In coming years, Data generated by IOT devices are predicted to increase more than the available capacity to store, and all data that's generated is not useful for analysis. Only limited part of it is useful. 

According to Computer Business Review(CBR) magazine, there are some data generating areas. It is important to know what data from which area is precious and what is not. Following are the ten of the biggest data generating areas. To know more about these areas, read: 

http://www.cbronline.com/news/internet-of-things/10-of-the-biggest-iot-data-generators-4586937

There are variety of languages and modeling packages available at the dispense of any predictive modeler. In most surveys capturing the trends and usage of various packages and software tools, R and Python occupy the top positions. Surprisingly these are both command line languages. The reasons for this are many. But what are advantages of using command line languages like R and Python or GUI based packages? Which user interface is useful to what kind of programmers? What is the market share of usage of these packages? Which one among them is most useful for a job aspirant? To know answers read

http://www.predictiveanalyticsworld.com/patimes/what-programming-do-predictive-modelers-need-to-know-0408152-2/

Companies are collecting datasets to have a competitor advantage in the market. But what exactly are they doing with the collected data? And how are they dealing with the ever increasing data that is inrushing their servers or storage units? 

To answer any of above question, we need to know about a process called Data Mining. Data Mining is a process used to analyze raw information to try and find useful patterns and trends in it. Basically a data miner’s job is to make some sense out of the huge pile of data that is available. There are a lot many techniques available to do this. If you want to know more about data mining and these techniques go through: http://www.businessnewsdaily.com/5947-data-mining.html

Like everything else, businesses must also do yearly cleaning of their contents to remove redundant, obsolete and trivial data from their systems commonly known as ROT. This ROT makes it difficult to find data which are important and useful. A few tips to do this daunting tasks of data cleaning given by experts are: • Identification of relevant and critical data for the business from the identified repositories with the help of Subject Matter Experts (SME)

• Analyzing data in these repositories which are suspected to be important with the help of SMEs, automated processes and leveraging software.

• Establishing metadata for finding and retrieving documents, access control, privacy policies and potential business value

• Metadata needs to be classified for all documents in the repository. This classification process reveals the ROT.

These essential chores can lead to cost-effective information governance by eliminating ROT.

Read more at: http://www.cmswire.com/cms/information-management/do-your-chores-clean-out-your-data-029232.php

We are all very used to Power outages in our daily lives but little do we know about the impact it causes on a large scale. Power outages have the potential to put an economy on a downward trend. Addressing the issue of Power outages are being done lately through various technology advancements.One such advancement in this field is to use grids that are smart and automated. Smart grid technologies are enhanced with grid communication by employing grid radios, which are connected in mesh type of layout. They automatically switch lines and isolate faults in few seconds which otherwise take hours to restore power. The selection criterion in accepting these communication technologies is based on their reliability, performance and future proofing. 

In order to build a complete smart electrical network we have different technology suites employed at different stages, and these technology suites are may not n\be from the same company, which means the companies must ensure that their products are capable of running simultaneously side by side. 

Read more at http://www.clickgreen.org.uk/opinion/opinion/126074-how-smart-city-technology-will-reduce-the-impact-of-black-outs

Big Data is growing bigger and bigger every day.

But, as it is growing, it is also becoming more complex. And complexities usually lead to discrepancies in interpretation of same information. As it is said that, “Prevention is better than cure.” Similarly, identifying these discrepancies and the reasons behind them at an early stage is better than allowing them to become a bigger problem.

