The dynamic interaction between human and artificial intelligence, is going to revolutionize the way, people perceive their IOT (Internet of Things) environment. IoT has numerous applications in businesses, from products like intelligent refrigerator and self-driven cars, to impacting a company’s costs and earnings. Artificial intelligence can help in understanding, say the time required to service a product, the cost incurred in the process and also the substitutes of the product. This knowledge helps in reducing production cost, generates new revenue opportunities, and also aids in diversification of the company in terms of new products. Read more at: 

http://venturebeat.com/2015/06/24/the-internet-of-everything-how-iot-and-machine-learning-will-revolutionize-your-business-webinar/

Large amounts of real-time data is being daily generated by internet usage. Nowadays, devices are being interconnected and smart products are also connected with the internet. In order to use this data efficiently, organizations need to re-structure their supply-chain. The motivation is not just to use historical data in the traditional manner; but to combine data from multiple product interactions generated by both consumers and suppliers, connected via cloud portals. Supervised machine learning can search for and capitalize on the patterns and relations that they derive in the data and help in supply chain based decisions. Once implemented, they can be continuously evaluated and improved based on performance. The end aim is to accurately predict the attributes of future demand. Read more at: https://hbr.org/2015/06/inventory-management-in-the-age-of-big-data

The future of analytics seems to be brighter with IoT data, it's near real speed analysis and complex event processing systems. But, where exactly is IoT analytics going. Michael Hummel, co-founder and CTO of ParStream, talks future of IoT analytics. Daniel Gutierrez has summarized his talks in an article on Inside BigData highlighting the 4 predictions Michael made:

• Big data, fast data and more analytics

• Horizontal integration and vertical application 

• Decentralization

• Integration of advanced analytics and Machine learning

For more on this piece, follow the link http://insidebigdata.com/2015/05/28/and-this-is-the-future-of-iot-analytics/

Yes, a $10 supercomputer swept Wall Street away. Braxton McKee, math and computer whiz, is tapping into the cloud to fetch all that market data inexpensively and built a software that employs Machine Learning algorithms to understand this data. Thanks to Cloud Computing and Big Data analytics getting cheaper, many startups and businesses can now employ their functionalities to provide services that are efficient, fast and scalable. The large amounts of data is mostly unstructured data, such as corporate documents, transcripts and social media and with the rise of Cloud Computing, the process is finally cost effective. Entities have begun leveraging cloud power to help hedge funds and other financial players run complex, big-data computer models. Maybe Cloud based Big Data analytics is the future. Read more at: http://www.smh.com.au/business/markets/10-hedge-fund-supercomputer-sweeps-wall-street-with-power-from-the-cloud-20150521-gh68hx.html

Nowadays, daily sales are not enough to ensure longevity of a business. Markets are beginning to realize the necessity of predicting future sales. They are tapping into the business trends and applying machine learning algorithms on big data to allow businesses to maximize their efforts in the most profitable areas. By knowing ahead of time, what a customer will buy, profits can be maximized and businesses can be tailored accordingly. Predictive Analytics allows us to segment customers, find patterns in their behavior and take preemptive measures to reduce churn. Analytics can be used to identify correlations and also to find causation. It can allow us to predict an individual's purchase behavior once we understand the causation underlying a pattern. Read more at: http://www.computerworld.com/article/2934086/business-intelligence/marketers-are-betting-big-on-predictive-analytics.html

Data mining sounds like a monotonous activity on a pile of information, requiring little oversight. It is however, in the words of Professor Uwe Aickelin, University of Nottingham, "A discipline that blurs the lines between artificial intelligence, machine learning, statistics and other cutting-edge disciplines to unearth the golden nuggets that lurk within data." He explains how data mining is the effort to extract valuable information from unstructured or 'messy' data. Statistics fails to recognize patterns and it is here where Evolutionary Computation and Machine Learning is required. Industries have begun to understand the need to make sense of the large amounts of data out there and Data Mining is more important than ever now. Read more at: http://www.gizmodo.com.au/2015/05/why-data-mining-is-so-important/

The Sports Industry is evolving. With the requirement to be accurate and the presence of data far beyond what humans can perceive and make collective sense of, there has risen a need to be able to observe, process and evaluate the actions of both teams. With the availability of large amounts of data to train the system, we can now accurately predict and develop strategies for the team. Machine learning is already being used to understand the conservative strategies of away teams at the English Premier League. It can also be applied to predict the behavior of individual players such as cricket bowlers in the IPL. Researches are also working on ML Algorithms to identify talented sportsmen based on their psychological characteristics and practice history. Read at: http://www.science20.com/the_conversation/machine_learning_and_big_data_is_changing_sports-155628

For the very first time in this computing era, it is made possible for machines to learn from experience and penetrate through the complexity of the data and identify associations between them, collectively known as cognitive analytics. This innovation works in a similar manner as of human brains. It processes information, draws conclusion and codifies behaviour and experience into learning. Cognitive analytics has the ability to process and understand exploding volumes of data in real time including data that may contain wide variations of format, structure, and quality. Instead of depending on predefined rules and structured queries to mine answers, cognitive analytics relies on systems that draw from a wide variety of potentially relevant information and connections to generate hypotheses. This process differs from traditional analysis in the way that more data is fed into a machine learning system, the system learns, which results in higher-quality insights and more accurate hypotheses. Read more at:http://deloitte.wsj.com/cio/2014/05/13/human-brain-inspires-new-cognitive-analytics/?KEYWORDS=analytics

Professional sports organizations are multi-million dollar enterprises with millions of dollars spent on a single decision. With this amount of capital at stake, just one bad or misguided decision has the potential of setting an organization back by several years. With such a large amount of risk involved it requires a critical need to make good decisions, and hence it’s an attractive environment for data mining applications.

Sports Data Mining has experienced rapid growth in recent years. The task is not how to collect the data, but what data should be collected and how to make the best use of it. From players improving their game-time performance using video analysis techniques, to scouts using statistical analysis and projection techniques to identify what talent will provide the biggest impact, data mining is quickly becoming an integral part of the sports decision making landscape where managers and coaches using machine learning and simulation techniques can find optimal strategies for an entire upcoming season. By finding the right ways to make sense of data and turning it into actionable knowledge, sports organizations have the potential to secure a competitive advantage over their peers. To read more how it has been used: http://www.ukessays.com/essays/psychology/data-mining-in-sports-in-the-past-few-years-psychology-essay.php