When it comes to risk management, it is often observed that the companies either implement blanket management programs applying the same strategies to all employees, or use the "squeaky wheel approach" focusing primarily on at-risk employees. However, both the approaches result in inefficiency. Thus, a strategic employee-specific management program can be adopted to identify the at-risk employees. Such a program monitors the employees for subtle and almost undetectable changes that are indicative of risky behavior and this is where predictive analytics model is of immense help. Predictive modeling enables the manager to identify not only the high-risk employees, but also the cause behind a particular incident.  Predictive modeling is fast becoming an indispensable tool for mitigating risk, retaining top talent, and building long-lasting relationship with the employees. Read More:- http://www.natlawreview.com/article/mitigating-risk-predictive-modeling

Data analytics with its growing importance is now used to plan mergers and acquisitions which require a well thought game plan. Throughout the buying process, data analytics can be used to see how market will respond to a deal being made. While merging with another company, data gets doubled and so it is important for employees to analyze all sets of data thus allowing business users to make better decisions. All through the M&A process employees must be trained to possess analytical process, needed to survive in this data-driven economy. Organizations must be aware of how effective and intelligent data management and analytics can help drive Mergers and Acquisitions win. Thus Big Data can offer success to M&A. Read more at: https://channels.theinnovationenterprise.com/articles/7467-the-big-data-game-plan-in-mergers-and-acquisitions

Data visualization or infographics allows us to see results in a simple manner without putting in much effort to analyze vast amount of data. The key to making visualization easy lies in making infographics that can easily convey complex data to a wide audience. People can easily relate to infographics as these graphics follow certain conventions. Deviating from these conventions can lead to confusion, making data visualization complex. Trying to fit too much information in a single infographic will make it complex to interpret. In such cases, only the important piece of information should be conveyed or maybe one can put together a series of visualization. Use of proper annotations, labelling the axes and using data wherever needed is crucial. In case of data comparisons, graphs can be made differentiable using contrasting colors. Arithmetic errors should be avoided else the infographic becomes next to useless. Read more at: https://channels.theinnovationenterprise.com/articles/5-common-mistakes-in-data-visualizations

According to the H.O. Maycotte (Austin Tech), Success in real time means collecting, assessing and acting on real-time customer data.” Ability to collect data and respond towards that data separates successful brands from the crowd. Steps to make real-time data and real-time response part of your operations, especially in marketing and customer service:
• Collect and consider real-time customer data in context.
• Look for context beyond the customer.
• Give mobile and social the central roles they deserve.
• Act fast.
• Anticipate your customer’s next move.
Read more at: http://www.forbes.com/sites/homaycotte/2015/06/16/5-ways-improve-customer-service-with-real-time-data-and-real-time-responses/

According to Narendra Mulani (senior managing director of Accenture Analytics), to survive in this competitive world organization need analytics to analyze opportunities and threats to their business. But now the question arises how to use analytics into their organization. Most of the organizations get stuck in applying analytics. So, the author discussed about the steps to simplify their analytics strategy and generate real outcomes. Some of them are:

• Accelerate the data.
• Delegate the work to your analytics technologies.
• Recognize that each path to data insight is unique.
Ways to delegate the work to your analytics technologies are: Next-Gen Business Intelligence (BI) and data visualization; data discovery; analytics applications; machine learning and cognitive computing. Read more at: https://hbr.org/2015/06/simplify-your-analytics-strategy

With the help of predictive analytics, marketers can predict future sales. With this prediction information, companies can now decide on their campaigns. Analytics is mainly used for correlation and causation. A lot of vendors pay maximum attention towards correlation but causation underlying a pattern is important to predict a customer’s purchase behavior. Thus predictive analytics analyzes customer behavior and offers them promotions according to their behavior so as they intake those. Hence if used properly, it can be of great importance. Predictive analytics can help marketers across the entire customer lifecycle, said Fern Halper, director of TDWI Research for advanced analytics. Read more about this article by Katherine Noyes (IDG News Service) at:  http://www.computerworld.com/article/2934086/business-intelligence/marketers-are-betting-big-on-predictive-analytics.html 

Predictive analytics make use of statistical or machine-learning techniques to analyze current and past facts to predict the future. Companies, by using predictive analytics, can make better and decisions at low-cost. Predictive analytics can help companies to get an idea of every possible event, thus allowing for risk management and calculating potential ROI. Using predictive analytics, companies can remove politics from the decision-making process. There is a growing awareness among companies about predictive analytics and companies that adopted this method have reported success and increased ROIs. Optimizing predictive analytics to produce better choices leads to decision modelling. Through decision modelling companies can gain insight into how predictive analytics can add value and how ROI can be measured. Read more at:https://channels.theinnovationenterprise.com/articles/making-faster-decisions-with-predictive-analytics  

