The world’s music habits were once relatively private. Record companies were aware about which radio station played their songs and where their CDs were popular, but that information painted an incomplete picture at best in reality. Who knew what music people were sharing on tapes and CDs burnt in the privacy of their own bedrooms? The sales figure notifies companies about the number of records or CDs sold, but no information can be tracked after that. With the time, the music industry is changing, thinking is changing, market is changing. The explosion of data from sources like torrents, music streaming sites and social media platforms has offered the music industry a huge opportunity to track sales, monitor post selling behaviour, understand their fans and spot upcoming artists like never before. Music analytics is now worth an estimated £ 1.8 billion per year. While internet is taking power away from record labels, it is also giving them the ability to predict future hits. Universal Music UK’s director of digital, Paul Smernicki, thinks that ultimately the music industry will always be focused on content, no matter what analytical tools are available. According to him,“It’s important to remember that it's just a set of tools to help inform us. The data doesn't make the decisions, that's an un-replicable part of what we do.” But with robots replacing humans in every sphere of work, who knows how long it will be before music- the most successful talent scout in the industry is defined by an algorithm!

To read more, visit: 

http://www.theguardian.com/technology/2014/apr/09/music-analytics-is-helping-the-music-industry-see-into-the-future

Most retail companies today operate in the master franchisee model where master franchisee manages the operations of the different stores of the brand. In such cases, it is important to understand the difference between what the company sells to its franchise stores and what the stores in-turn sell to the consumer. This metric is where the insights are hidden. Hence it is very important that companies track &understand this metric to optimally manage stocks and avoid costly stock-out or over-stock situations. Important decisions like the number of new stores that should be opened, best location & size of each store in different cities are not a straightforward choice! Data analytics usage in this space has proved to solve this dilemma. It helps companies to decide the optimal size of each new store they open, while ensuring that these decisions takes into account key parameters like real estate rental costs, catchment area, average incomes in the geographical area, presence of competition etc. Therefore, data analytics is simplifying complex business problems.

Arvind Nagpal wrote about this aspect in his recent article. To read more, please visit the following link:

http://www.informationweek.in/informationweek/perspective/288161/analytics-helping-retail-outlets-critical-decision

According to a new survey conducted by Deloitte, it is found that as complexity in global supply chain networks continues to increase, less than one-third (26 percent) of business executives are using data analytics tools and processes to help manage third party relationship risks.Thirteen percent of those surveyed are still learning how to use analytics software and 22 percent use no data analytics at all. Few market leading companies leverage advanced data analytics tools and forensic accounting to identify anomalies in their transactional data & getting astonishing benefits while rest are lagging far behind in this issue. Nearly one-third (31 percent) of business executives surveyed, said that their organization has faced supply chain fraud, waste or abuse in the past 12 months.

Mark Pearson, principal, Deloitte Financial Advisory Services LLP, speaks about why organizations need data analytics to tackle supply chain fraud. To read more, please visit the following link:

http://www.net-security.org/secworld.php?id=16641

The visual data discovery (VDD) approach increases the probability that business managers will find the information they need in time to make effective decisions. This is the main reason behind VDD’s rapidly growing fan base. While traditional BI solutions can be invaluable, they do have limitations. Dashboards come in handy in few cases, but there is still a fundamental problem – the dashboard designer must have anticipated the need for that line of questioning or train of thought when the dashboard was originally built.  So, if unexpected questions arise, business managers end up making decisions without data, operating on gut feel alone.  And that means they end up operating without real answers at all! So, here’s the key question:  Do you ever get questions in your logistics operations that are unexpected and hard to answer? This is where visual data discovery tools excel.Visual data discovery tools provide a rich, visual environment that enables business users to easily interact with, and manipulate, information directly, hands-on.  They provide a compelling solution to help business managers explore data, without the assistance of skilled IT staff.

To know more, visit David White’s article by clicking on the following link:

http://logisticsviewpoints.com/2014/04/07/new-approaches-analytics-revolutionize-logistics/

The trend towards larger data sets is due to the additional information that can be derived from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data. This is allowing better correlations to be found to spot business trends, determine the quality of research, prevent diseases, link legal citations, combat crime, and determines real-time roadway traffic conditions. Today, the applicability of Big Data analytics is not limited to only organizations. Big Data wings have established a longer reach beyond that sphere. Many apps are already in place to help people find the cheapest flights, improve their diets, even suggests how to become better drivers! From travel to shopping to health to civic life, big data got a role to play. Embracing Big Data analytics has already started paying dividends to the consumers. With more explorations, Big Data analytics is going to help each and every curve of consumer lifestyle in not so distant future!

