According to Vincent Weafer (Senior Vice President of Intel Security), analytical capabilities will help you to be ahead of your competitors. Predictive Analytics help you to analyze the future trends more accurately and help you to realize your threats and opportunities which affects budget, purchase and staffing decisions. For prediction, a large amount of data is required from a range of activities which organizations perform, historical events and third party intelligence and to make predictive analytics more effective, you need to build foundational abilities like real-time hunting, prioritization and scoping of security incidents in their environment. You need to analyze your stakeholders for blocking decisions. Read more at: http://www.darkreading.com/partner-perspectives/intel/predictive-analytics-the-future-is-now/a/d-id/1319956

 According to Dave Blanchard (Industryweek), customer’s demands for lower delivered costs are seen to be a big challenge. But, by using predictive analysis, we can identify patterns and predict future events. An organization using predictive analysis can make better decisions. George Prest (CEO of MHI) says that companies that continue to use traditional supply chain models will struggle in the future.  According to the MHI/Deloitte study, it was found that less than 25% of companies have adopted predictive analytics though that number is expected to climb to 70% over the next three to five years.

 Read more at: http://www.industryweek.com/supply-chain/predictive-analytics-let-manufacturers-see-more-clearly-their-supply-chains

In this customer focused world, supply chain is no longer an internally visible process. The focus has now shifted to knowing where the supply chain needs to be. Predictive Analytics helps answer this question. According to Bill Abernathy, head of North America product supply logistics excellence, Bayer CropScience, “Predictive analytics are changing consumer buying behavior and supply chain professionals need to be able to satisfy the increasing demands of consumers who expect products delivered exactly when promised.” With the help of predictive analytics, manufactures are now able to make better decisions that are based on anticipating present and future customer preferences. Dave Blanchard, Senior Editor, IndustryWeek, has written in his article link about how predictive analytics is becoming a necessity for manufacturers. 

Read more at: http://www.industryweek.com/supply-chain/predictive-analytics-let-manufacturers-see-more-clearly-their-supply-chains 

Many companies have found some ways to generate business results through collaboration. In a recent webinar, structured approach to social business was explored. But what is structured collaboration. Jim Lundy (CEO and lead analyst for Aragon Research), explains it as an association of existing social techniques with product management technologies augmented by predictive analytics. He also says that "Collaboration isn't new, but teams that move fast are going to do more collaboration. They're going to do it with more people. And they're going to get to those outcomes faster."  To know more, follow: http://www.cmswire.com/cms/social-business/the-next-phase-of-collaboration-getting-the-job-done-026898.php

According to Jayakanthan Chidambaram (Associate Technical Architect, Aspire Systems), sustaining in the market with a persistent growth is considered to be one of the greatest challenges in any industry. Companies are focusing their investments on business intelligence technologies in order to predict behavior and consequences from patterns found in large volumes of data. Predictive analytics transforms data into important and useful information. Data analysis can be broadly categorized into 4 types - Descriptive, Simple Statistical Summations, Prescriptive and Predictive. Personalization based on location, behavior and preferences are driving how organizations should cater to customer needs and grow their business. When organizations adopted Business Intelligence, they took the first step towards understanding what is currently happening in their business. Now is the time to take that maturity to the next level where the need is not only to know what is happening but also be able to predict what is about to happen. Read more at: http://www.informationweek.in/informationweek/perspective/298003/predictive-analytics-future-business-intelligence?utm_source=referrence_article

Big data generated by drones is useful in every sector including monitoring data of animal cruelty on farms and surveillance data from military drones. The drones' usage needs a revolution in big data cloud services. However, flying a drone and taking pictures is the first step in data collection process. Since software to reason directly from video feeds is still in a research phase, drone data handling needs to be improved. The use of a cloud-based in-memory computing platform can enhance analytics, processes, and predictive capabilities. Amazon recently proposed to increase sales and revenue by providing the delivery of food using drones. By gathering data on a large scale, service providers will be able to process unique levels of details and turn it into usable information. To know more, go through Abhishek Sharma (author of InfoQ)'s article: http://www.infoq.com/news/2014/09/drone-data-big-data-analytics

There is no privacy in the era of big data. Personal data is collected and traded and there are few ways to control it. "We're being monetized in essence. We are being mobilized as products with inducement of the services of we use such as Facebook and Twitter" says Rob Livingstone, a fellow of the University of Technology and the Head of a Business Advisory Firm. However, major problem that regulators are facing is - how they can regulate the collection, storage and trading of personal data on the internet, when all of these activities, and corporations, operate across multiple continents and jurisdictions. Read more at: http://analytics.theiegroup.com/article/53a4371c3723a8398400014e/Little-Privacy-In-The-Age-Of-Big-Data

20-30% of the business decisions really need the use of advanced techniques like predictive analytics.   70-80% of marketing decisions can be judiciously carried with simple analytics techniques. A CMO broadly expects 3 key outcomes for his business:

• Bring more “future” customers in the most cost-effective manner.

