SigmaWay Blog

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  .

Sampling error can cause problems if they are not taken care of. Errors in judgment about sample size can be fixed easily and sample sizes must be considered seriously if big data is being used for predictive analysis. A leader trying to use big data in predictive analysis should always consult the data scientist. The way to understand whether enough data has been collected or not for the purpose of prediction involves understanding the tolerance of the risk associated to accept the assumptions drawn from the sample size characteristics. There are two types of risk: the risk that you're going to take some action when you shouldn't and the risk that you are not going to take some action when you should. Also enough information should be available about the sample variation and precision of measurement to know whether enough data has been collected to make prediction. To know more about importance of sample size in predictive analytics, go to John Weathington (President and CEO of Excellent Management Systems, Inc.)'s link: http://www.techrepublic.com/blog/big-data-analytics/why-samples-sizes-are-key-to-predictive-data-analytics/ 

New technologies are emerging and along with that various analytics tools are also developing. Unlike previously, where building predictive models would take as long as twelve months, it now takes only a day or a week, provided you have the right information in your hands.

 But, wait. Your task does not finish here. You have just built a predictive model. The main thing is how you implement it. Properly deploying your model must be made in a systematic approach, as otherwise, can take months. You have to keep in mind that you have to implement your model in the real world.

How to go about it? Read these tips: http://www.banktech.com/business-intelligence/5-tips-for-operationalizing-analytics/240168237 .

For the long term purpose, Predictive analytics will be very significant for healthcare organizations and providers. With the advancement in technology and complexity in healthcare sector, predictive analytics must be implemented as it will make the work easier and reliable for the providers.
In order to avoid risk and uncertainty and for taking quick decision,
predictive analytics will be of a great substitute to a traditional business intelligence practices.

Please go through the link for more details: http://www.predictiveanalyticsworld.com/patimes/what-can-predictive-analytics-do-for-healthcare-reform/

Data complexity is a major challenge faced by the healthcare industry. Now healthcare industry is going through a transformation phase. So companies like General Electric, 3M CO, Siemens and Xerox Corporation are taking initiatives to gain insights for making healthcare processes more efficient by the use of predictive analytics.

Read more at: http://www.nasdaq.com/article/analytics-a-shot-in-the-arm-for-healthcare-analyst-blog-cm337055

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

In the recent years, social media has become a place where emotional roller coaster of the stock markets can be captured. How the Twitter and Facebook users are reacting to different business news can foretell where the index is heading. Knowing this information can help the companies in predicting their market price, top line, etc.

To know more on how social media can help in predicting in stock prices read http://www.investopedia.com/articles/markets/031814/can-tweets-and-facebook-posts-predict-stock-behavior-and-rt-if-you-think-so.asp .

 HR predictive analytics empowers a company to anticipate and prepare for the future. There is a continuous growth in the use of predictive analytics for the last few years. Studies have shown that predictive analytics act as a decision support system, enabling managers to take best decisions, resulting in an average of 5-6% higher gains than competitor companies.

Predictive Analytics 

For more information follow

http://resources.rpoassociation.org/blog/bid/327375/The-Potential-Value-of-Predictive-Analytics-in-Human-Resources                

 “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