Predictive analysis helps in analyzing the choice and behavior of the viewers. This in turn helps in finding the target audience as well as optimizes revenue. Combination of system dynamics and agent based modeling can be used to predict the profits from the shows or movies. Proper techniques along with real time analysis can assess the content precisely and accurately.
Social media analytics or sentiment analytics allows the industry to understand the perception of the audience and the critical reviews that they give.
“Bag-of-words opinion analysis” approach can help in evaluating the tweets from various social media sites. Cost efficiencies can also be achieved with proper efficient use of data analytics.
Read more at: http://sites.tcs.com/blogs/agile-business/analytics-entertainment-industry

Location analysis is a very important part of Big Data classification in automobile segment. Big data analysis is used in every stage of production and marketing of automobile industry.
Product Design – Data is collected from customers who would help them to make newer improved models.
Supply Chain – With the help of Big data analysis manufacturers can now analyze different suppliers based on various aspects and reach a decision.
Marketing – Helps in identification of the right kind of customers.
Aftermarket – Helps In improving logistic and sourcing processes.
Financing – It enhances the understanding regarding the market of each model.
Read more at: http://www.smartdatacollective.com/anandsmartdata/325598/big-data-automobiles-use-cases-today-and-opportunities-future

With the advent of Internet of Things (IoT), businesses can now avail additional ways of communicating with their customers. Sensors can be embedded in household appliances allowing consumers to control those which simplify household chores. Doctors nowadays can use apps to link patient statistics and provide personalized advice. Thus healthcare market makes ample use of IoT to provide better services. Security concern should be kept in mind as organizations are now deploying IoT in their enterprise. Organizations should also encourage application developers to maintain a certain standard to prevent poor codes from being deployed. Cloud technology, big data and analytics, mobile computing and social networking along with IoT makes a complete package. Read more at: https://channels.theinnovationenterprise.com/articles/deploying-iot-in-the-enterprise

Through predictive algorithms, text analytics in Big Data can discover a wide range of happening things about a business. On the positive side, text analytics can be fed a lot of unstructured text. Customer experience management, brand monitoring, compliance and brand management can be done with the help of text analytics. This was of great use during the US election as it found out reasons behind interested voters.  Though on the negative side, text analytics tool get confused for words which has a dual meaning in the same culture or for different cultures. Hence, it should be made such that it can stand up successful for different sentiments too which many are trying to establish now. Read more about this article by Elliot Pannaman (International Events Director) at: https://channels.theinnovationenterprise.com/articles/maximizing-business-performance-with-text-analytics

The Great Recession was the general economic decline observed in world markets around the end of the first decade of the 21st century. The exact scale and timing of the recession is debated and varied from country to country. The most probable cause behind this recession is the use of bad data. Generally, retailers lose their revenue because of out-of-stock issues which is due to the use of incorrect and outdated data. Hence bad data is destructive. It results in losses because of dwindling customer satisfaction and incurs additional expenses in order to correct the faulty data. Big data can affect both small and large enterprises but companies can be immunized against bad data by being active and using the correct tools.

Read more at: http://www.business2community.com/big-data/bad-data-a-21st-century-business-epidemic-01252370

In modern world, analytics plays a significant role and therefore various organizations develop cohesive analytics for customer satisfaction and thereby leading to enhancement of sales. According to some retailers, customer experience drives revenue while analytics focuses on an organization's commitment in terms of value to the customers.

 Audi representative Aaron Cohen (the company's general manager of strategy and analytics) explained the process of optimizing customer experience and hence the importance of data blending. Companies are shifting their focus to freemium services, open source platforms for valuable insights. In this context, Monobina Saha (senior data engineer at Sears) highlighted the significance of R-programming among data scientists.

Read more at: http://www.cmswire.com/analytics/how-are-you-capitalizing-on-customer-analytics/

Just like other areas of economy, marketing, too, has been impacted significantly by the growth of data analytics. Although the importance of analytics has been recognized in marketing, yet it is observed that in most cases the tools are not being used efficiently. For this, aligning data with the goals of the company needs to be done. Secondly, the smooth transition from big data to useful data and determination of the relevant metrics and adoption of those metrics are required. And last but not the least, hiring of the right people with analytical and creative minds to properly use the data is imperative. Read more: http://www.forbes.com/sites/johnrampton/2015/06/21/how-can-cmos-make-sense-of-big-data/