A recent article posted in wall street journal by famous author Lora Kolodny, gives a detailed view on this subject. To read, click on the following link:

http://online.wsj.com/news/articles/SB10001424052702303369904579423383203100794

Companies do a lot of expenditure on resources to analyze big data. But surprisingly, in most cases they fail to address the question of why they are doing so. Many times they use different high end big data software unnecessarily when there is a simpler solution. Before starting the analysis of big data, a proper data thinking approach is necessary. This will not only save company’s time and money, but also optimize resources. So before mining big data, we have to think analytically with some small data. It is data thinking that can prove to be really big.

Robert D. Behn, lecturer at Harvard University’s John F. Kennedy School of Government, talks more about defining a proper question before analyzing Big Data here:http://www.govexec.com/excellence/promising-practices/2014/04/got-big-data-first-define-your-big-question/81661/?oref=river .   

Eric Siegel, Ph.D., founder of Predictive Analytics World and Text Analytics World and Executive Editor of the Predictive Analytics Times, talks about how businesses can incorporate predictive analytics to upgrade their process.  A predictive model will help to know which new customers are likely to return and which are probably one-timers. He also mentioned about various benefits of retaining new customers. Moreover, by adopting this model, the first time customer can become a loyal customer.

For more information follow  http://www.predictiveanalyticsworld.com/customer_retention.php

Many marketers find themselves faced with overwhelming amount of data and organizational complexity, rapidly changing customer behaviors, and increased competitive pressure. Data or information has always been the USP, but now people are not using that USP in their advantage. If an investment is made in big data, one can extract only the relevant information and analyze it to transform the business decisions for the future.

Please go through the link for more details:http://www.marketingprofs.com/charts/2014/24321/marketers-and-big-data-in-2014-barriers-budgets-and-opportunities

Analytics can help businesses greatly to increase their online sales. Surprisingly, the potential of analytics is not yet fully uncovered. In most of the cases, either the business owners or the managers do not take the full advantage of it or they don’t know how to use it.

So, how should businesses use Analytics and interpret them to decrease attrition rates and increase sales on their website? See the tips here: http://cio.economictimes.indiatimes.com/news/business-analytics/8-ways-to-use-web-analytics-to-increase-online-sales/32728371

Whether it is elections in India, or a service delivery model of a market leader in any sector, the incumbent is always at a disadvantage. Be it their government, their services like banking or healthcare, or their retail experience, the consumer wants them to continuously evolve in a direction which they perceive as beneficial to them. As time bound service contracts become passé, disruptive technology becomes available at affordable prices (think 3D printers, e-books, digital content, Google fiber, multi-core smart phones) today’s consumers and clients are exposed to a plethora of choices. With lowered exit cost barriers it is becoming imperative that a business does all it can to retain their customers.

Organizations realize that they not only have to ward off direct competitors but also cope up with tangential attacks from substitutes powered by disruptive technology. The choice between a successful well proven business model and a new idea is never easy. In the first case the organization risks its immediate client base by diverting significant funds and effort for turning an innovative idea into a winning product. This strategy has a high probability of failure which might cost them a significant market share. HTC lost a large portion of its small-medium priced phone market share in its bid to create a premium flagship smartphone model. In the second case, it believes in its business model and tries to take incremental steps to make it more robust till it is too late for them. Blockbuster, Blackberry and Borders are examples of organizations which could not counter the threat of new market players like Netflix, Android (Samsung, LG etc.) and Amazon. There is also a third case of the likes of Barnes & Noble and Nokia where the incumbent tries to play it safe by not going all out for the new technology but yet has a back-up plan to survive. So what’s the magic mantra? To me it remains the quintessential quest.

Though one thing I firmly believe in, is that the fickle consumer / client has become even more choice aware and will try out the new flavor of the month if they feel that their implicit or explicit objectives are not being met in the vendor-client relationship. Thus it is essential that organizations know the pulse of their clients and consumers and be geared towards meeting their changing demands. Analytics can play a key role here. Predictive analytics can help organizations preempt attrition by taking mitigating measures while social media and web analytics can help them know what some of their consumers are thinking and provide insights into their future behavior.

While the objective of analytics i.e. to provide actionable insight, remains the same, the demands of the information consumer have drastically changed. They want a lot more data crunched in much shorter time frames and yet presented with all the bells and whistles. With today’s scalable cloud computing infrastructure like AWS available and tons of reporting packages which play well with them it’s almost possible. It is also possible for the security conscious to invest and manage the requisite infrastructure in-house. The JIT (Just in time) lean principle applies here as well. An organization should only generate information and insights at the speed which it can take action on. Anything more could be a waste of resources.

