SigmaWay Blog

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

A recent webinar by INDUSTRYWEEK explains the different uses of predictive analysis in auto industry and how it is changing the competitive atmosphere.

For More Information :  http://www.industryweek.com/webinar/predictive-analytics-transforming

The biggest challenge for any insurance company is  to capture, store, search, share, analyze and visualize big data. Moreover, handling these data is also very challenging for the insurers. Étienne Castonguay, partner and co-founder of InEdge, suggests that Predictive Modelling in insurance can help them in solving the problems.

For more information, please go through: http://tdwi.org/Articles/2013/02/26/Insurance-Analytics-Trends.aspx?Page=1

To find new opportunities and minimize current problems, it is important for an organization to collect and use big data efficiently. On a day to day basis, Excel is a common tool. But when it comes to analyze huge data, sometimes Excel becomes inefficient. The probability of errors are always there in spreadsheet, especially when data set is more complex. A blog by Renat Stewart, Banking Officer at M&T Bank, talks about how to utilize Excel spreadsheet in an efficient manner.

Please follow this link : http://www.riverlogic.com/2013/05/data-complexities-prove-excel-spreadsheets-difficult-to-maintain/

Machines are important in any manufacturing industry. As wear and tear of the machines are obvious, a preventive maintenance program is vital to i) reduce machine downtime, ii) get the overall view of the health of the machine and iii) estimate the cost of the maintenance.

The preventive maintenance can be done with the help of a Predictive Maintenance program, also known as PdM which includes the techniques of Predictive Analytics. Different sensors installed in the machine provide inputs to the Analytical Software from which the degradation process can be understood. This will help in predicting different failure modes of the machine.

To know its benefits and how PdM improves machine & equipment maintenance, please go to http://www.lselectric.com/4-ways-predictive-analytics-improves-equipment-maintenance/

The box of treasure that analytics promises, contains the ability to fully understand customer behavior, derive actionable insights, and further tailor customer experiences to meet the needs of any segment. It is estimated  that big data will grow from 12 billion dollar industry to 53 billion dollar industry by 2017. However, with various types of investments and expectations that exist across many industries; it will not be easy to achieve the capabilities needed to derive deep insights and decisions making mechanism. Many firms still struggle with capturing basic information, collecting external customer information, social media information, and measuring wallet share. However, based on a recent Forbes survey, 63% of the firms reported a transformation impact on their business, by an integrated approach to analytics, where predictive modelling, customer analytics, and big data are working together using cloud services. The investments are starting to pay off, which indicates there may really be a pot of gold that awaits those who pursue the analytics path.

To read more, visit the following link:

http://www.business2community.com/business-intelligence/reaching-pot-gold-investments-analytics-paying-0801572#ssAKHfblgoitLWGX.99

A presentation by Geoff Watts, Chief Executive Officer  & Co-Founder of EDITD, where he speaks about how big data is helping the fashion industry. As apparel brands are going global, huge amount of retail and consumer data are generated .To tap these data, the fashion industry is taking the help of different analytical tools to get a complete picture of the industry during different time periods and take a decision.

For More Information :- 

A blog by Carlos Centurion, President, River Logic Solutions, talks about the third phase of analytics i.e. prescriptive analytics. Prescriptive analytics helps not only to anticipate what will happen and when will it happen, but also why will it happen.

Healthcare providers are using various tools of prescriptive analytics to enhance their performance and efficiency.  It also helps the provider to take better decisions. To know more about Prescriptive Analytics in Healthcare Provider Industry, please follow this link:

http://www.riverlogic.com/2014/02/prescriptive-analytics-healthcare-provider-industry/

Imagine, in this IT enabled world, different departments of a bank are operating on a standalone framework, managing millions of customer data. So it’s obvious that they will not have a clear picture about customers’ demographic pattern, their preferences and their account usage patterns.

Thanks to Analytics, these problems are now being solved. Omar Sohail, Principal, Deloitte Consulting LLP and Banking & Securities, Business Analytics & Information Management Leader, writes how structured, unstructured and semi-structured data can help the banks in reaping benefits by getting more insights about customer data.

For detailed information please go to http://www.baselinemag.com/analytics-big-data/banking-on-big-data-and-analytics.html .

Banks are the backbone of every economy. With the help of data analytics, banks are defining the modern age of customer retention & services through out the world. However, the Indian BFSI sector is yet to implement data analytics in their day-to-day operations. Mohan Jayaraman, Experian Credit Information Company of India, explains how Indian banks can improve their customer lifecycle by using data analytics. To read about his views visit:

http://www.informationweek.in/informationweek/perspective/287791/indian-banks-improve-customer-lifecycle-analytics

A report by McKinsey & Company talks about how usage of data analytics is transforming organisations. It also conveys that information on niche segments can also be important.

For more information follow

http://www.forbes.com/sites/mckinsey/2014/01/15/new-ways-for-turning-data-into-dollars-now/