SigmaWay Blog

Nowadays, medical sensors and data analytics are used to boost medical devices. Devices can forecast unfavorable outcomes before they occur. After analyzing large data sets, researchers can identify small changes in patient behaviors. Combining with data analytics, implantable medical sensors will allow monitoring patient health. Utilizing predictive analytics, smart sensors identify unfavorable changes in data which helps to detect medical crises very fast. Data analytics is used to influence smart devices that provide guidance to patients. These devices receive inputs from their sensor data. Predictive analytics help to make unique medicines. Smart devices use data to predict how an individual patient will respond to specific courses of action. Data analytics also help manufacturers to go beyond the general results of clinical trials to better interpret the value their devices for specific groups of patients. Read more about this article written by Battelle : http://www.healthcareitnews.com/news/big-data-difference-predictive-analytics

Data scientists have categorized this new era of data with "four Vs". They are volume of the data, variety of the data sets, velocity of analysis of data and veracity of data quality. Nowadays, companies are turning to external Big Data for answers. Organizations want to distinguish which external factors will influence the sales and demand of a particular product in the future. The answers they get from the above questions, is setting the trend for three distinctive predictive analytics process. They are - Predictive hypothesis testing, Closing the gap between data and delivery, shrinking the barrier between internal and external data.  With the speed of technology diversity of data continues to grow. Read more about this article written by RICH WAGNER(Author) at:

http://www.information-management.com/news/big-data-analytics/a-new-era-of-predictive-analytics-2016-trends-to-watch-10028253-1.html

A cyber - attack may happen anytime in today's world. Big data and predictive analytics help in cyber defense and convert data into actionable intelligence. Predictive indicators can identify new risks and assist in security. They can go undetected. Predictive analytics can detect these unusual data, including hidden data. By finding these unusual patterns, predictive analytics help to reduce a company's overall risk. With predictive analytics, risks are evaluated and ranked in importance. Managing the predictive analytics process requires an organization to handle the false positives and false negatives that are generated during the threat surveillance process and it cannot be too restrictive as it will block logical traffic, which can lead to a reduction in profit or customer service. It depends on how a person is using it to get the best results. Read more at:

http://www.information-management.com/news/big-data-analytics/using-predictive-analytics-to-identify-cyber-security-risks-10028270-1.html

Today world is changing at a rapid speed. Technology is playing an important role. Agriculture technology is also advancing and promises to unleash its productivity. The combination of advanced mathematics, automation, advancements in sensor systems and next-generation plant breeding are setting the stage for the next Green Revolution, which is needed to ensure a better future. Next-generation farms are putting science and technology to work towards delivering a step change in yields and growing more from less. Here are the four most exciting developments in 2016.

1) The Mathematics revolution.

2) The Sensing Revolution.

3) Putting it together with automation.

4) Next generation plant breeding of corn.

Read more at: http://www.forbes.com/sites/gmoanswers/2016/01/26/four-technologies-that-will-usher-next-generation-farming/#3cfe147b69f5

The use of predictive analytics in the Human Resource Management is the recent development. The HR team is taking advantage of the loads of data to keep a check on the employees. Predictive Analytics is changing the relationship between employee and enterprise. Predictive models can easily predict the upcoming trends. The performance of the employees can be judged in an unbiased way rather than gut feelings. The models identify meaningful insights by collecting data from the various platforms. The kind of posts that individuals have on the social networking platforms, LinkedIn profiles all lead to predictions. HR teams use these insights to decide the promotions, pay scale etc. This also acts as a disadvantage to the privacy of workers. Soon there will be a need to redefine the worker privacy laws which will take care of these new technologies. Read more about this in the article written by Rodd Wagner (A best- selling author) at: http://www.stevenspointjournal.com/story/opinion/columnists/2016/01/22/predictive-analytics-transforms-workplace/79116764/

Contextual Integration refers in identifying meaningful relationships between different information types. This gives a multi-dimensional view of the data rather than a single access point. The best approach is to analyze these volumes of data from different perspectives. The traditional way is to follow a fragmented approach. The web teams, marketing and sales team will look at the different statistics offered by data. This lengthens the time to take decisions and also introduces inaccuracy. The need is to look at data from many angles to create a multi- dimensional profile of the customer. Then predictive analytics can assess and lead to intelligent messaging. Machine Learning is also helping to improve these predictive analytics algorithms by checking it on the real time data. Read more about it in the article written by Dominik Dahlem (Senior Data Scientist at Boxever) at: http://data-informed.com/contextual-integration-secret-weapon-predictive-analytics/

