Everyone knows that football is a simple game. But, nowadays, football clubs are focusing on statistical data and that in turn help clubs to take important decisions. Football clubs are applying analytics to answer important questions. For more read the article written by The Sunday Telegraph: http://www.newindianexpress.com/sport/Why-Football-Clubs-Place-Such-Importance-on-Analytics/2016/04/17/article3384984.ece

CRM helps companies to maintain healthy relationship with clients. CRM software improves two aspects of business: productivity and profitability and the right technology, if implemented correctly, not only improves business, but also improves the user experience for your clientele. But, it is important to acquire the right technology. This article writes about few factors that are important before adopting any CRM software.  Read more at: http://it.toolbox.com/blogs/insidecrm/5-factors-to-consider-when-selecting-the-right-crm-solution-for-your-business-73169

Nowadays, social media is much more than a marketing tool and most businesses are beginning to realize the power of integrating social media teams into their business. Social media marketers are the first points of contact with customers. They have conversations and audience data that can be used to create a better consumer experience. Author Dhariana Lozano writes in her article about some ways to use social media beyond marketing. They are - Product development, Feedback, Extend the Life of Content, and Testing & Borrowing Content for Offline Initiatives. Read more at: http://www.business2community.com/social-media/4-ways-use-social-media-beyond-marketing-01503766#1C1P7ukIEJ7h66ir.97

We can now analyze real estate data with the help of Business Intelligence or BI. BI tools are also beneficial for sellers and brokers. These tools can track prior industry performance statistics, such as sales in a given area, which in turn help sellers set asking prices that are actually competitive. In short, they are making marketing easier. For more read the article written by Sean Mallon (blogger) : http://www.smartdatacollective.com/seanmallonbizdaquk/402264/how-business-intelligence-making-real-estate-easier

Embedded Analytics mean adding features associated with BI software such as dashboard reporting, data visualization and analytics tools to existing applications. This can for the most part be accomplished in two ways: o In-house development o Purchasing and embedding out-of-the-box software. To know more about embedded analytics, follow the article written by Eran Levy (Content Manager, Sisense) : http://www.smartdatacollective.com/eran-levy/400533/does-future-lie-embedded-bi

Oil is the backbone of the worldwide economy. But the procedures to transform crude, raw petroleum into a refined item is costly. Application of analytics in the oil industry minimizes hazards and augment benefit. With analytical information, the refiner can better predict processing issues and actualize financially savvy moderation systems. With the recent advances in data and analytics, it is possible to minimize risk and maximize profit. There are several analytical tool which is helping the oil industry. They are: Unrefined fingerprinting - a diagnostic gadget measuring properties of the rough, coupled with refined predictive analytics. It offers quick reaction, on location analysis of unrefined oils and other hydrocarbon liquids. Crude fingerprinting - tests and recognizes unrefined crude before it goes into the refining process. 

For more read the article written by Tom Stanley

(Chief Technology Officer, distributed power and water GE Power): http://www.environmentalleader.com/2016/04/04/how-data-analytics-is-changing-the-oil-gas-industry/#ixzz45s52g6qt

Every financial and social interaction includes credit information, current & past borrowing behaviors and the ups & downs of a company's cash flow. Nowadays, business owner obtains an alternative loan based on data stream which is easier. Lenders look at many things when assessing the total collection of data from a business:

• Marketing data

• Omni-channel content creation and 

• Social media activity, content, shares and interactions

These factors will help in creating opportunities for short term loans and other types of business lending. 

For more read the article written by Sean Mallon ( Blogger) : http://www.smartdatacollective.com/seanmallonbizdaquk/398935/can-big-data-help-your-business-get-funding

Graphs are not just nodes or links. They are powerful data structures anyone can use to represent complex dependencies in their data. Graph applications are used in various places ranging from cancer research to large-scale cyber threat detection to collaborative filtering recommendation systems. In the world of data-intensive analytics, memory bandwidth is the primary performance restrictor. Because graph algorithms display non-locality and data-dependent parallelism. When you crisscross a large group, you are constantly asking for from main memory. For these problems, GPUs provides superior bandwidth to memory and can deliver significant speedups over CPUs. GPUs are very fast for graph processing and analytics, where memory bandwidth is a problem. The memory bandwidth of GPUs provides a new way to speed up data-intensive analytics and graph analytics. For more read the article written by Brad Bebee ( CEO, Blazegraph) : https://devblogs.nvidia.com/parallelforall/gpus-graph-predictive-analytics/