Lisa Morgan, Freelance Writer, in her presentation at Information Week, has pointed out six major causes behind big data discrepancies. They are:

• Same Data, Different Quality
• Data-Cleansing Issues
• Problems with the Algorithm
• Models Differ
• Model Complexity Differs
• Interpretations Differ

To understand them in detail, please visit the following link:

http://www.informationweek.com/big-data/big-data-analytics/6-causes-of-big-data-discrepancies/d/d-id/1320692

The global healthcare big data market is set to grow at 17 percent compounded annually according to the predictions of ResearchFox Consulting. Predictive and prescriptive analytics shall be the main area of focus in the United States. The Internet of Things (IoT) Industry is likely to get a big push from internet-enabled blood pressure monitors, mHealth apps, and wearable technologies. With the increasing need for interpolation of health data, improved healthcare coordination and robust big data analytics are becoming highly essential. Healthcare is in need of accurate data, real-time insights into patient care, and a better understanding of population health management, big data analytics is expected to gain importance. Read at: http://healthitanalytics.com/news/healthcare-big-data-analytics-driving-billions-in-market-growth

In the present scenario, the retailers who are combining customer analytics, internet of things (IOT) and data innovation to retrieve and analyze data of consumer preferences, are generating the maximum sales. Others are losing out. Customers now-a-days are well aware of their personal data being collected online by firms and hence expect better retail experience in return. That will only be possible when customers are segmented and served, which is done by analyzing their personal data, using data analytics. Big data can also be used in framing a product's optimal price system and inventory management, according to the prevailing or to be prevailed customer preference trends. Read more at:

http://channels.theinnovationenterprise.com/articles/grasping-the-value-of-data-analytics-in-retail

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

Advanced analytics is considered as a game changer in all industries today. The benefits received from its application are tremendous. These benefits can also give the user firms – a competitive advantage- which further gives them a lead in their industry. Gavin Seewooruttun, in his article at abc.net, has listed five top applications of advanced analytics, that will prove to be a winning element for its users. They are:

• Gaining the whole customer view
• Customer micro targeting: upselling and cross selling
• Customer micro targeting: acquisition and churn management
• Customer micro targeting: value addition
• Proactive maintenance

To understand them in detail, please visit the following link:

http://www.abc.net.au/technology/articles/2015/06/04/4248646.htm

Analytics has eventually gained importance in the banking sector. From cost optimization, risk balancing to revenue growth, analytics does it all. Operational analytics: reporting, basic forecasting with data and Advanced analytics: model driven, focusing on the predictive aspects- these are used by the banking sector. Slowly customer analytics and risk analytics are also coming into the picture. These help in revenue growth, investment banking, improving customer experience and save the bank from the uncertainties of the market. Analytics is giving the banking sector well defined strategies, changing paradigms with the advancement of technology. With this evolution of analytics, the need for professionals who can bridge the gap between IT and businesses is immediate. Banks are already employing personnel to read into the data offering growth, efficiency and risk management. Read more at:http://www.businessworld.in/news/economy/analytics-&-banking/1719002/page-1.html

To more than a billion cricket frenzy people in the country and outside, how can cricket be made more interesting? Is there a way organizers of the game can make fans grip to it with increasing fervor? Apparently these are the key points ICC tried to focus on in this world cup. Predictive analytics is what ICC banked on to increase fan engagement. ICC Cricket World Cup 2015 was considered as the most digitally advanced in history. ICC, with support from SAP, came up with the much insightful Match Center through which statistics, comparison techniques which earlier used to be available, albeit not very advanced, to coaches of teams, and commentators were available to any owner of a smart phone with the touch of its app. ICC has increased its use of analytics lately, and will sure rely on it more than less in coming years.

To read more, follow:  http://www.financialexpress.com/article/industry/tech/when-cricket-married-predictive-analytics/58534/ 

A famous chain that oversees the operation of 514 restaurants across the US, has found a way to integrate Big Data with Business Intelligence Services. The system works on data from point-of-sale, marketing, promotions and customer surveys to provide feedback on sales in real time. This not only helps them maintain a competitive edge, but also maximize profits. Big Data was used to select which item to put on the restaurant's menu too. After evaluation of sales, simplicity of preparation, profitability, quality and brand. Only once they met the required target, were they made permanent on the menu of that restaurant. Read more at: http://www.forbes.com/sites/bernardmarr/2015/06/02/big-data-at-dickeys-barbecue-pit-how-analytics-drives-restaurant-performance/

