SigmaWay Blog

Nowadays, Marketers are re-examining the role of TV advertisement in digital age for making budget modification, as very few are tuning in TV to watch your ads. The number of Youtube viewers are increasing marketers are blending it and new viewing trends to learn new ways of drawing customers. The first driver of Youtube visitors is it  variety of content such are mash-up of memorable events, movies trailers etc. This has lead many personalities to entertain online which are rather becoming more famous among viewers according to a survey. Secondly, Youtube is gaining respect in media industry as it garners credibility to views as reach metrics Third drivers has to be the cord- cutting movement as people are purchasing digital programs instead of cable or satellite which offers them the flexibility of when and what to watch along with engaging in their other important business simultaneously. Moreover, these viewers preference offers analytics opportunities to understand the changing trends in peoples preferences. Read more at :- http://www.cmswire.com/digital-experience/are-you-really-reaching-customers-with-tv-ads/

Large companies are collecting big data every minute. The need for new data base analyst and system programmers is increasing having specialised skills and in depth knowledge. Their roles include 1) management of databases and their recovery readily measured and monitored against disaster. 2) Implementation of self-realising and self-tuning processes. 3)SQL and database performance tuning. 4) Database logs and error management. Many of these roles are unnecessary due to high speed of big data analyst solution. Similarly, database performance tuning has become redundant as it lacks internal performance tuning resulting of no need of specialist in new enterprise environment. The task of specialist involves, 1) System and network performance tuning along with software installation and migration. 2) They be the leading technical specialist for critical application assisting data architecture changes. 3) Set the performance benchmark for prospective vendor tools. This infers that less and less specialist will be required in the coming phases when the role of generalist can be easily done by supplementary staff. Read more at:  http://it.toolbox.com/blogs/database-administration/big-data-technologists-transition-to-customerfacing-roles-the-current-team-70675

Over last quarter-century, there has been a steady increase in the features available for different application of analytics industry. At one level all the features help organisation get the most value possible from their information assets. All different technologies are designed for different user types. Due to merging of needs of different user, if we want to leverage all the features, we typically need several servers and interfaces. SAP cloud analytics integrates all technologies into a fast blazing platform which we don’t even need to install. With SAP HANA, we can easily cleanse, integrate combination of data sources and provide insightful budget forecasting. We can also analyse combination of structured and unstructured data and augment data exploration with sophisticated geographical mapping with the ultimate vision of Big Data Discovery. Based on SAP HANA’s HTAP capabilities, its all possible on real operational data with instant sophisticated of data for analysis. Read more at:- http://timoelliott.com/blog/2015/10/reinventing-the-analytics-experience-sideways.html

Surveys show that security concerns are primarily stopping user from using cloud solutions. In reality, the security provided by reputed cloud vendors is much more secure than home grown solutions as seen in Sony's email hack and target credit card breach cases. Cloud solutions are cheaper, adds flexibility, providing tremendous computation capacities to users in just second without even installing software. It updates continuously, where we can store personal data with greater security because the cloud service provider know better protection than a customer. People are reluctant towards transferring all data from premise to cloud just for analysis because they don't understand how much of the data analyzed comes from internal system. Audience at the international conferences uses cloud analytics over Hybrid Transaction/Analytical Processing (HTAP) due to risk of disruption of jobs. The visible risks of cloud solutions are acceptable because of the rule that relying on outside services run by experts completely overwhelms the potential cost. Read more at:- http://timoelliott.com/blog/2015/10/whos-afraid-of-cloud-analytics.html

Big data analytics require skilled data scientists who are paid unreasonably high amount of money, because of their ability to ask right question and create the most effective algorithm in order to extract meaningful information from tons of data. But, not anymore. Researchers at MIT teamed had developed a machine of automated analytics that explores patters and designs in data structures. Read more at:- http://blogs.csc.com/2015/10/16/can-automated-analytics-reduce-need-for-data-scientists/

Baseball is an exclusive game. It has been played for over centuries and hence is defined by the long history of data collected over the years which has hardly changed. We can relate baseball and big data. Baseball combines analysis of historic structured data with real-time event data and sequential insights. The following three factors have a direct analogy in consumer marketing: 1. in baseball, a manager look at batting averages, spray charts, and righty-lefty matchups whereas a marketer might look into historic purchases, create segmentation of behaviors and demographics, and add channel preference.
2. In baseball, a manager might look into real time data e.g. how pitch is affected, player’s approach. On the other hand the marketer looks into how he can capitalize on real-time events.
3. For a pitcher, set up pitch is important On the other hand, in marketing, the flow of emails or offers dictates the next offer or product promoted.
Read more at: http://www.tibco.com/blog/2015/08/10/baseball-and-big-data-marketing-closer-than-you-ever-thought/

