. But once usedcombo with Microsoft Dynamics, the results are formidable.


Uploaded Date:09 November 2018

Today there is very little excuse for businesses not to make use of data for their decision making. This is true for both consumer-facing as well as B2B firms. The head of digital marketing at Google says that this approach will soon replace the HiPPO bias. HiPPO stands for highest paid person’s opinion. This usually means the opinion of the CMO or other such high official, whose opinions are based one experience and gut feeling. This might work out on a number of occasions, but won’t many times. The healthcare industry is making use of data for its decision-making, but few players are able to make the right use of business analytics. Instead of using data as a transformational force, many are simply using it as a defense mechanism towards their confirmation bias. As a result, data and its subsequent analytics is only taking place retroactively, after a project is done. This allows for analysis, but remain postmortems. But those shunning HiPPO will go a step ahead and proactively weed out deficiencies for the next projects.


Uploaded Date:08 November 2018 

SKYLINE Knowledge Centre

Phone: 9971700059,9810877385
© 2017 SKYLINE. All right Reserved.