According to Scott Langfeldt (Teradata) “Big data is changing the way the world works- By combining with faster processor speeds and innovative analytic tools- helps in detecting fraud, part failure, and churn.”
For implementing big data and analytics into the company- analytical, business and IT departments need to work together. But they usually have differences.
Scott Langfeldt (Teradata) discussed some ways to solve these differences:
• Categorize your business and mission critical process.
• Make sure that flexibility is built into “business critical” process.
• Create a partnership between the IT developers and analytical teams.
• Develop “business solution” specialist on the analytic team.
• Develop “analytical experts.”
To know more about these ways, follow this link: http://www.forbes.com/sites/teradata/2015/06/02/avoiding-the-drift-into-analytics-oblivion-turning-your-business-into-an-analytics-driven-one/

With the advent of Big Data and technologies that can handle data in real time, Big Data is now reshaping the landscape of security with radical changes in the analytics methods being used. While most security specialists admit that perfect protection is not possible, using Big Data can help in increasing the prediction accuracy of attacks. Generating data for each user signature for example and storing them in NoSQL databases made them scalable. The motivation was to allow relocation of security information and improved monitoring for systems. As companies move from descriptive analytics to predictive analytics, the scope of Big Data in security greatly increases. Read at: http://www.techrepublic.com/blog/big-data-analytics/how-big-data-is-changing-the-security-analytics-landscape/

According to Kevin Mahaffey (CTO at a security firm Lookout) "The more ways we make data more convenient, the more risk there is to access the data and access things without your knowledge". Such is the case with Apple watch. It expands the data set freely over the internet. Not only your location can easily be traced, your heart rate and activities can also be monitored by which your mood and choice can easily be deduced. To know more about the new technology, follow: http://www.cmswire.com/cms/mobile-enterprise/is-the-apple-watch-a-security-threat-029105.php

Rolls Royce, a company that is a leader in the manufacture of engines that generate large amount of power in a high-tech industry has begun to look for Big Data solutions to increase profits and reliability. Its engines and machine parts are fit with scores of sensors that monitor their operation and detect change in real time. Royce now employs Big Data in design, manufacture and after-sales crunching of data. With over 3TB data generated per manufacturing component a year, there is no doubt that there is a need for integration of Big Data. Read more at: http://www.forbes.com/sites/bernardmarr/2015/06/01/how-big-data-drives-success-at-rolls-royce/

The increasing amount of data available to retailers, are giving them quite a sweat, when it comes to extracting the required data from such a large pool. Big data yet again proves to be the savior of the day. Big data helps in segregating consumers, and sending them promotional offers on the basis of their purchasing patterns and location, thus improving 'customer opinion' in the process. It has been observed that customers, now-a-days are increasingly doing online study about their desired product before making the final purchase from offline stores. Big data integrates this data along with the data of actual purchases, including the location of the purchase, to filter the segregation of customers and give them better service. Read more at:

http://channels.theinnovationenterprise.com/articles/using-big-data-to-improve-consumer-sentiment

Customer analytics deals in the evaluation of consumer satisfaction, from the purchase of a product or service. The insights obtained from the data, assists in assessing a company's principle performance indicators, the sales division's performance and in making future sales forecasts. Customer loyalty aids in generating profits for the company. Customer relationship management (CRM) analytics, when applied efficiently, generates the best insights on customer satisfaction. CRM analytics analyses data, ranging from the profile of the consumer to customer feedback, to ensure the best possible results. Read more at:

http://channels.theinnovationenterprise.com/articles/the-secret-to-measuring-customer-experience

Optimizing production for manufacturers with complex operations is not an easy task. There can be volatility in costs and prices, managing multiple plants and figuring out the combination of inputs for products are complex operations. These complexities are abundant in the chemical industry. Advanced data modelling and analytical techniques have helped this industry perform better. Data about companies’ performances can be put into a mathematical model which predicts production under different conditions. The resulting model brought sea changes in the companies’ production decisions, increased plants’ EBIT returns and production capacity. This change was not without a side benefit: better cross-unit collaboration and decisions were made with all constraints and trade-offs in mind. Read more at: http://www.mckinsey.com/insights/operations/taming_manufacturing_complexity_with_advanced_analytics