In case of an upcoming cyber-attack, there is a chance of huge cash outflow and there is no alarm mechanism which will caution. The only way to find and eliminate the new set of undetectable tools is computer behavioral analysis. AI can adapt itself based on what it sees to both better identify and more quickly eliminate a threat. The scanning can be far more comprehensive, the response faster and the result itself far more secure.

To read, follow: http://www.cio.com/article/2886748/security0/artificial-intelligence-may-save-us-from-new-breed-of-cyber-threats.html

Google has developed a new artificial intelligence system which can recognize the geo-location of a picture. It can also identify a particular monument and area by using sophisticated mapping techniques. This will not only improve an intelligent image search, but also provide a more accurate mapping facility. The system doesn’t exactly spit out GPS coordinates. Instead the team divided the globe into about 26,000 squares of differing sizes based on how many photos from the data set were taken in the area. Huge image search and detailed mapping system is space consuming and particularly troublesome for smaller devices like smartphones and tabs which have space constraints. In order to minimize these issues they are trying to compress huge data within 377mb only.

To know more, follow: http://www.forbes.com/sites/ericmack/2016/02/25/this-google-a-i-can-figure-out-where-that-photo-was-taken/#1bff3c4e34f7

Retailers need to connect, track and collect data from assets and customer shopping patterns. Business intelligence tools have become must-have resources. Now firms became more proficient in modern analytics management, including the front-end requirements for data preparation. The retail industry has not incorporated analytics on a large-scale, and many of the entities have deployed the technology are still in the early stages of use. The starting point for retailers is market basket analysis, which is heavily dependent on third-party data. Retailers are also reported-dependent than other industries. By first getting data preparation and initial archival-related strategies right, retailers can better position themselves for long-term success with their analytics investments. For more read the article written by Jon Pilkington (Chief Product Officer, Datawatch Corp.) at :

http://risnews.edgl.com/retail-news/Retail%E2%80%99s-Latest-Ventures-are-Rooted-in-Analytics104638

Customer information is important for every business. Organizations should have their CRM systems up-to-date. Nowadays, CRM maintenance is easier because of cloud technology. Organizations who depend on on- premise CRM system should also do periodic maintenance. CRM maintenance is important to achieve high levels of data quality and performance. This article by Peter Kowalke (journalist and editor), explores some CRM maintenance basics that every business should perform regularly. They are – Data Cleaning, Data Enrichment, Template and Workflow Review, Authentication and Automation Pruning, Security Review, and   Performance Tuning. Read more at:  http://it.toolbox.com/blogs/insidecrm/six-crm-maintenance-basics-71558

Analytics help retailers move to a unified commerce experience and allow for more targeted, cost-effective marketing. Advanced analytics is used for the optimization of merchandise planning. Real-time analytics is integrated with customer and product information across all channels. Retailers have the ability to use science and algorithms to optimize pricing strategies to reduce markdowns, improve demand forecasting to optimize inventory, localize assortments and optimize space planning. Advances in software and the occurrence of cloud-based solutions enable retailers to think about accelerating their upgrade/replacement cycles. It will help limit overstocking and discounting. With real-time analytics, retailers can market to consumers on a 1:1 basis based on customer context. Retailers have the ability to know what customers have in their closet, purchase behavior and preferences. For more, read the article written by Forrest Cardamenis : 

http://www.luxurydaily.com/analytics-offer-solutions-to-merchandising-and-marketing-problems-brp/

Customer service is the most effective part of any company. It not only helps to improve customer retention rate, but also helps to understand new market trends. Big data analytics and business intelligence play a crucial role here. Simple web crawling into a social site helps to understand the on-going sentiment and current market trend. Different social responses, video hits, ads and newsletters help to optimize the search results. In other word this process facilitates smother recommendation system and customer centred predictive modelling. To make a better prediction we can use –

1. Connectors help to integrate diverse data sets in different formats

2. Lightweight search facilitates even non-technical data analysis

3. Queries optimizes the search dynamics

4. Enterprise search also scores with its semantic search, which means that the context is recognized and included in the analysis and structuring of the data.

Using all these parameters business enterprises can design a better profitability model.