Thus at the end of day, know the customer’s pulse, make an informed choice between evolutionary and revolutionary changes to your business model and be prepared to be blind-sided by someone your organization may have never heard of. To be or not be is an incumbent’s curse but making an informed choice and being prepared for change must never be a dilemma.

In this era of advanced analytics, there are many new challenges involving privacy and security of individuals, as computing power allows nonprofits, businesses, and researchers to gather ever-larger troves of information. Though it can be observed that there is a colossal increment in the amount of available data, we still only have two eyeballs and one brain! How a traditional library will look without a librarian, or an encyclopedia without an index? Scale that prospect up to the realms of web analytics, astronomy, high-speed finance, or even basketball statistics, and the problem becomes clear.

Research scientist Fernando Pérezof, University of California, Berkeley, recently spoke about it at a symposium titled “Weathering the Data Storm: The Promise and Challenges of Data Science,” hosted by the Institute for Applied Computational Science (IACS) at the Harvard School of Engineering and Applied Sciences (SEAS).

To read more, Please visit Harvard university article link given below:

http://news.harvard.edu/gazette/story/2014/01/the-promise-of-big-data/

Big data is the buzzword and messenger, but not the whole story. Yes, tons of telecom use cases will fit the so-called 3 V’s of big data: volume, velocity of analysis and variety of technologies. But today, a rise of fourth V can be observed in the big data equation: Value, referring to optimizing performance, maximizing revenue & monetizing the telecom business in more sophisticated ways.

The Telecom Analytics & Big Data Market, a breakthrough analysis report by Dan Baker, editor of the Black Swan Telecom Journal & research director of Technology Research Institute, gives us new insights about telecom big data analysis. In an interview with Craig Galbraith, Mr. Baker speaks about competition, barriers of market entry, customer experience, network analytics, integration with cloud, outsourcing & big data.

Read more at...

http://www.billingworld.com/articles/2014/03/telecom-big-data-analyst-report-explores-solution.aspx

While various Analytics softwares help you to track & get basic traffic and visitor data, they also have few yet hazardously significant holes. There are many reasons for getting irrelevant, non required and Null/missing data. Some examples of the areas from which you might be getting conflicting data are Internal Traffic, Direct Traffic, Mobile Traffic, Private Browsing Traffic, Keyword Traffic, Social Media etc.

To know more, visit the following link:

http://blog.kissmetrics.com/what-analytics-is-hiding/

 “The future is like a corridor into which we can see only by the light coming from behind”, well said by Edward Weyer Jr..  Like that, to analyze anything, the first step is to collect relevant data and develop predictive score for each element, the second step is to assess these predictive score and then implement it into a model. Finally prediction and conclusion can be made accordingly.  The book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel explains how predictive and other insights associated with predictive analysis work.

Please go through the link for more details:

http://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853

An article by Mr. George Shen, information strategist and consultant with Deloitte Consulting, explains how analytics has become an inseparable part of any election process nowadays.

For More Information please visit

http://www.analytics-magazine.org/january-february-2013/731-big-data-analytics-and-elections

Stuart Rose, global insurance marketing manager at Cary, talks about how Insurance companies gather piles of text based data everyday about customers’ detail. It also addresses the different patterns used to explore unstructured information and analyze them. Text Analytics helps in understanding the details contained in the unstructured sources. The areas where text analytics can positively impact on insurer profitability are – Subrogation, Fraud Detection and Brand Reputation.

For more information please follow

http://www.insurancetech.com/business-intelligence/3-insurance-business-applications-for-te/240165269

Insurance organisations face problems in storing information. With the increase of information, innovations are required to handle information to detect risk properly. With the help of analytics in distribution channel, market products have improved. It also helps in taking decisions and reduces customer retention.

For more information, please go through: http://www.youtube.com/watch?v=KN-KgCgZdTE

The main threat of any insurance organisation is to detect fraud. To prevent fraud effectively, SAS has taken a Hybrid Approach, which enables detection of fraud 10times more effectively. The technologies in this Hybrid Approach are: Business rules, Anomaly Detection, Predictive Models and Social Network Analysis.

For more information, please go through: http://www.youtube.com/watch?v=HbJmJ_jK7gU

Susanne Sclafane, senior editor at Wells Media Group, talks about the extensive use of predictive analytics in insurance sector. The most prevailing uses of predictive modelling are rating, underwriting, marketing and forecasting. It also conveys some of the threats faced by the insurance industry.

For more information follow http://www.insurancejournal.com/news/national/2013/11/07/310601.htm