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/

Dubai Airports CEO, Paul Griffiths, is revolutionizing the airport experience and working. The Dubai Airport is handling 120 million annual passengers. This causes a need for something preventing the degradation of customer experience. Using in-dept. analytics to assign gates in a dynamic way is the first step. The system can assign gates in a far more efficient manner – such that if two flights have a significant numbers of passengers common between them, they will dynamically be assigned gate positions as close as possible. A new initiative allows retail stores in the airport to scan passengers' boarding passes and provide current updated about their departure gate, time, and the estimated time to move from the customer's current position to the gate. There are thus using data to improve the efficiency and reduce the cost structure of air industry operations. Read more at: http://www.networkworld.com/article/2944535/big-data-business-intelligence/big-data-enables-an-entirely-new-design-of-airport.html

 Through a randomized double-blind controlled trial, we can guarantee predictive analysis though these trials sometimes fail. So in real-world the chances of true predictive analytics is virtually none as randomized double-blind control trials are also highly suspected. Thus workforce analysts should start using correlational analytics by first accepting that predictive analytics probably doesn't exist in most organizational settings and that we need to do with correlational analytics. Then they can assess the risk of investing large sums based on analytics which are correlational rather than predictive. Next always run a small pilot or two before spending big money on implementing the results of "predictive analytics" as we now know it is just correlational analytics. Read more about it by Max Blumberg (Workforce analytics and automation consultant) at:  https://www.linkedin.com/pulse/dont-waste-money-predictive-analytics-doesnt-exist-hr-max-blumberg?trk=hp-feed-article-title-like

Data scientists are the ones who use sophisticated quantitative and computer science skills to both structured and analyse unstructured data as well as derive intuitions from the data and suggest actions. Data scientists can tackle with problems that are complex, huge in size and disorganized using several coding languages. To identify a data scientist following points are to be taken into consideration:

• Qualifications: Data scientists are required to have an advanced degree usually a masters or PhD in a quantitative discipline such as economics, statistics and their educational background may be diversified.

• Skills: Data scientists are efficient users of different tools used for analytics and are well versed with coding languages such as Python or Java used for writing programs, transformations etc. They are also have expert knowledge about statistical and machine learning models such as R and SAS.

• Dataset size: They usually work with datasets measured in gigabytes up to petabytes.

• Job responsibility: Data scientists are well equipped to work on every stage of analytics life cycle which also include data acquisition, transformation/cleaning, analytics to predict patterns of the datasets, prescribing actions and programming/automations to contribute to a firms data products.

The main idea behind this is that whether you are a data scientist, analytics professional or programmer you always need to be well versed with the new languages coming in the market each day just as big data has been gaining importance and keep up with the new technology.

Read more at: http://www.smartdatacollective.com/lburtch/320541/more-just-title-how-identify-data-scientist

Predictive analytics tools and techniques are becoming a rage nowadays because of their immense application in a variety of sectors. Collecting data and predicting future trends have been so easy. More and more business are vying for these tools as they help with predictive modelling of customer behavior. Even bigger potentials are lurking in the near future and organizations are investing in the development of these software keeping that in mind. Companies which have still not adopted these software are falling behind in competition with other firms. Thus there has been a surge in the usage of predictive analytics as every firm is craving for more information. Read more at: http://www.dataversity.net/how-your-business-can-benefit-from-predictive-analytics/

Organizations along with their partners needs to participate in the supply chain - from suppliers to customers in order to fully exploit their business relationships. The route for management of supply chains is high speed communication and cooperation. The digital supply chain helps to build trust and improves transparency and efficiency. The Internet of Things (IOT) is crucial for facilitating supply chains in future. It uses sensor-embedded equipment in order to convey data about the parameters and this boosts performance and safety. Predictive analytics is also a blessing for the supply chains. However the most significant aspect of a digital supply chain is social media. Some businesses integrate social technology with business transactions thereby leading to an increase in transparency.

For further details on this study, please follow the link: https://channels.theinnovationenterprise.com/articles/the-development-of-the-digital-supply-chain

In technology adoption, the healthcare sector is still lagging behind, due to its intricate compliance and security requirements. The healthcare sector has been mainly focusing on medicines, which is evolving continuously. However, the scenario is changing, as healthcare providers have started to compete for market share. Healthcare marketers are trying to identify prospective patients, most likely in need of their healthcare services and retain them in their institutional networks. Hence, predictive analytics is being applied to big data, in order to determine consumer behaviors and patterns in their operational areas, according to their healthcare service usage. Read more at: http://www.techrepublic.com/article/the-underexploited-big-data-sweet-spot-for-healthcare/

Today, enterprises have come a long way dealing with the big data challenges. But there are more to go. Revisiting a few of them would help understand the task ahead. Timo Elliott, innovation Evangelist for SAP, in his blog Business Analytics talks extensively about the following 5 challenges:

• There’s still a lot of technology to learn

• The right people are still hard to find

• The right business case is key

• Integrating with existing systems is more important

• New business models are the next big opportunity

For an enriching account of each of them follow the link http://timoelliott.com/blog/2015/05/top-big-data-challenges-revisited.html