Industries are growing techno-savvy and understanding that being able to gather, identify, and comprehend patterns in data is critical for their long-term success. We are finally entering an age of data-powered farming that work on open data sets of weather and yields, IoT, drones and smart agricultural machinery. Such digital tools are becoming as important as traditional tools in the agricultural sector. IoT sensors are also helping companies predict and manage risk for automobile policies in real-time. Governments and industry are cooperating to share data on natural catastrophes in the hope to predict them. Drones are even being used to inspect and manage energy sites like turbines and rigs. Governments too will start using Big Data for evidence-based policy making. Read more at: http://yourstory.com/2015/06/big-data-unleashing-value-for-startups/

Healthcare Analytics Market has been predicted to have tremendous potential over time with low cost and improved population health. Survey report gives an increase in analytics adoption rate though many still don't use predictive modelling techniques. Data integration is required to derive analytical value which comes as a big challenge. Data Breeches and Data Security is a lesser factor than the former one since enterprises take their own data. But this factor works when external data is taken into account. People with a unique combination of statistics training, technology skills, and data knowledge are also required in large number. Also, infrastructure improvement for analytics must be done according as many healthcare decision makers. Thus analytics is getting much priority in healthcare in recent times. Read more about this article at:  http://www.cio.com/article/2904270/healthcare/healthcare-analytics-4-things-impacting-the-adoption-rate.html

Business decisions in modern times are being taken on the basis of appropriate data analysis and projections, which are generated by crunching the data. But business leaders need to stop and think once about the quality of the data; the most important factor because all the forecasts and predictions are done in the light of the data. So here are some points which should be kept in mind before starting data processing. They are: • The source of the data, i.e. survey/ public domain data etc. should be known correctly.

• Purpose of data collection: objective of data collection should be clear.

• Data collection method should not have any sampling bias.

• The nature and scope of the data should be clear. Read more at: Business decisions in modern times are being taken on the basis of appropriate data analysis and projections, which are generated by crunching the data. But business leaders need to stop and think once about the quality of the data; the most important factor because all the forecasts and predictions are done in the light of the data. So here are some points which should be kept in mind before starting data processing. They are: • The source of the data, i.e. survey/ public domain data etc. should be known correctly.

• Purpose of data collection: objective of data collection should be clear.

• Data collection method should not have any sampling bias.

• The nature and scope of the data should be clear. Read more at:

http://www.huffingtonpost.com/2015/05/27/simple-questions-about-data_n_7453668.html?ir=India&adsSiteOverride=in

Data analytics is no longer limited to the field of business and market research but it has spread across the financial sector as well. Stock market investors are using data driven insights and predictions to make crucial investment decisions. By observing the market conditions and various metrics related to it, the data scientists are being able to come up with accurate inferences about the market, and the data analysis is becoming increasingly precise and data intensive.  This is due to the availability of financial data and decreasing cost of information technology. The growth of analytics in this sector is likely to continue and is going to revolutionize the way financial trading is done. To know more read: http://www.huffingtonpost.com/irene-aldridge/why-big-data-matters-in-f_b_7553438.html?ir=India&adsSiteOverride=in

We know how big data has helped organizations achieve big success in almost every field. Today, when everyone is in a race to establish a competitive edge over the other, businesses want their data and analytics to be real time or near real time so that they can act quickly. Mary Shacklett, president of Transworld Data, elaborates for us four ways real time analytics lead to competitive advantages. They are:

• Buying Preferences

• Network Intrusion detection and forensics

• Logistics Orchestrations

• Predictive maintenance

To know what these systems had in common follow the link http://www.techrepublic.com/article/4-ways-real-time-analytics-lead-to-competitive-advantages/

The healthcare sector is now exposed to data breaches as it has adopted the online mechanism, for maintain health records or say for remote patient monitoring. Data breaches are not only expensive in terms of costs incurred by the healthcare provider but also in terms of patients' lives. Data analytics helps in interpreting unstructured data, which may help to prevent not only data breaches but also (say, medical prescription) frauds.  Big data analytics allows organizations to amalgamate and analyze their data irrespective of the source, type, size or format and finally understand the patterns needed to address fraudulent activities. Again, by updating, pharmacies, doctors, and hospitals on data insights, concerning substance history information of patients, it is possible to take better prescribing decisions and identify potential prescription drug abuse. Read more at: http://venturebeat.com/2015/03/31/big-data-analytics-can-prevent-health-care-fraud-heres-how/

Sigmaway consultants have worked with clients in benefits solutions workspace providing analytics on healthcare plans. For more details visit http://www.sigmaway.us/