The relationship between structured and unstructured data in real time can be analyzed by turning the structured and unstructured data (wide data) into fast data. Origin of a problem can be found by wide data. Quicker decision and analysis can be made by fast data. With the boom of Internet of Things (IoT), new challenges relating to the structured and unstructured data may arise. By turning wide data into fast data we will be able to retrieve massive amounts of data from various sources and then use them to identify and potentially assess their behavior in real-time.
Unstructured data can also be used to meet real-time data demands. When wide data is turned to fast data it opens doors to various opportunities.
Read more at: http://www.tibco.com/blog/2015/07/17/big-datas-companion-wide-data/

Optimization has played an important role in corporate world enabling executives and managers in making best decisions. Using optimization technology and techniques has made day to day business issues easier. Optimization came into play since 20th century with the help of digital computers. Linear programming was the first optimization method used. It enabled analysts to achieve best possible outcome in a given situation. Computing capabilities became affordable enabling companies to use more complex algorithms to handle more ever difficult optimization problem. Optimization analytics is popular among businesses giving them potential to improve performance and create competitive advantage.  Read more at:http://www.smartdatacollective.com/robert-kugel/336864/optimization-analytics-comes-mass-market

Need for Chief Data Officer (CDO) in boardrooms has increased now days. Importance of CDO is same as that of big data in companies, as a person in charge of data team. There has been increase in number of companies who are employing CDOs. At present 43% of executives reported that their firm had appointed CDO, while two years back it was only about 19%. During financial crises in 2008 many companies lost their significant amount of money after their data had been shown completely wrong. That gives rise to Chief Data Officer, a person that can be held responsible for validity of data. He can protect businesses from loss due to crises and also to help appease law makers who were going to require change within reporting and storage of data.  Read more at:

https://channels.theinnovationenterprise.com/articles/the-evolution-in-the-cdo-role

People are increasingly using mobile devices to interact through mobile browsers and apps. As a result mobile analytics is occupying an important position in the analytics world. Jeremy Levy, CEO of Indicative and previously co-founder of Xtify and Meetmoi, in a special guest feature in Inside Big data talks about how mobile analytics is changing and how to navigate in the changed scenario. He believes that the changes in the field of mobile analytics are driven primarily by three factors- access to attribution data, deeplinking and new platforms for some of the largest players in mobile. Again there are 3 factors that will define the success in the changed scenario- 

• Clean attribution

• Tracking deeplinks properly 

• Finally, flexible analytics tools

For more on this hit the link http://insidebigdata.com/2015/07/17/thriving-in-the-new-mobile-analytics-ecosystem/

Big Data appears to be the solution of every problem, especially when it comes to IT. But, one thing we often forget is that the big repository of data is useful only when one is able to generate actionable insights from it. Otherwise, it’s just another stack of data. With organizations becoming increasingly dependent on technology for every aspect of business operations, IT enterprises are finding themselves locked with big data with no useful insights. Raja Mukerji, president of ExtraHop, in an article in Inside Big data talks about IT operation analytics as the most viable solution to this problem and how it has emerged as a framework for a horizontal approach to Big Data in IT. He believes that ITOA will help release useful insights form IT Big data and the horizontal, silo buster approach will transform IT from a mere support system into a strategic business driver. For more on this piece follow the link http://insidebigdata.com/2015/07/18/taking-a-horizontal-approach-to-big-data-for-better-it-and-business-outcomes/

They are often first point of contact with the customer, and a happy encounter makes a happy customer. Taking care of experienced call center agents and making employee satisfaction a priority produces numerous benefits for the company. ? Below is a list of a few things that you should be doing to take care of those employees who take care of your customers. • Excellent salary. Even in a very competitive industry, experienced agents can command high salaries befitting their expertise. They can get salaries that are higher than the average day job. Aside from their base salaries, these agents also deserve additional benefits and overtime pay for their hard work

• Incentives and commissions.  Aside from their salaries, agents also get additional money for reaching sales quotas or other milestones. 

• Awesome working environment. As a reward for their hard work, call center agents get all the pampering they can get through excellent workplace facilities. This is why most call centers provide their employees the works – immaculate bathrooms, cozy sleeping quarters, play areas, clean pantries with free coffee and so on.

• Career growth opportunities. If you’re like most agents, chances are you won’t be satisfied with being an agent for the rest of your working life. You will look for opportunities that can push you up the corporate ladder. A good call center provides its agents the chance to add more skills to their resumes so they can become eligible for higher positions in the company. A call center that takes care of its people’s career advancement produces agents who are not only successful with their jobs but also those who are actually happy staying in the organization.

Big data - which admittedly means many things to many people - is no longer confined to the realm of technology. Today, it is important in any business. In addition to providing solutions to insurance companies' long-standing business challenges, big data solutions offer the power to transform processes, organizations and entire industries. Big data is especially promising and differentiating for insurance companies. With no physical products to manufacture, data is arguably one of their most important assets. Financial, actuarial, claims, risk, consumer, producer/wholesaler and many other types of data form the basis for every decision an insurer makes.

Insurance companies will realize value by effectively managing and analyzing the rapidly increasing volume, velocity and variety of new and existing data. By putting the right skills and tools in place to better understand their operations, customers and new markets, insurance organizations will be on the right track to compete and thrive in this global, dynamic marketplace.