Computer systems are quite similar to living organisms. The living part of the system is not so much the hardware or even the software it is the data. We seek to build computer systems based on ourselves and take this as models. Non computer science based behavior patterns and structures provides a language for discussing the patterns, behaviors and structures of systems. An artificial neural network is inspired by the biological nervous systems, such as the brain. It is composed of a large number of highly interconnected neurons to solve specific problems. An Artificial Neural Network or ANN is configured for pattern recognition or data classification. For more read the article written by Bruce Robbins (CEO, Xcipi) : http://www.smartdatacollective.com/bruce-robbins/397616/data-alive-through-deep-learning-and-intelligence

The challenges faced by the insurance industry are same as other companies. Insurance industry involves generating, storing, and making large & complex sets of data to create efficiencies and improving their bottom line. In a recent survey, 54% of 48 U.S. and P/C insurance executives said they use predictive modelling for underwriting/risk selection, and that usage is expected to grow by 40% over the next two years. Predictive modeling helps in claims triage, underwriting appetite and strategy, market-share analysis, and litigation propensity. Predictive analytics can boost companies' profitability by: 1. Developing a clear analytics roadmap across business units. 

2. Monitoring their outputs against what is happening to avoid the situation where underwriters push back on predictive models.

3. Developing an enterprise-wide model monitoring program to ensure models are recent and recalibrated on a consistent basis. 

4. Looking outside the industry to see how other organizations measure ROI.

For more read the article written by Loren Trimble and Michael Kim(Contributors) : http://ww2.cfo.com/risk-management/2016/03/predictive-modeling-can-make-insurers-better-risk-managers/

The need for wearing body cameras for police personnel has increased. Police departments are also taking the help of specialized data mining solutions to predict and prevent misconduct. The problem with this, is that, it leads to officers being treated differently based on actions they are yet to take, and might never take at all. Predictive analytics is playing an important part in modern policing and in ending crimes which are less serious than police misconduct. For more read the article written by Graham Templeton ( Writer ): http://www.extremetech.com/extreme/224560-new-analytics-can-predict-and-possibly-prevent-police-misconduct

Nowadays, new solutions are based on IoT, analytics, and business intelligence and we are surrounded by more applications of big data, analytics, sensors, and business intelligence. According to Mary E. Shacklett (president of Transworld Data) writes in her article about some examples of behind-the-scenes technology that is redefining the world. Some of them are - 1: Weather and political prediction systems for global supply chain and financial market impacts

2: Merchandising

3: Police suspect identification

4: Robots for cleaning your floors and your carpets

5: Medical patient demographics

6: Helping the blind see

7: Crime scene forensics

8: Building information modeling (BIM)

9: Self-driving vehicle systems

10: Inventory management . To know more, read the article written by Mary E. Shacklett (president of Transworld Data) : http://www.techrepublic.com/blog/10-things/10-ways-big-data-analytics-and-sensors-are-helping-behind-the-scenes/

Previously, CRM was unable to provide insightful data on customer behavior. The Internet of Things (a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifier) changed the scenario. The CRM providers have been inspired by IoT and they are successful to improve the customer relationship and meet customers' needs. This new trend is known as CRM of Things. It creates new marketing opportunities for businesses, and helps to improve customer service. Read more at written by Inside-CRM : http://it.toolbox.com/blogs/insidecrm/how-crm-can-benefit-from-the-internet-of-things-71582

The goal of customer-centric CRM is to improve customer value. Every customer is unique and their needs are also different. Organizations should customize their services and products according to customer needs, for gaining customer loyalty. A customer's contribution towards the net profit of a company determines the customer value. Customer loyalty is the most important thing for any company. Surveys will them to measure loyalty. Customer identification and prioritization is also important as it helps to identify customers that provide the most value. There should be well-planned strategies for customer acquisition and retention. Customer-centric CRM can improve revenue, decrease costs and enhance the customer experience of a company. For more read article written by Inside-CRM : http://it.toolbox.com/blogs/insidecrm/crm-and-the-concept-of-customer-centricity-71614