• Convert those who come to the door into customers.

• Maintain the current customers “buying.”

Predictive Analytics need advanced skills and constant maintenance. A product manager or an operations manager equipped with the right “Data to Decisions” framework and easy access to data can optimize 80% of their daily workflow on their own, without having to depend on little and costly analytics resources. For 20% of decisions, where the potential ROI justifies the use of advanced techniques, they can work with their analytics counterpart. In summary, a smart CMO knows that a marketing team equipped with a “Data to Decisions” framework and easy access to data without the company of a data science team would emerge much better than a marketing team lacking data skills supported by a large data science team.  Read more at: 

http://www.forbes.com/sites/piyankajain/2013/05/26/the-8020-rule-of-analytics-every-cmo-should-know/

Social media is an ever changing scenario where social media marketers are increasingly using predictive analysis to ensure longevity. Various brands are using predictive analysis technologies to trawl through social media chatter to identify upcoming trends. It will also ensure that your brand be one of the first few to take advantage of the trend and gain maximum exposure. Your social media campaigns will also be much more refined compared to those of brands that don’t use predictive analysis. With predictive analysis, your brand will be able to pick out the right news, items, etc. that could become the next big thing on social media landscape, giving you ample time to prepare. Now smart brands are realizing that predictive analysis can be used in social media marketing to understand what consumers are looking for. Predictive analysis tools ensure that brands understand consumer behavior on social media.

Read more about this article at:

http://www.simafore.com/blog/bid/205332/How-Predictive-Analytics-Can-Boost-Your-Social-Media-Campaigns

While some industries are beginning to see the transformational capacity of big data and predictive analytics, these methods haven't quite panned out for supply-chain managers. The reason is that the largest obstacles happen to be the cost of hiring experienced employees. Researchers Matthew Waller and Stanley Fawcett write in a paper that the convergence of predictive analytics and big data has the capacity to change the way in which supply-chains managers lead. The goal is to increase the understanding of how to utilize big data efficiently and develop a new breed of supply chain leaders that are experienced in using data and analytics judiciously. A recent Wall Street Journal article quoting a survey by The Economist points out that while most companies see the value in using predictive analytics and big data to eliminate increasingly complex issues within their supply chains, they still perceive the cost of deployment as too high.Read more at: 

http://sloanreview.mit.edu/article/are-predictive-analytics-transforming-your-supply-chain/

Over the past two years, one of the seminal issues regarding Big Data was storage, especially with respect to the exponential growth and size of unstructured data that did not fit into databases. Today, however, the competitive landscape is very different. Proper storage is merely a pre-condition to finding the real jewels in Big Data-turning data from massive streams into knowledge, and thereby actionable intelligence in real time as events unfold. The following five steps are imperative to master Big Data and drive business growth:

1. Infer, Infer, Infer- Inferences transform data into knowledge, which results in greater process transparency and improvements.

2. Empower a C-Level Data and Predictive Analytics Champion. - With big data analytics changing rapidly and straining information structures, corporations and governments need “executive horsepower” behind its data initiatives.

3. Assess And Modify Your Supply Chain In A Multidimensional Global Context. - Analysis of supply chain will ultimately include relationships with parties such as customers, manufacturer, etc. 

4. Give Your Data Time-Critical Situational Awareness. - Analytics help a business line identify potential points of improvement.