Smartphones are fantastic examples of devices whose success largely depends on the performance it delivers. Big data researchers are working towards developing the network, computing and cloud architecture required to sustain their projects. The sheer volume of data generated by us doubles every two years and there is an urgency to make storage devices manageable and affordable. SSA (Solid State Array) at the hardware level and SDS (Software-Defined Storage] at the software level are becoming quickly affordable. With the evolution of in-device memory, a variety of data will now come into light that may revolutionize the applications of Big Data in the mobile industry. In-memory databases will allow real-time analytics and awareness of the ‘live’ transaction data – rather than delayed ‘fact-based’ analysis of old data. Read more at: http://www.forbes.com/sites/howardbaldwin/2015/06/22/flash-ways-to-make-big-data-run-faster/

The future of analytics seems to be brighter with IoT data, it's near real speed analysis and complex event processing systems. But, where exactly is IoT analytics going. Michael Hummel, co-founder and CTO of ParStream, talks future of IoT analytics. Daniel Gutierrez has summarized his talks in an article on Inside BigData highlighting the 4 predictions Michael made:

• Big data, fast data and more analytics

• Horizontal integration and vertical application 

• Decentralization

• Integration of advanced analytics and Machine learning

For more on this piece, follow the link http://insidebigdata.com/2015/05/28/and-this-is-the-future-of-iot-analytics/

Planning before working out is the most basic idea. A planner successfully merges both aesthetics and practical planning. But a strong design comes from the collected data. Combining user and sales data, Ford F-150 has been designed to reduce weight and increase sustainability which are made keeping in mind both environmental and business sense. Though A/B testing was a popular method of creating effective marketing choices due to its manufacturing constraints, companies nowadays use data gathered by observing public interest to create new products or make changes to earlier products. With increasingly complex algorithms, architectural design is now getting more accurate. With faster computing power, various tests can be done to establish strength and quality. Read more about this article by Chris Towers (Big Data Divisional Head) at:  https://channels.theinnovationenterprise.com/articles/designed-by-data

Rapidly changing consumer demands are becoming one of the major concerns of the financial institutions. Analytics is the technology which can be adopted to address this need. With evolving customer expectations, analytics solutions, too, have adapted, but the question under consideration is whether the banks are prepared to adopt and benefit from the next-generation analytics solution. As viewed by Thomas H. Davenport, evolution of analytics is a three-stage process of which Analytics version 3 is the latest generation which merges qualities of previous technologies and focuses on prescriptive analytics. Based on the data collected, Analytics 3.0 will enable the financial institutions can create tailor made services for customers. However, to fully reap the benefits, the institutions have to make sure that the right technologies and practices are in place. Read More at: http://internationalbanker.com/banking/are-banks-ready-for-next-generation-analytics/

Responsible for the largest and most complicated science experiment, the LHC, CERN has tried to uncover the secrets of the universe. Its major experiments are all in the domain of Big Data. How? Well the LHC single handed generates 30 Petabytes yearly. They use effective data warehousing and Big Data Analytics tools to make sense of such large amounts of raw data. Crunching all the data collected from the 600 million particle collisions per second requires enormous processing power. Distributed computing gives them access to enough processing power and storage capacity otherwise far too costly to build into one data center. The data can be accessed at greater speed by researchers wherever they are in the world now. Read more at:https://www.linkedin.com/pulse/big-data-uncovering-secrets-our-universe-cern-bernard-marr

The film Moneyball based on a 2003 book, talks about the Oakland Athletics baseball team whose focus is on the team's analytical, evidence-based approach to improve the baseball team despite its poor revenue system. This film made the plot for professional sport associations to spend big on analytics. Analyzing data points captured by video cameras around the stadium uncover patterns that coaches and players might miss. Data can also be collected by wearable devices to help prevent injury by measuring its likelihood. So, companies need to follow get up and move up to the major leagues of Big Data. But predictive analytics can decrease efficiency as supporting analytics platform records, analyzes and visualizes trends. Read more about this article at: http://www.tibco.com/blog/2015/05/26/how-big-data-analytics-are-transforming-sports/

With increasing number of data breaches, companies should now take necessary steps to combat it. Phishing, though a fairly old method, is the most common method of attacking companies. Emails which contain phishing methods should be identified, filtered and prevented from hitting inboxes. Spam filters are already doing this task to a great extent, but creating flagging system that can filter out the necessary emails through human logging works better. Systems should be upgraded to prevent from being vulnerable to malware and malicious cyber-attacks. Misuse of system and human error should be prevented to prevent hacking. The most important way of protecting system from attacks is by educating people who use it in the best ways to protect it. Read more at: https://channels.theinnovationenterprise.com/articles/data-breaches-aren-t-changing-but-you-should