To understand the crime pattern, data are used by police from the past. The objective is to predict acts of terrorism based on the large amount of surveillance data. The technology will collect data on ordinary citizens, including information on their jobs, hobbies, consumption habits, and other behaviors and will flag unusual behavior that could signal a potential terrorist attack. Police in both Los Angeles and Manchester ran trials using a computer algorithm to predict where crime would most likely to take place. Their aim was to find patterns in criminal behavior by analyzing crime data to prevent crime through predictive policing. There is a need for research into machine learning and other artificial intelligence technologies to identify human faces in surveillance video. The police can analyze large amounts of crime data, i.e. 'big data'. Enterprises are now using big data technologies to detect and fight cyber threats. Big data analytics and shared threat intelligence can increase the organization's effectiveness in security measures. For more read: http://www.cxotoday.com/story/how-big-data-can-be-used-to-predict-crime-patterns/

The Internet of Things (IoT) generates semi-structured or unstructured data in real time. Organizations take advantage of cloud because big data can be best managed in the cloud. By utilizing fog computing, organizations can decrease time to action; reduce costs, infrastructure and bandwidth; and can get greater access to data. The advantages of the decentralized method of fog computing and IoT analytics cover both the organization and the end user. One of the benefits of centralization is to focus and understand the data location and the accessibility. The decentralized method is associated with flexibility and agility. This tends to describe the data management trends and applications. Read more at the article written by Jelani Harper (blogger) : http://data-informed.com/the-internet-of-things-and-the-necessity-of-fog-computing/

The development of mobile, IoT, and the cloud has increased the need of analytics to solve challenges in the customer, product, operations, and marketing domains. The established companies need to restructure their business and technology to increase their sales. Organizations need to involve cross-functional teams to establish data governance. Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. Now in the era of Analytics 3.0, when tools make decisions and measure the impact. For more read the article written by chandramohan Kannusamy (Technical Architect) : http://data-informed.com/analytics-3-0-and-data-driven-transformation/

The best way to improve the probability of desirable outcomes is to predict the unknown future results. Predictive analytics help organizations to become forward looking and proactive. It uses a number of predictive modelling and analytical technique for the prediction of the future. One of the modelling techniques is a response model which doesn't predict influence. It only predicts the desirable outcomes of one method without making any prediction of alternative method. Healthcare organizations will be more successful if the predictions for treatment decision results in the desired outcome. For more read the article written by Eric Siegel (founder of the Predictive Analytics World Conference):

http://data-informed.com/drive-influence-with-uplift-modeling/

A proposed framework focuses on the development and application of electronic healthcare predictive analytic (e-HPA) applications. The framework is built on "earlier frameworks of model development and utilization" to show opportunities for e-HPA, ways to address challenges and ideas for motivating stakeholders to adopt and refine the framework.

Five areas provide the structure for the framework, which includes:

1. Data barriers and model development

2.  Transparency and model evaluation

3. Ethics

4. Regulation and certification

5. Education and training. 

It is the task of healthcare leaders, e-HPA practitioners and other stakeholders to ensure an infrastructure .The infrastructure promotes effective use of predictive analytics to improve patient outcomes, satisfaction and the value of healthcare resources. For more read the article written by Katie Dvorak(an associate editor for FierceHealthIT and FierceHealthcare) at : http://www.fiercehealthit.com/story/proposed-framework-focuses-electronic-healthcare-predictive-analytic-applic/2016-03-08

The future of AI is filled with amazing opportunities. Alan Turing designed the Turing machine about a century ago. It is expected that in the coming times the machines will pass the Turing test. The AI machines are being expected to use all the five human senses. Governments from all over the world are planning to make simulation models which make use of big data as input to predict the criminal or terrorist activities. AI has been improving human lives by uplifting the healthcare and medical facilities. To make the computer - human interaction more natural, scientists are working on the Natural Language Processing Algorithms. Read more about it at: http://nerdsmagazine.com/artificial-intelligence-five-highly-anticipated-developments/