Failures in the functioning of equipment are inevitable in any kind of industry. The repair and recovery time often leads to big financial losses each year. But we have machine learning and predictive analytics as a solution. The machine learning models are trained to learn the ideal functioning of the machinery. Then this functioning is compared with how the machines are working at present. So if even a minor change occurs somewhere, it doesn't go unnoticed. Then, with the help of predictive analytics the loss that can take place in the near future is predicted. This adds to the huge advantage of the firms. Learn more about this in the article written by Mike Reed (manager of analytical services for Avantis PRiSM software) at: http://www.intelligentutility.com/article/16/02/saving-money-and-man-hours-machine-learning

Predictive models are used to predict future outcomes on the basis of data collected from past. Many organizations take their crucial decisions based on the foundations laid by the predictive models. But what if the model goes wrong? "BOOM"- crash of a significant part of the strategy! Though each predictive model has some scope of error. There are chances that the input variables considered for the model were not appropriate. But we need to find out what kind of error and to what extent it is acceptable. There is a need to work on the foundation of the predictive models to prevent failures.  Read more about it in the article written by John Bates(Senior Product Manager for Data Science & Predictive Marketing Solutions) at: http://blogs.adobe.com/digitalmarketing/analytics/what-to-do-when-your-predictive-marketing-is-wrong/

Those using Predictive Analytics easily outpace those who don't. There is a clear incremental sales lift in the marketing campaigns which consider predictive analytics. Every organization strives to achieve a higher return on investment (ROI) from that spend on marketing. Predictive analytics help creation of unique customer profiles by analyzing the data.  Read more about how Predictive Analytics can be useful in B2B marketing in the article written by Laura at: http://blogs.forrester.com/laura_ramos/15-07-02-the_power_to_predict_can_give_b2b_marketers_an_unfair_advantage

Even at the time of hiring, workforce analysis influences from starting i.e. creation of a job description and worker roles, till the recruitment of the person with better chances to succeed in the organization. Through the analysis, companies can make better hiring choices and monitoring the performance in real time. This will help companies to refine their organizational structure and develop talent and leadership within the organization. Workforce analysis will also help in reducing legal claims issues. These all will contribute in cost reduction and smooth working of the organization. Read more at: http://www.smartdatacollective.com/sarah-smith/358073/control-business-costs-workforce-analytics

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/

Big data will play a major role in shaping the future of businesses. The two major areas that are going to undergo significant changes are as follows:

(i) Previously, once data had been used, it used to be deleted. But now, as predictive analytics makes use of historical data, so data needs to be stored for an indeterminate period of time. This is against the current laws and thus requires amendment. In future, well-defined laws about collection, distribution and storage of data are going to be the key to success for businesses using Big Data.

(ii) With technological advancement, data is being collected at a faster rate than before. Also new and improved technology is being used to sort it. In future, frameworks will need to be robust in nature and analytical processes should be improved in terms of speed and accuracy. Read more at: https://channels.theinnovationenterprise.com/articles/big-data-and-the-future-of-business

The increasing number of startups have surpassed the big players with data driven business models. Data science has become obsolete! The next big thing is predictive analytics.

But there are fears associated with adopting this new technology like the fear of complexity, replacement and failure. 

How effective predictive analytics will be depends on how the organization perceives it. When everyone in the business starts thinking about how predictive analytics can improve their organization, it results in big wins for the company. 

To move forward with predictive, the business needs to leave data science behind. Predictive analytics is a completely different approach. Applied to business, predictive models are used to analyze current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company. It uses a number of techniques, including data mining, statistical modeling and machine learning to help analysts make future business forecasts.

To know more- https://icrunchdatanews.com/3-keys-smooth-migration-data-science-predictive-analytics/

Big Data is the game-changing opportunity for marketing and sales. Airlines are spending on advertising. With the help of big data and analytics airlines can see how to allocate ad dollars. Marketing mix is one example of analytics which helps us understand which media vehicle works best. Following are the three key aspects of marketing mix-

1. Examining the noise- Airlines should take into account the impact of noise.

2. Normalizing the data- This means to adjust the data for external factors (those outside marketing campaign).

3. Performing a regression analysis- To calculate the return on each investment made for marketing and to know the mean impact of each campaign analysis of data is necessary.

