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


Periodically now, reports and articles emerge condemning Artificial Intelligence (AI) for job losses. A study by the Oxford University states that nearly half the known present jobs would be lost by 2033. The OECD meanwhile claims that 9% of the jobs in its twenty-one member states could be lost much earlier. Management consulting giant McKinsey too states that job losses could be pegged at five percent. However, deeper introspection of available data tells us that more than automating human jobs, AI is actually engaged in machine-to-machine tasks. Such transactions are the low hanging fruits of this field rather than the mass people displacement as fear mongers have suggested. IT, marketing, finance and customer service are broadly the four fields in which majority of the world’s companies are investing in AI for. Three broad methods have been identified to locate these low hanging fruits. The obvious first one is to use AI in fields with an instant return on revenue and cost. An example of this would be Amazon’s use of AI to detect frauds. Certain opportunities need to be identified where the talks could be accomplished using the same number of people as involved presently. The Associated Press (AP) for example automated a large number of factual stories to its AI usage to increase footprint without affecting any employment. The business transformations attempted must start with the back end operations and not the front office. This is because front end operations such as sales or customer service require empathy and touch ordinary lives on daily basis.


Uploaded Date:10 July 2017

When legendary mathematician Alan Turing, fresh from his World War II success of decoding the German Enigma first devised the Turing Test, it was barely conceivable that a machine could outwit a human’s intelligence. Now not only are machines regularly passing such tests, they are actually being seen as a threat to human employment. However, this grave fear need not be so as shown through the Century Link example. As one of the largest telecommunications firms in the US, Century Link has for long been in the business of transferring sales leads to clients from various sources. This year they invested in an Artificial Intelligence (AI) enabled tool called Conversica. It uses a virtual assistant Angie to scan through 30000 plus emails in a day and provide business intelligence on the sales leads after interpreting those mails. It was found out that Angie could interpret 99% of those emails without human assistance and there was a twenty times revenue generation per dollar spent on the tool. Similarly Epson America was getting a large number of leads from numerous sources but started using AI to streamline the entire digital marketing process and empower the sales team with more qualitative leads. Wentworth and Rapid Miner are two other companies with similar roaring success in using AI.


Uploaded Date:10 July 2017

It is well known that today organizations and platforms are generating enormous amounts of data. In fact, analytics, artificial intelligence, internet-of-things and big data are now among the most talked of aspects. However, utilization of such data depends more on the firm’s management rather than technology. Thus some methods have been separated out which must be the primary aim of such operations. First of all, this enormous data must be used for better decision making. Innovative products, processes and services need to be developed. Data must get imbibed into every aspect as information needs to get extracted from different products, processes and services. This business intelligence garnered must be used to disseminate relevant content to concerned stakeholders. There must also be a method where one can be directed towards the right content as Google and Quora are already doing. The overall quality must be improved, costs eliminated altogether and a sense of trust developed. Asymmetries get built in when one party has more information than another. Such asymmetries need be leveraged as a business proposition as done by several hedge funds, car dealers, sports venues and airlines.


Uploaded Date:01/07/2017


Machine learning leverages Artificial Intelligence (AI) for digital transformation. By the year 2025, this along with related technologies could be worth a hundred billion US dollars in the market. There are several ways however using which machine learning is helping companies improve their work processes. As a first, customer service is getting improved by making use of chatbots. Machine learning is helping gauge real time authentic business intelligence on customers’ traits. This is further enabling their individual characteristics to emerge so personalized marketing can be possible, enhancing their loyalty levels. A lot of mundane, repetitive jobs are getting automated, with highest positive impact being felt in finance, enabling the professional from the field to focus on high level strategic tasks. Such automation also helps reduce manual fraud through early detection. Talent recruitment has also improved as algorithms can cut across vast swathes of data, thus eliminating clutter. Similarly, this analytics can also be used to assess the overall brand exposure. A lot of data can also predict upcoming business trends, market needs and forecasts. Even supply chains are now smoother due to better developed algorithms. Other areas, where machine learning could soon further improve operations include Drone or satellite induced asset management, career development and assessment of retail shelf lives.


Uploaded Date:27/06/2017

Researchers at the McKinsey Global Institute (MGI) pointed out several years ago that retailers who made use of business analytics would be able to reduce costs by eight percent and improve operating margins by about three-fifths. There are some digital natives such as Amazon, Google and Facebook that have made extensive use of Big Data to excel in their businesses. But for legacy companies the impact has not always been rosy as they have had to re-jig their entire processes to maintain relevance in the present time. Their core processes long pre-date the advent of the digital age. Thus, few of them have achieved the optimization of data analytics at scale. In case organizations aren’t able to exploit these new technologies they might as well consider some useful online tools that process data at fast pace. Their adoption gets complicated by the fact that a lot of front-facing managers do not find the use of analytics as beneficial towards decision making. So adoption of tools is often not matched by enthusiasm among the staff members. Companies must take active steps to imbibe such hands-on training on analytics for its employees.Source:


Auto giant Ford has imbibed Big Data capabilities in a big way to analyze micro-trends at the industry wide level. Ford has always been progressive about the use of technology having put in microprocessors to monitor fuel usage back in the 1970s. But now the capabilities have shifted from an earlier almost complete reliance to hardware to a situation where the same forms only three-fifths of the overall technological footprint. Ford is also making extensive use of cloud based technologies. Cloud also helps in data warehousing operations, as otherwise it would get nearly impossible to store the quantity of data now generated. This also enables the tracking of any automobile using simple devices such as smartphones. Privacy could have become a sticking point but Ford has taken due steps to regulate this by affirming that all data collected from passenger vehicles is owned by the customer. Ford is adamant that while this data may be useful for non-automotive companies too, it cannot be shared freely.


Chief Marketing Officers (CMOs) have unique challenges these days propelled by innovations such as cloud computing, business analytics, social media, smart phones and gigantic heaps of data. That is why it is important for them to prioritize functions and realign the company’s focus in that direction. First of all the disparate streams of data from different sources need to be interwoven together to derive cohesive meaning. The data warehousing operations done must deliver customer value. This will help in providing relevant solutions to the customer base. Costs are being controlled by leveraging social, mobile, analytics and cloud technologies as has been done by Cisco. Instead these investments like events and tradeshows are being driven towards training the people or new technologies such as on automation or loyalty programmes. Habits also need to be modified accordingly. For example the content needs to be tailored specifically for the relevant audience rather than mass campaigns. Data needs to be replicable to be a success in overseas markets as well. The CMOs can no longer simply allow the CIO to provide all technology support as marketing has becoming increasingly driven and not just reliant on technology. The CMOs needs to understand basics of data security.


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