Video Analytics identifies events or patterns of behaviour through video analysis of monitored environments. It monitors video streams in real-time and automatically creates current security alerts and analyses historical data to identify specific incidents and patterns. It helps agencies to organize, analyse and share the insight gained from data to make smarter decisions and enable enhanced coordination.

Using a combination of algorithms, video analytics analyses captured video in real time and presents alerts about whatever the application is programmed to identify. Today's video analytics applications are able to do much more than just identify motion, and false alarms have been reduced to negligible rates as they can automatically filter out motion caused by wind, snow, rain and change of lighting. Some applications now also have the ability to detect tampering, and can automatically adjust the visual parameters of enabled video cameras according to individual scene characteristics to ensure optimal brightness and contrast for video viewing and recording. To read more about the evolution of video analytics read here: http://www.sourcesecurity.com/news/articles/co-2173-ga.2504.html 

Sampling error can cause problems if they are not taken care of. Errors in judgment about sample size can be fixed easily and sample sizes must be considered seriously if big data is being used for predictive analysis. A leader trying to use big data in predictive analysis should always consult the data scientist. The way to understand whether enough data has been collected or not for the purpose of prediction involves understanding the tolerance of the risk associated to accept the assumptions drawn from the sample size characteristics. There are two types of risk: the risk that you're going to take some action when you shouldn't and the risk that you are not going to take some action when you should. Also enough information should be available about the sample variation and precision of measurement to know whether enough data has been collected to make prediction. To know more about importance of sample size in predictive analytics, go to John Weathington (President and CEO of Excellent Management Systems, Inc.)'s link: http://www.techrepublic.com/blog/big-data-analytics/why-samples-sizes-are-key-to-predictive-data-analytics/ 

Unlike other retailers, Nordstrom (a fashion speciality retailer), wanted to learn more about its customers like how many came through the doors, how many were repeat visitors. The company started testing new technology that allowed it to track customers' movements by following the Wi-Fi signals from their smart phones. Nordstrom's experiment is part of a movement by retailers to gather data about in-store shoppers' behavior and moods, using video surveillance and signals from their cell phones and apps to get information as varied as their gender, how many minutes they spend in the candy aisle and how long they look at merchandise before buying it. If a consumer looks for Wi-Fi network, a store that offers Wi-Fi can pinpoint where that particular shopper can go and get Wi-Fi connection within a 10-foot radius. Stores can also recognize returning shoppers as mobile devices send unique identification codes when they search for networks. This means stores can now tell how repeat customers behave and the average time between visits. Read more at-http://www.nytimes.com/2013/07/15/business/attention-shopper-stores-are-tracking-your-cell.html?pagewanted=all&_r=0/

This article elaborates on the sentiment analysis from tweets using data mining techniques. Instead of using SQL, it shows how to conduct such analysis using a more sophisticated software called RapidMiner. It explains how one can extract Twitter data into Google Docs spread sheet and then transfer it into a local environment utilizing two different methods. The emphasis is on how to amass a decent pool of tweets in two different ways using a service called Zapier, Google Docs and a tool called GDocBackUpCMD, along with SSIS and a little bit of C#. Zapier is used to extract Twitter feeds into Google Docs spread sheet and then copy the data across to local environment to mine it for sentiment trends. Next, it is shown how this data can be analyzed for sentiments i.e. whether a concrete Twitter feed can be considered as negative or positive. For this purpose, RapidMiner as well as two separate data sets of already pre-relegated tweets for model learning and Microsoft SQL Server for some data polishing and storage engine. Read more at:http://bicortex.com/twitter-sentiment-analysis-mining-twitter-data-using-rapidminer-part-1/

While taking decisions on buying and selling in the bull market, very few follow the various technical indicators that are available with any charting software. These are some vital signals which people choose to ignore while making decisions on the market. But, if statistics are to be believed they are helpful in making profitable decisions. To know more kindly visit:-

http://www.forbes.com/sites/greatspeculations/2014/05/05/taking-the-technical-vital-signs-of-an-aging-bull-market/

Internet search engines like Google, Yahoo or Bing are like large knowledge repositories - they provide you with any information you want. But, there is a condition - you have to type right keywords. Everyone one of us knows this.