5.   Rely On a Core Platform That Creates Derivative Intelligence and Knowledge in Real Time -statistical inferences can turn data into actionable intelligence that supports reasoned decisions. Read more at: 

http://www.forbes.com/sites/benkerschberg/2014/01/03/five-steps-to-master-big-data-and-predictive-analytics-in-2014/

In the mid-1980s some of the big companies were trying to bring video telephone technology in the market but it was a big flop with the consumers. The market did not want video phones even though the technology existed. Today's banks have something at their disposal that the telecoms of the 1980s did not: big data and pervasive computing. The financial services industry is trying to create personalized banking so that it would use the right IT solutions and it would allow for robust predictive analytics- in order to use the banking features that will satisfy their customers and improve the bottom-line. The challenge is to understand how to have their data at their disposal into value. Stepping outside traditional platforms will help banks realize that they need to reevaluate self-service and customer engagement in this completely new environment. Banks need to make sure that they have a strategy around all self-service devices. Customers are ready to connect to banks over smart phones and tablets, from any location and at any time. For that to happen banks must use their customer feedbacks. Read more at:

http://www.informationweek.in/informationweek/news-analysis/297141/master-branch-online-platform-transformation

Big data contains consumer information including transaction data, demographic information, buying patterns, CRM data etc., collected across multiple platforms. The data gives an insight to the customers' preferences of support options, desired communication mode, future buying patterns, impactful promotions, etc. Big data provides better customer service in several ways. Using predictive analysis tools organizations can now predict a customers' next move also. Big data using these tools helps the organization to predict customers' present and future preferences, drive real-time decisions, increase customer retention and increase profitability. More than 77 percent marketers agree that individualized messages and offers are more effective than mass messages and offers, which can drive engagement, boost sales and increase conversions. Usage of Omni channel marketing strategy increases client retention rates and bring superior financial results. Using data to create a cross-platform customer engagement strategy ensures highest customer service. A multichannel shopping experience shapes a brand's story generating revenue and customer loyalty. In two years smartphone users will be more than basic phone users, mobile service increases at the rate of 7 percent annually. Thus best customer experience is delivered through mobile channels for high performance organizations. Unable to ignore the potentiality of social media big brands register tens of thousands of social media interactions every day. There are wide range of options available. Communication through online communities reduces call center costs. Read more at:
http://it.toolbox.com/blogs/insidecrm/5-ways-big-data-can-enhance-customer-service-62054

According to the Business Technology Innovation Research, analytics is the topmost priority. Three key core areas comprise 2014 analytics research agenda. The first consists of a definite focus on business analytics and methods like discovery and exploratory. The people and process aspects of the research include how governance and controls are being implemented along with these discoveries. The exploratory analytics space comprises business intelligence. Value indexes, mobile business intelligence and business intelligence will provide deep explanations and ranking of vendors in these categories. The area of second agenda is big data and predictive analytics. The first research on this topic will be released as benchmark research on big data analytics which explains vendors of software that helps organizations do real-time analytics against vast data. The third focus area includes information simplification and cloud-based business analytics including business intelligence. Thus, Analytics as a business discipline is getting more importance as we move forward in the 21st century. Read more at: 

http://tonycosentino.ventanaresearch.com/2014/01/23/business-analytics-in-2014-trends-and-possibilities/

Some strategies haven't succeeded dealing with supply-chain management. The reason is the cost of hiring expert workers. According to researchers the union of data science, predictive analytics and big data likely to alter the way in which supply chain managers lead and supply chains function. They named this as DPB. Companies have used datasets to plan ideas to meet customer demand. But now they combine external data to better estimate future risks .two points to judge analytic skills: 1) Data science and domain expertise are not mutually exclusive: Analytical skills are important for data scientists who focus on Supply Chain Management (SCM).2) that doesn't mean theory doesn't apply: Strong theoretical knowledge is essential in SCM. Use of suitable theory to build models before operating predictive analytics is key to justifying a circulation of false positives. The three links in supply chain: manufacturers, retailers, supply management, shipping management and human capital. Read more at: 

http://sloanreview.mit.edu/article/are-predictive-analytics-transforming-your-supply-chain/

While CRM applications officially gather terabytes of helpful client data for organizations, significant deeper insights are also en route because of a creating new pattern of predictive analytics capabilities being integrated into CRM. The huge draw is that organizations will have the capacity to utilize existing CRM information to tremendously enhance basic one-on-one associations with clients. An alternate key profit is that it will help organizations create extra deals when clients reach them by breaking down approaching client information progressively. 

It's the same thought with CRM that incorporates add-on or implicit predictive analytics when a potential client arrives at your company's Web webpage to make a purchase. In the event that a client is offered this item at this cost at this point, would they say they are likely to purchase it? One can make a targeted offer to a client focused around what they are looking for. The probability that they acknowledge that offer will figure out whether you can augment client maintenance, deals and benefits. 