To read, follow: http://www.computerweekly.com/blogs/Data-Matters/2016/02/search-driven-business-intelligence-intelligence-for-a-customer-centric-business-world.html

According to a report, analytics and big data are going to see most vigorous hiring. Established firms and startups are offering handsome income to talented data scientists. Analytics and big data sector have been on consistent growth over last five years and are expected to increase at a compound annual growth rate of (CAGR) of 33.2% and 26.4 % respectively. The demand for data professionals has increased. It has been predicted that in coming times data science is going to have most exciting jobs. Read the full article here: http://economictimes.indiatimes.com/jobs/analytics-big-data-to-see-robust-hiring-high-pay-packets-report/articleshow/51105814.cms

Google, Amazon, and Apple all are tech giants. But apart from being this, they also have one more thing common and that is they all have their data center in rural areas. But this is more of an exception than a rule. Most data centers are located in or close to a city. These giants have done this due to the cheaper rate of lands available in rural areas. But these places lack good telecommunications and power infrastructure. Moreover, there are enough facilities to attract right human capital to such places. It’s better to have a data center in a city than a village. Read the complete article here:  http://www.cio.com/article/3036080/data-center/cities-not-cornfields-draw-data-centers.html

Predictive analytics and statistical analysis are based on the concept of relationships between observed and future actions. When analyzing people, we observe a small sample of data on people and build a predictive model to identify a number of shared traits they have. Micro-targeting is the idea of finding relationships among variables to recognize the target audience's shared traits. It helps to identify the right people. The steps included to predict purchase behavior and design a campaign to expand customer base are: 1. Create dataset. 

2.  Once we have a dataset loaded, we will analyze the people who have purchased the cloud solution and find what they have in common with one another. 

3.  To do this, our first step is to create a dependent variable. 

4. Then, we build a predictive model which takes that variable containing cloud purchase information, and compares it to other variables in our data set. 

5.  The regression model we build then compare our cloud purchase variable to whichever other variables used for analysis, and then gives us statistical correlations for each variable. 

6. Initiating the cloud solution licensing, then identify more people who fit this demographic.

For more read the full article at:

http://www.econtentmag.com/Articles/Column/Marketing-Master-Class/Why-Predictive-Analytics-and-Microtargeting-is-a-Game-Changer-for-Marketers-109377.htm

The most important issue in IT industry is a cyber - attack these days. Advanced security measures and sophisticated firewall systems are not free from bugs. Hence it is not difficult to crack complicated coding and launch a potential cyber-attack. Sometimes it is observed that these attacks take around one month time to get sorted out. So the question is how did these hackers are getting access to data of a particular company? The answer is simple, it's an in-situ type attack, i.e. hackers stay inside the firm and get familiar with encryption pattern, firewall dimensions and sophisticated filters. The solution is to identify these red flags. That can be only managed if all small potential deviations are identified and listed. These create a huge data pool which can be implemented to automate the alert system. By this process small attacks can be taken care off in no time and protect company from higher damage.

To read, follow: http://www.informationsecuritybuzz.com/articles/data-analytics-in-the-early-detection-of-cybercrime/

In healthcare, information comes from multiple sources and it does exist in multiple forms like images, video, text, numerical data, multimedia, paper, electronic records or what not. These all are very important in the healthcare industry. Data mean the world to them.  So it is really necessary to have the right analytical tools to analyze such huge volumes of data. Moreover, jargons used in healthcare are often alien to patients or a layman. Inconsistent definitions, unstructured data is very hard to aggregate and maintain. Hence, if big data analytics used wisely can help save hundreds of billions every year. Read the complete article here: http://www.forbes.com/sites/sap/2016/02/22/can-big-data-analytics-save-billions-in-healthcare-costs/#4fdcfd7f6253

Few news organizations have developed "editorial analytics" approaches that move beyond the generic use. They develop a customized approach to analytics aligned with the specific editorial priorities. Editorial analytics differ from generic approaches in three ways.

First, editorial analytics are customized to the specific editorial priorities and organizational imperatives of a given organization. 

Second, editorial analytics are used to inform both short-term day-to-day decisions and long-term strategic development. 

Third, editorial analytics are always evolving to keep pace with a changing media environment. 

Editorial analytics represent a significant improvement in news organizations’ capacity to understand the media environment in which they operate. It also helps journalists to reach people.

For more read the article written by:  Federica Cherubini (Italian journalist and editorial researcher based in London) AND Rasmus Kleis Nielsen (director of research at the Reuters Institute for the Study of Journalism) at:

http://www.niemanlab.org/2016/02/the-next-step-moving-from-generic-analytics-to-editorial-analytics/

Chemical industry deals with lots of data sets. Some of these data are relevant to product development and the rest of them mostly helps to identify external parameters governing the production process. Generally, big data help to facilitate cost cutting issues. It brings a new product to the market and improves industry environment. Intellectual sensors are integrated with the research and material related informatics. This process minimizes the time required for new innovation. Sensitive sensors can also help to predict the upcoming situation by measuring current situations. Big data works in the back end. This ensures worker's safety by analyzing the toxic level, heart rate, etc. Intelligent sensors diagnose the past chemical data and all possible combinations to predict the new combination. It smoothens the R&D process. Therefore, automation, safety guide and intelligent forecasting are the key mantra for success in the chemical industry.

 To read, follow:  https://www.environmentalleader.com/2016/02/23/how-big-data-is-changing-chemical-manufacturing/ 

Big Data is massive. This article will explore the issues of big data: what it is, how it will improve decision - making, and how to use it correctly.