Movies are big businesses where a lot of money needs to be spent so that box office hits. Prediction of this hit is an impressive science. So studios nowadays depends on its blockbusters by making early prediction to carry their less profitable films. So, companies uses a number of different metrics to predict the sentiment towards a movie. Analytics has spread its arms from Hollywood towards the World’s Biggest Film Industry, Bollywood. Social Sentiment Index (SSI) reveals measures about successful films. It is argued that it might hamper creativity and risk-taking factor in the movie industry. Thus it might affect small films to even take a chance. Read more about this interesting article at: https://channels.theinnovationenterprise.com/articles/can-social-media-analytics-help-end-box-office-flops

When it comes to Data Analytics, failure is a very important part of the innovation process. A part of the data scientist’s job is trial-and-error and assumptions to vet data for new insights. Pushing the analytical process to uncover new uses of data and new ways of applying analytics necessarily involves risk and failure. Leading websites embrace this by testing hundreds or thousands of both minor and large scale changes to their site daily. Nothing is rolled out broadly on a major website today unless it has gone through rigorous testing. An organization based on rapid experimentation, exploration of new ideas and educated in doing analytics right is one destined to succeed. Read more at: http://www.forbes.com/sites/teradata/2015/05/27/why-being-wrong-is-the-best-way-to-get-your-analytics-right/ 

The evolution and advancement of modern technology has led to a remarkable development of how business is done. With this rapid progress, the complexities in terms of how these technologies are used, has increased largely. Often the names and terms are clubbed together, and it becomes difficult for non-technical business leaders to understand and identify the independent usage of these technologies. "Big data analytics", "marketing analytics", "social media analytics", "audio stream analytics", etc. are some of them. It seems that analytics are often clubbed with other words, many of which are associated with storage and inflow of large volumes of data. If the volume of the sample is adequate enough to give a statistically valid output, then analytics can be executed without resorting to big data .To know more read: http://www.mytechlogy.com/IT-blogs/7973/does-analytics-need-big-data/#.VXE5WM-qqko

In today`s world, data has become a comparative advantage and an integral part of product development. It is a vital component when it comes to enhancing business performance. The most crucial role played in this context is the role played by the data scientists. The data scientists are shaping and building the future of business through modern techniques for data analysis and forecasting. An important reason in such remarkable progression in this field of data analytics is due to the accessibility of the data. The rise of online community, e-commerce, mobile and overall digitization of the society has contributed enormously. According to industry experts, companies are realizing the potential benefits of data analytics and are using it as a powerful tool for business development. To know more read: http://thenextweb.com/dd/2014/11/23/data-scientists-changing-face-business-intelligence

With the rising importance of big data, organizations are now collecting and storing data, but many don't put it to good use. Retailers can leverage customer data to make personalized recommendations about offers and promotions thus providing a shopping experience customized to individuals. Analyzing customer data can help companies to identify customer preferences for products and the prices they are willing to pay. Customer data can be used to identify the most relevant users to ask for feedback, create new products or services and provide better customer services. By analyzing customer data, companies can identify patterns of behavior of customer data and thus formulate targeted marketing strategies, improve organizational effectiveness and reduce risk and fraud. Read more at: http://www.cio.in/feature/8-ways-to-make-the-most-out-of-your-customer-data

Faced with challenges of bureaucratic vices and realities, government agencies often let things slip through the cracks, including fraud. It is disheartening to learn that huge losses are incurred by many public facing agencies due to fraud and such losses are regarded as expected operating costs. Yet, no measures are being employed. Thankfully, investments are being made in predictive analytics tools by agencies and some progress has been achieved in detecting preventing and prosecuting fraud. However, to tackle crimes effectively the tools need to be comprehensive, flexible and affordable. For example, dynamic case management solutions can be applied to tackle the mammoth challenges faced by the agencies. Read more: http://gcn.com/articles/2015/06/10/fraud-control.aspx