Although most modern enterprises run on data, it becomes useless if we cannot derive intelligence from it. Data includes all the information that is available including raw facts about companies and individuals. Intelligence is when we look at the data and try to find a relation to better interpret challenges and opportunities in the market. Insight with data and intelligence deals in adjusting strategies in response to the market. To develop predictive models about the market, we need intelligence and insight alongside data. Raw data is often mistaken for insights. For smarter decisions, internal and external data sets should be used as starting points and then create intelligence to draw sensible insights. To know more:  http://www.smartdatacollective.com/ray-renteria/327508/data-does-not-equal-intelligence-predictive-analytics-enterprise

Big data analytics in healthcare industry, encompasses the whole transformation of data, beginning from the recording of the fundamental data to predicting diseases and alerting patients. Any health concern’s big data analytics capabilities fall into three major categories, descriptive, predictive and prescriptive. Descriptive analytics quantifies past events, like for example, the number of patients, who visited the hospital in the last month. It provides considerable insights into the data which can be later used to make predictions, regarding disease trends. Predictive analytics uses these descriptive data to predict the future, which often reduces unnecessary costs, related to controllable diseases, by making early detections. Finally prescriptive analytics provides suggestions on ways to mitigate the imminent threats, suggested by predictive analytics. Hence, an efficiently functioning data analytics infrastructure, incorporates all the three processes and is invaluable in the healthcare industry. Read more at:

http://healthitanalytics.com/news/healthcare-big-data-analytics-from-description-to-prescription

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

The combination of new software technology, industrial data science and visualized information is acting as a game changer in the aviation industry. Analysts monitor flight data every second on the ground, for variations and immediately alert the pilot of any anomalies. Real-time data monitoring also helps in reducing fuel consumption apart from providing safety and security. Tim Leonard of southwest Airlines while talking to business insider said that the predictive analytics tools help prevent disasters they make pilots, mechanics, and employees more aware. It also helps provide frequent and precise data on current weather conditions leading to better scheduling and minimising delays. Read more at: http://www.businessinsider.in/Why-every-flight-you-take-is-obsessively-monitored/articleshow/47694902.cms

Like other conservative industries, chocolate industry is also interested in applying smart marketing techniques for their business. Companies need to be more scientific in their marketing ways and by now it's proven that future growth of the chocolate industry also depends on data-driven consumer marketing. Vosges Haut-chocolat company used the method of predictive analytics solution to reach its buyers. The difficulty in this task was to figure out how price sensitive were the consumers. With newly discovered software's and AgilOne predictive marketing cloud, Vosges tried to target all its customers who had left shopping carts and also found all different reasons for their - not purchasing the chocolates. Predictive analytics made it possible for the company to provide heavy discounts to the people segmented into proper user-groups and increase their sales so that consumers who would never buy any chocolates ever could be differentiated from the rest.

Read more at: http://www.smartdatacollective.com/socialmktgfella/326221/even-chocolate-needs-smart-data

When it comes to the healthcare industry and its ongoing revolution, Big Data and Internet of Things have become an integral part of it. As more and more health data is now being digitally stored, the seer amount collected on patients is growing at exponential rates. Big data, already used for the mapping of the human genome, is now showing its value in the analysis of large complex data and thereafter in predictive analytics to find people most at risk of a certain illness. Predictive analytics isn't the only place where Big Data and IoT have helped in the advancement of healthcare services. There is now the possibility of analysis of data generated by IoT devices to help calculate medication tailored for each patient and also calculate their results in advance. Read more at: http://www.predictiveanalyticsworld.com/patimes/the-iot-and-big-data-are-teaming-up-to-push-healthcare-further-than-ever-0627156/

Social media and networking sites are helping businesses and companies to grow. The social media not only helps as a platform to communicate with the customers and build up a brand reputation, but it also helps the way business is run. It also helps business enterprises to collaborate across departments, offices, countries, and with other business houses as well.  According to recent studies, social media analytics along with predictive analytics is going to be the most effective technology for business development. Its impact will be greater than internet of things and mobile payments. So embracing this aspect will be necessary for a business to survive.To know more read: http://www.cio.com/article/2937401/social-collaboration/how-collaboration-tools-can-turn-your-business-into-a-social-enterprise.html

Big data is completely transforming the way we look at business. Analytics is being used to foster growth in every industry. The entertainment industry uses predictive analysis to forecast the box office collections, gauging the success of the movie before they even start shooting it. Production houses can use big data to plan strategic release dates and to retain consumers by analysing patterns derived from the data. By collecting data from the social media sites, they can formulate plans based on consumer sentiments as to what kind of music, casting, genre, are in trend thereby increasing the ROI and giving improved entertainment to the end user. Read more at: http://analyticsweek.com/how-big-data-is-changing-the-entertainment-industry/

Predictive analytics helps police officers in America and the UK solve crimes. To reduce crime rates, there is an increase in budgeting, planning, installing cost which aren't considerable to the proportionate reduction. Thus more money spending would help improve public spending. New equipment will also assist public safety. Likewise, gradually predictive analysis will also be used to solve terrorism. PredPol is one such systems to target robberies, crime etc. Obviously, like others it too has backdrops as it involves mass data collection. But the advantage that has occurred and might occur because of it are much important to focus on than its backdrops. Read more about this article at: https://channels.theinnovationenterprise.com/articles/how-predictive-analytics-is-revolutionising-public-safety

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