1) What's the big deal about big data?

Big data is all about three "V's" Velocity, Volume and Variety.

2) How it will improve decision?

# Deliver customer insights.

# Deliver targeted communication.

# optimize performance.

3) How to use it correctly?

# Collect data.

# Clean Data.

# Merge Datasets.

# Analyze Data.

To learn more about big data follow the article by Angela Hausman (PhD) at: http://www.business2community.com/big-data/big-data-like-teenage--01458960

Silent customers create major risks to companies. They don't express their dissatisfaction. Companies can avoid these issues through the proper use of technology with predictive analytics.  Predictive analytics can stop the silent customer attrition by identifying four ways to retain customers:

1. Recognize customers who make a detailed analysis before they determine.

2. Determine the most effective actions to reduce the attrition

3. Distinguish between the best time, message, and channel to reach the customer.

4. Identify the full path to retention rather than one single action.

Companies that adopt predictive analytics to identify who is likely to leave and determine the best plan of action to stop attrition.

For more read :

http://www.information-management.com/news/big-data-analytics/using-predictive-analytics-to-stop-silent-customer-churn-10028295-1.html

Teaching profession is facing lower job satisfaction, unclear idea related to a particular subject. Inaccurate teaching processes are generating slack in the education industry. To manage such issues we can use big data as used in famous movie "Moneyball". It can predict the future outcome by using simple statistics and work according to forecasted outcome. Proper mining could help us to find undervalued personnel with higher potential to attain a high success rate. So in educational context we can segregate major characteristics. These attributes can be:

1. High level of optimism, enthusiasm, sense of humor

2. High standard of expectation from their students

3. Continual self-evaluation

4. Self-criticizing, understanding and updating.

These attributes can be well observed if a bigger pool of educators is created and their continual behavioral attributes are analyzed. In this way future prospective educator’s list can be predicted and deploy them in proper institutional scenarios.

To read, follow: : http://www.brookings.edu/blogs/brown-center-chalkboard/posts/2016/02/03-teacher-moneyball-big-data-analytics-murray

Big data analytics can analyze the past behavioral data of humans. It is highly correlated to future outcomes.  The most important task is to understand how much emotional extent can be predicted accurately. By analyzing critical reasoning and judgmental decision, there will be higher chances to predict more efficient human behavior. Social media plays an important part by providing more sensitive data having higher emotional quotient. Search dynamics also help to predict future trends. Therefore, combining big data, transaction data and social data along with shopping behavioral data opens a new horizon which enables marketers to influence human behaviors and perceptions. To read, follow: : http://www.forbes.com/sites/forbesinsights/2016/02/02/has-big-data-taken-the-human-out-of-human-behavior/#2280493b170c 

These days, faster diagnostic and machine learning on large sets of data promise a real-time understanding of health. Predictive analytics help to decrease costs, and helps in preventive disease management. Precision medicine is an approach to treatment and prevention considering individual variability in genes, environment and lifestyle for each person and also classify people precisely, based on susceptibility, microbiology and/or prognosis, at a considerably higher resolution. Health data analysis provides useful perspectives to predict the future. Precision medicine makes true predictive analytics possible. Prediction requires precision, but it does not require precision alone. Predictability comes from a wide and narrow gap which requires a data sets with high-accuracy. For more read : http://hitconsultant.net/2016/02/22/31535/

Effective data analysis is learned overtime. It takes time, patience and effort. Here are few tips which can help to make the journey of learning smoother.

·         Use simple analysis terms and methods rather than complex algorithms. If your customer and engineers are not able to understand your analysis then all the effort goes in vain.

·         Look for multiple data sources. 

·         Use familiar tools rather than new tools. We should stay updated with the newest technology in market but avoid abundant use of fancy new tools which are difficult to understand. Stick to classics.

·          Provide your insights with the indicators.

·         Clean your data.  Structure it properly. Look for the center, unusual features, spread, and shape of the data. 

·         It is important to move in the right direction rather than spending too much time on definitive answer.

·          Value how actually software works rather than how you understand or think software works.

Read the full article here:  http://dataconomy.com/7-habits-of-highly-effective-data-analysis/

Earlier, organizations need to analyze the impact of external factors on the basis of performance. They used old statistical modelling tools and took months of data collection and analysis and guessed the external factor which had more impact on the business. These models became outdated as soon as they were developed as external data were constantly changing. During the economic crisis, companies did not understand the economy. But, nowadays, as consumer behavior and other useful data sets have become more available, businesses can address challenges and opportunities by improving bottom line profits and helps to generate higher revenue by following correlation of real time data model.  For more read the article written by Rich Wagner (President and CEO of Prevedere) at : http://www.informationweek.com/big-data/transforming-an-antiquated-business-intelligence-process-/a/d-id/1324197