How many times have you got the right search results you expected? It can be never 100% correct, even though you thought of the keywords carefully. Here comes the accuracy of the search engines, and improving this accuracy between what you type and what results you get is of utmost importance to the search engine providers. Behind the wall, lots of text analytics are involved.  What you normally type in the search box is an unstructured content and the search engine's job is to analyze, extract meta data and index it to convert the content into a structured one. The query is made upon the indexed data, after which the results are shown to you. What makes search engines different is the ability to add context, extract meta data from unstructured content, and index them so that accurate search results are shown to the extent possible.

What text analytics are involved here? Know them here: http://www.business2community.com/big-data/text-analytics-important-search-0889955#!PueWW .

The way technology is making a progress and competition is becoming tougher with increased uncertainty, big data and analytics tools have become important to your company. There is another matter that is also important - your customers. So, which will you give more importance? Your analytics tools or your customers? It is of no doubt that big data and analytics tools will give you hidden information that is not visible easily, by sifting through customer data. But, you have to remember one thing - analytics tools have no brains. It will never ever answer 'why' - well, that is the biggest answer for your business. The reason - analytics tools never understand your customers!

To attract customers to your shop, or to your website, you have to build an experience within them. Your marketing communication strategies will form that experience and reshape them. You have to address questions like what your customers want and why they want - in short, you have to understand their behavior. So, to excel in business, it is more important to understand your customers, not analytics tools.

How to understand your customers' behavior? Read them at: http://thenextweb.com/entrepreneur/2014/05/21/beyond-analytics-understanding-humans-scaling-business/ .

For emerging markets, exploring data to predict trends is not an easy option, mostly because of less availability of data in new fields. However, organizations in the emerging markets have the option of gradual progress. They have to start with analysing small data and then there will be scope to move into big data in near future. Problems for companies trying to do business in these areas are limited access to information and appropriate analytic modelling techniques. Gartner reports that companies are predicting 40 to 60% of their growth in revenues will come from emerging markets over the next decade, and McKinsey & Company predicts that in 12 years, 57% of the nearly one billion households with earnings greater than $20,000 a year will live in the developing world. "Companies need analytics to cope with these new markets," said Richard Howells, vice president of Go to Market for SAP. To know about this aspect, please visit the following link: http://www.techrepublic.com/article/start-with-small-data-in-emerging-markets-to-get-to-big-data-analytics/#. 

The term big data refers to large amount of customer information gathered from social media, which helps companies to improve sales and services by analyzing those data. But unfortunately small businesses have limitations while analyzing big data. Also the huge volume of data may be confusing for small organization and some time they don't know where to begin. Social platform gathers information which can be important for organisations. Hence tools provided by Twitter, Facebook, LinkedIn, are a good start as they offer low start-up investment as per Evan Greenberg, CEO of marketing and communications firm Allscope Media. To read more about how big data can help businesses to think outside the box, follow Nicole Fallon's article in this link: http://www.businessnewsdaily.com/6190-smb-big-data-tips.html

Companies are using data analytics intensively and they should remember that to make money out of it and enjoy profits, they have to measure it.

At VentureBeat’s Databeat 2014, experts were of the opinion that companies are not using analytics to its full potential. Data scientists must be engaged with the organizational activities for providing support and insights, based on which the concerned employees can react. In the background are various social networks. By using the right social media tools, they can get a view about their operations from a real – time perspective. They can generate dashboards and graphs which will provide them valuable inputs in terms of revenues. Apart from this, it can be an effective marketing tool also.

All this will zero in the ultimate thing – increase the profitability. But, for implementing analytics, cultural change in the organization is also necessary.

Read at http://venturebeat.com/2014/05/19/some-simple-but-important-reminders-on-how-user-analytics-can-help-you-make-more-money/  to know what the experts told in the conference.