Companies can use data scientists to prepare data sets, business analysts to develop models using both statistical and machine learning algorithms, application developers can be used to deploy and manage predictive analytics life-cycles, and tools. There are many vendors in the categories of customer analytics, cross-selling, smarter logistics, e-commerce etc. Open source software community is driving predictive analytics into the mainstream. Many Business Intelligence platforms also offer “some predictive analytics capabilities."  Rapid Miner’s predictive analytics platform can also be integrated into the cloud. Read more about this article at: http://www.cmswire.com/cms/big-data/3-vendors-lead-the-wave-for-big-data-predictive-analytics-028684.php?mkt_tok=3RkMMJWWfF9wsRomrfCcI63Em2iQPJWpsrB0B/DC18kX3RUnJb6Wfkz6htBZF5s8TM3DVlJGXqlI4UEKTLE%3D 

From Software and Platform as a service to data as a service, the trend has always been to evolve to the requirements of the current generation. The next step is mixing them all together and mass upscaling of the data in the system to provide Big Data solutions as a service (or BDaaS). Multiple businesses in the past have sprung up and are offering cloud based Big Data solutions. Big Data refers to the large, mostly unstructured information created and stored, and the analysis and use of this data is called Big Data Analytics. BDaaS is a term used to describe the outsourcing of Big Data functions to the cloud. It includes the supply of data, providing the analytical tools and the actual analysis of the data. BDaaS can also include consulting and advisory services. Read more at : http://www.forbes.com/sites/bernardmarr/2015/04/27/big-data-as-a-service-is-next-big-thing/

Analytics is being widely used in many areas and sports is no exclusion. Data analytics has successfully scripted victories for many teams, already. Data analytics can be applied to any sport be it tennis, baseball or cricket. But, sports analytics is a completely new concept in India. Sports analytics is the amalgamation of sports and information technology. Sports teams are now hiring data analysts who feed data in their algorithms, which then process the data fed and perform numerical calculation to figure out the strategy for the next match. The analyst then informs the coach and the team players about the prescriptive strategies. Read more at: http://www.cio.in/feature/big-data-is-here-to-have-a-good-innings

A recent study has revealed that there is a growing gap between the amount of data that firms are gathering and their staff's ability to analyze it. A major difficulty that the companies are facing is that as the value of data analytics is being recognized the competition for capable analysts is also increasing. Employees are more attracted to companies with a pre-established analytics framework than to newly established ones. To mitigate this problem several companies are outsourcing their analytics work. But having data analysts internally is beneficial as it allows for easier transformation of analytical insights into business actions. Companies should partner with higher education institutes to initiate analytics courses to meet their needs. Firms should also recruit multi-talented analysts and also offer training to existing staff. Read more at: http://channels.theinnovationenterprise.com/articles/the-analytics-skills-gap

Companies now are emphasizing on converting data into action as quickly as possible. This is being done by unifying the databases used for operational applications with those used by analysts. This leads to continuous up gradation of models and adjustment of business functions as things are happening. Information is essential in a number of areas, targeted marketing being one of them. Due to the advent of online shopping and real-time processing of data, retailers are now being able to sell their goods at unadvertised low prices, without loss of profits, to customers who leave their website without purchasing the goods in their cart. In these cases, data handling and extracting data to deliver real-time actionable analytics is a challenge. Read more at: http://channels.theinnovationenterprise.com/articles/driving-real-time-decision-making-with-business-analytics

Getting around in big cities without facing traffic jams is almost impossible. After the businesses, it's the city planners and transportation experts now who are resorting to Big Data when making improvements to city transportation. Commuters have certain patterns they like to keep to while travelling. By analyzing these actions and the factors responsible for them, transportation experts understand why certain routes and modes of transportation are preferred over others. Gathering call data records help provide access to data about travelers, which data scientists can use to decode the transport pattern of travelers. Countries like Australia and Brazil have already implemented Big Data to upgrade public transportation to keep up with the current demand of metropolitan cities. Read more at: http://channels.theinnovationenterprise.com/articles/big-data-s-impact-on-public-transportation

Mobile applications are an integral and inseparable part of modern data analytics. Mobile applications not only influences consumer`s decision to make purchases, but also allows companies to get insights into consumption pattern and market demand. Enterprises seek immediate-data from a wide range of sources, to fuel business processes by means of big data and analytics and this is provided by different types of mobile applications. It plays an important role, particularly in the field of global supply chain management and predictive analytics. On the other hand mobile applications are also being fuelled by data analytics, so that it cannot only collect the data but also analyze it and process it simultaneously to provide instantaneous results in real time. Read more about this article at: http://www.forbes.com/sites/benkerschberg/2014/12/17/how-big-data-business-intelligence-and-analytics-are-fueling-mobile-application-development/