As these sorts of predictive analytics features are presented, organizations will need to evaluate their methodologies to joining the right parts into their own particular foundations. That will take research, detailed inquiries and discussions with teams from marketing, IT and other departments, as well as market research and more. It's not something one will be able to jump into with little thought. One ought to know his objectives before he make the first strides so he can attain sufficient payback from his investments of time and resources. To read the full article visit: http://www.cio.com/article/2371968/customer-relationship-management/how-predictive-analytics-will-improve-crm.html

Most shopping websites, regardless as to whether they are auction based like eBay or are just one sprawling marketplace like Amazon, tend to prominently feature a list of recommended products on their homepages. These lists are the results of their product recommendation engine.

They work by taking into user preferences or your preferences for that matter and then correlate it with the products and services available on the site. Needless to say, product recommendation engines naturally enjoy access to the entire product and service database of the website. Information is filtered with your preferences in mind and, afterwards, the product search engine comes up with a list of products and services that it considers likely to appeal to you.

Predictions made by product recommendation engines are not only based on the description of the product and service but also on whatever information it can obtain from your own social environment and previous web history. It first gains access to a pool of users and collects data based on their behavior online, their activities, and their preferences. All the information collected is then filtered and submitted to a platform which categorizes them into products that a group of users may like or dislike. When you visit the site, the first thing it will do is to determine which group of users you belong to. From there, it will provide recommendations on the assumption that your tastes are similar to users it had studied in the past. To read more: http://www.aboutdm.com/2013/01/product-recommendation-by-amazon.html

Risk analytics is increasingly important for banks as they cope with a complex regulatory and competitive environment. Important technologies and calculation engines are now available that are critically important to the future of banks and the entire industry. At the same time, it is possible to develop an over-reliance on analytics, so a balance needs to be found.

Developing more comprehensive and integrated capabilities is increasingly important. Integrated stress-testing, for example, is an important means by which the science of risk management can be turned into more of an art, such that it can be communicated and appreciated by a wider audience. An effective stress-testing framework encompasses a wider spectrum of macro-economic, social, political and environmental considerations and forecasts and so can help banks avoid the tunnel vision that can prevent them from making good decisions and taking timely action.

Companies are investing in risk analytics and intend to increase those investments, yet the potential return is often stifled by inconsistent or incomplete data. This prevents organizations from generating the insights needed to support a more predictive approach to risk management. To read more: http://www.baselinemag.com/analytics-big-data/banking-on-big-data-and-analytics.html

Rapid Miner had an existing partnership with Radoop - an analytics company that optimizes the big data platform known as Hadoop. Now, after successfully acquiring Radoop, Rapid Miner will be able to provide access to many other Hadoop features to its customers which will in turn build a larger presence in the Hadoop ecosystem for RapidMiner. The acquisition also brings partnerships with Hadoop platforms Cloudera and Hortonworks, and adds 20 new clients to RapidMiner’s customer base. The powerful combination of RapidMiner and Radoop will allow applications of advanced analytics to big data. Apart from providing scripting and advanced predictive analytics for experts, it will also help non-technical people to access, analyze, and visualize big data.

To read more, Visit the following link:

http://betaboston.com/news/2014/06/17/rapidminer-acquires-big-data-analytics-company-radoop/

Today, companies have the option of collecting information about consumers which was never available in the past. This information is collected through internal sources, such as company websites and sales records, and external ones, such as social media, smartphones and tablets. This vast amount of information on consumers is increasingly referred to as big data. When a consumer visits a website for the first time, cookies are sometimes uploaded on his browser containing a unique ID, making it possible for the company to identify him during his next visits. Once identified, it will be possible to link the consumer to all information the company stored about his profile, which makes personalized marketing possible. Today, because of prescriptive analytics models embedded into their operational systems, websites and apps can analyze consumer information in real time in order to offer personalized travel options instantly. In the next few years, we will witness a gradual move to 1-to-1 marketing in the online travel category, with each consumer treated in a different way in terms of the whole marketing mix. To know more about this visit:

http://blog.euromonitor.com/2014/05/big-data-unique-ids-and-prescriptive-analytics-revolutionising-online-travel-marketing-part-1.html  .