Today, every company is talking about Big Data, but there is also an important thing to consider - that is organizational culture. As high technology companies are embracing Big Data, the direct beneficiaries will be their IT professionals – who will have the opportunity to outshine themselves. Companies need to look for new generation talents who can handle latest challenges keeping in tune with the advancement of Big Data technologies.  With bright futures ahead, big data can make a scope in every aspect – be it scientific research, medical science, or any data driven industry, companies and their IT professionals can see a silver line. To see increased productivity and boost profitability, what is required is the right mix of big data tools, vendor support and IT talent team.

To know how organizations can earn the feather cap by using big data analytics tools, taking alongside the cultural change, read at http://midsizeinsider.com/en-us/article/big-data-analytics-success-through-cult .

To manage your sales upwards, you need to click your senses. Have you ever thought, that much talked Big Data is also a part of your ‘sense’?

The way big data and analytics are making a progress, especially in marketing and to increase sales, it can no longer be ignored. Your buyers are now more informed about your products and services and you have to dig in deeper to gain new insights about the mind and heart of your customers.

That’s where big data comes in effect. Lots of customer data are now available and without going hither and thither, you can mine for gold. Yes, gold – because customer data is like an asset to you depending on which you can gain that ‘new insight’ to keep yourself ahead of competition. Click on http://www.entrepreneur.com/article/233969  to view on how big data can raise your sales. Who may say, you may be the next beneficiary of leveraging the power of Big Data!

Giving constant connectivity to customers and ensuring customer satisfaction are two most important things for telecom companies. As an owner of telecom company, you have to ensure that customers lose their call to as minimum as possible to prevent business loss and inconvenience to them. 

For this, you have to analyze every moment and take preventive actions whenever any faults occur. It is necessary that you must have analytics tools for it. But, unlike in most business cases, you will not able to use traditional machine data analytics. It is of no doubt that you will get patterns and trends, and will be able to evaluate program performance and pinpoint defects – but all these after the fact. By that time, you may lose your customers and revenues costing millions!

What you should do is do a real time monitoring and real time analysis as well. Using ‘streaming big data analytics’, you will be able to collect and correlate streaming data at a customer’s specific location, cell site performance, call failure rates and event prevent momentary glitch before they turn up into a large scale event. Not only this, based on profile of your customers, you can prioritize their issues. All these will increase one of your important parameters – customer satisfaction.

Read more at: http://smartdatacollective.com/dale-skeen/199716/why-telcos-can-no-longer-rely-traditional-machine-data-analytics-deliver-high-qual  to know how streaming data analytics help telecom companies to monitor calls in real time and increase customer satisfaction.

As businesses are becoming tougher and becoming more competitive, usage of advanced analytics is on rise. With the help of it, companies have increased their marketing effectiveness and they are able to generate more revenue out of the cost spent.

But, one thing has to be accepted. Deploying analytics tools in an organization is not an easy task. One of the main concerns is scalability – whether analytics will be utilized on a large scale or a small scale, because, depending on that, cost will vary; and if you choose the wrong one, it will eat your revenue. Change in organizational culture is also noteworthy – those who are not yet oriented to analytics, may face difficulty in adopting the technology.  In spite of all these, analytics have proved to be tremendous successful for marketers. Given in the present dynamic situation, CEOs must learn to adopt analytics and should motivate his or her marketing organization to use. Click on http://www.forbes.com/sites/forbesinsights/2014/05/19/an-advanced-marketing-analytics-cheat-sheet-for-ceos/?ss=business  to know the tips for CEOs.

Calpian Commerce (the U.S. payment processing division of Calpian), has launched the SecurePay Insights merchant analytics platform. It helps businesses to leverage value-added data and analytics to improve their businesses. Calpian Commerce's SecurePay Insights (powered by Womply, a San Francisco-based technology company) provides real-time vision into revenue, online reputation, social media performance and competitive intelligence on a web-based dashboard.

To read more, visit the following link:http://www.marketwatch.com/story/calpian-commerce-launches-securepay-insights-merchant-analytics-platform-powered-by-womply-2014-05-07?reflink=MW_news_stmp