To know more- http://revenueanalytics.com/blog/airlines-can-maximize-return-on-marketing-spend-with-predictive-analytics/

Actionable data to support or automate decision making is used by predictive and prescriptive analytics. Prediction of a quantity i.e. regression is required by many use cases. The actions based on the predictions is more important than the actual quantity predicted. Predictions of future events in the context of predictive and prescriptive analytics needs a full probability density distribution. The predictions might give some estimated value of expected fluctuations, but details will still be hidden in the full probability density distribution. In this case, advanced predictive models can be used to derive best point estimator for a given scenario.
Read more at: http://www.blue-yonder.com/blog-e/2015/07/20/predictions-as-a-probability-density-distribution/

Predictive analytics can be seen as business investment rather than an IT investment. But researchers found that there has been shift of funding from general business budget to IT business budget. According to research 15 percent of the organizations prefer to purchase predictive analytics. But truly speaking you will find that there is increasing demand for predictive analytics. This can let businesses to respond faster to market activities and threats. Business investment in predictive analytics has occupied more space in front office but according to research IT and operations are closely related to these operations. To use such analytics technique, the requirement is just a permission from high level management of organization.  Read more at: http://www.smartdatacollective.com/tony-cosentino/332022/predictive-analytics-investing-and-selecting-software-properly

External factors have a great influence on businesses thus understanding these factors is very crucial when building a statistical forecast. To know whether an external factor has influence on our analysis or not we should consider the following aspects-

1.Consistency- This means how volatile is the data.

2.Accessibility-This is the ease with which we get data.

3.Frequency of getting data- For yearly decision making a quarterly data would do good but for frequent decision making daily data fits best.

4.Data Granularity-The granularity of data refers to the size in which data fields are sub- divided.

To read more- http://revenueanalytics.com/blog/uncovering-external-influences-in-your-analytics/

Through Predictive analytics, customer behavior can be predicted to learn engaging with them and improve their experience. Mobile on the other hand is making marketers refocus their analytic efforts. Thus, predictive mobile analytics enable organizations discover consumer behavior by looking at data of how apps are used and finding their acting patterns. Using this information, marketers can focus on their marketing and advertising initiatives, looking at customer engagement during a promotion in real-time to facilitate greater targeting. Through predictive analytics, one can find the targeted customers. Digital tags enable to create a digital dossier where browsing history can link to a particular unidentified individual helping maintain their privacy. Thus can establish repeat behaviors that lead either to a purchase, or a rejection. Read more about it at:  https://channels.theinnovationenterprise.com/articles/why-you-must-invest-in-mobile-analytics

Data science with number of practical uses is becoming an indispensable tool for all businesses. Its pace has reached an exponential level in recent times due to many advancements. But many experts are now shifting the gears into next level, predictive analytics, to save the future of this invaluable science, but getting this accepted industry wide is going to be a rough ride. Getting a smooth change from data science to predictive analytics will need an industry wide trust. It also needs the barrier of welcoming the new entrant eliminated by showing the effectiveness of this new technology which will create a helping environment for employees and employers alike. But to many hardcore fans of data science it is tough time leaving it to accept a new technology, only the returns and competitive edge this provides will make the shift more pleasant. Read more at: 

https://icrunchdatanews.com/3-keys-smooth-migration-data-science-predictive-analytics/

The huge demand of predictive analytics has led to various trainings been sprung up to impart or at least promising to impart the kind of skills required for developing an analytics model. But more often than not it forgets the primary ingredient. Geert Verstraeten, managing Partner and professional trainer of Python Predictions, feels that understanding the main project phases is the most important thing. He explains these phases with an interesting analogy- developing analytics model is as simple as making soup in a soup bar. Here are 5 phases of model building: 

• Taking the order- Project Definition 

• Mise en place- Data Preparation 

• Cooking the soup- Model building 

• Tasting the soup- Model Validation

• Serving the soup- Model Usage

This makes learning more engaging. To keep enjoying the analogy, click the link http://www.predictiveanalyticsworld.com/patimes/how-to-manage-projects-in-predictive-analytics0710151/