Read more here- http://analyticsindiamag.com/has-the-insurance-industry-mastered-the-art-of-utilising-big-data/

Big data and internet of thing (IOT) have made it easier for companies to collect new data. This will help companies to have comprehensive view of consumer’s buying and viewing behavior which in turn is used to make competitive business intelligence. Today businesses are struggling to face high bills of Internet Service Providers (ISP) as they already have too much data to cope with. With huge investment, industries have spent time in finding solutions for crunching large data within limited time. There is huge cost involved in storing big data. But Software Defined Storage (SDS) gave storage the operational efficiency as other big data aspects. This provides flexible and scalable architecture to empower the business to meet big data fluctuation demand.  Read more at:https://channels.theinnovationenterprise.com/articles/big-data-and-the-communications-data-bill

Big data and financial services have played their role together to boost the company's profits. Big data is providing advantages to financial services in three ways. Firstly, big data is keeping financial companies ahead to develop more creative and innovative ways of using big data to predict future, which companies are going to earn profits and which type products will be demanded in future. Nowadays, financial companies can easily monitor and respond to changes that exist in financial markets. Secondly, banks are also using big data to become more customer focused. This can be done by tapping the reams of unstructured data like lifestyle information, social media activity, and customer feedback and support requests. Thirdly, big data can be used in fraud detection. Read more at:http://www.smartdatacollective.com/bernardmarr/335942/3-ways-big-data-changing-financial-institutions-forever

It is not easy to understand what a dataset represents and how to use that information. One can understand the data by interacting people who know about data and through other alternative means. Usually the problem of finding and understanding the data exists in many companies which leverages data to enhance their decision making and their products. Many technologies relating to data made it easier to process massive amount of data and visualizing results in a better way. But the absence of focus on empowering users to find and understand data has made it difficult for reliability and efficiency. It has been more concrete to measure and show progress of efficiency and visualization capabilities of the process. Read more at:http://www.smartdatacollective.com/venky-ganti/332052/data-time-investment-finding-and-understanding-data

Marketing is heading towards Big Data and analytics. Marketers are experiencing increased complexity in both their departments as well as daily pursuits. Data complexity is increased with campaigns on social media combined with other data sources from sales and finance. So companies are now resorting to marketing analytics for data aggregation, analysis and making data-driven decisions. Before going ahead with marketing analytics, companies should identify their key objectives based on a definite analytics platform which is divided into milestones in sync with the existing framework of the company. Once this is done, one can start the analytics journey and hold key stakeholders responsible for results all of which are based on the company’s goals and vision. Data techniques to be used should be decided solely on the basis of the company’s objective. It is important to focus on the correct segment of data within the entire data set. This way one can increase the chance of gaining actionable insights on data and thus pushing the company towards growth. Read more at:https://channels.theinnovationenterprise.com/articles/marketing-analytics-do-you-have-a-framework

Over a decade ago, High frequency trading (HFT) used to be part of very few financial firms, but now it is an integral part of every major financial firm and is key to drive the success of these firms. Many industry experts in the field are of an opinion that big data has started entering into the financial sector at a minute level for now and it will follow the same trend like the HFT in expanding into ever major company. It will be the major decisive factor in taking many calls in near future. On the technical level, many experts feel Machine Learning (ML) will take a dominating role in areas where Statistical techniques are now used for finance and risk management. ML with its ever increasing algorithms/techniques is an ideal replacement to humans in trading scenario, though it has its own caveats. It is seen that ML and Big data is going to lead a new revolution into the field of Finance. To read more: 

http://www.automatedtrader.net/headlines/153852/gdtrm15-machine-learning-is-the-new-c%2B%2B

Today data and analytics wave has replaced the HR and people management. Human resources are one of the most data rich functions in most business. Earlier the data were mostly ignored or just kept in tables and charts but now data analysis has used such data and helped predicting when will employees leave, from where they can grab most suitable candidates and how to identify and retain most talented employees. HR departments are now crunching their data and easily taking decisions for recruitment. Earlier it was not so easy for HR's to look around the globe for hiring but now they can do just by sitting in a room, analyzing data without looking at the globe. Read more at:http://www.smartdatacollective.com/bernardmarr/334941/how-hr-can-use-big-data-smart-way-hint-most-are-not

In the gambling industry, the house with most information wins the game. And it’s no wonder that Big Data is changing the world of gambling. Betting firms are now making extensive use of Big Data and analytics to manage their business and stay at the apex of the game. Big Data services empower gamblers by giving them more information and helping them to plan strategies more effectively. Big Data is also transforming sports gambling. With lots of data to collect and analyze, sports organizations are using it to study players and their tactics. Gamblers are now using Big Data as a way to get the odds in their favor and this has led to the growing popularity of fantasy sports betting. Gamblers are not only using Big Data for their games but also to improve their marketing efforts for example the casinos are analyzing customer data to develop personalized marketing campaign. Read more at:https://channels.theinnovationenterprise.com/articles/how-big-data-is-changing-the-gambling-world