SigmaWay Blog

Today's big data and analytics efforts bring welcome relief to banks, insurance companies, healthcare agencies, non-profits, and other organizations that have habitually struggled with finding the most profitable customers and then selling to them. A new set of analytics reports can move these companies forward in connecting with their best customers. According to a research by Fractal Analytics, banking users are struggling in an industry where 40% of cardholders are inactive and 60% are unprofitable. Banks want to increase spending in its existing credit cardholder base, so that it can implement customer analytics framework that was once targeted at improving first-hand understanding of these customers' needs. Once the bank understood who its most profitable customers were, it developed a "genomic" understanding of how these customers spent their money and found that insurance and food expenses were among the leading "spend" categories. This enabled banks to plan and target promotions built around these major spend areas. By doing so, banks increased its value per customer while decreasing expenses on marketing campaigns, likely because the campaigns were better targeted. To know more about this go to: http://www.techrepublic.com/article/genomic-analytics-build-sales-by-finding-your-most-profitable-customers/ .

Mobile phones serve a dual purpose in the context of Big Data. Each mobile phone, non-smart phones inclusive, creates numerous types of data every day. These include call detail records, SMS data, and geo-location data. In case of smartphones, such devices also generate log data via the use of mobile applications, financial transaction data associated with mobile banking and shopping, and social media data from updates to Facebook, Twitter and other social networks. The volume of portable information and the velocity at which it is made is just going to build as both the worldwide population and cell phone infiltration rates ascent, and the utilization of online networking expands. When investigated viably, this information can give knowledge on client opinion, conduct and even physical development designs. Because of the sheer number of cell phones being used, Big Data specialists can tap versatile Big Data examination to better see such patterns cross over large population and sub-portions of clients to enhance engagement strategies and improve the conveyance of administrations. It gets to be especially valuable for examination purposes when joined together with outside information sources, for example, climate information and investment information, which permit experts to relate macro-level patterns to focused on sub-portions of clients. To read more: http://wikibon.org/blog/the-dual-role-of-mobile-devices-for-big-data/

Big models and big data have long been a feature of weather and climate modelling. Computer-generated global weather forecasts are initialized from millions of diverse observations from satellites, weather balloons, surface weather stations, ships and buoys. Data assimilation, the procedure of ideally mixing these perceptions into the estimate model, is the most computationally difficult part of making a worldwide conjecture, and is a basic component of forecast skill. The international climate modelling community has evolved interesting infrastructure and social institutions that enable a diverse community of interested users to obtain standardized results from leading climate models developed around the world, to capture aspects of climate modelling certainty and uncertainty and help inform decision-makers and the interested public.

Past the thriving information administrations industry, weather has huge monetary and well-being ramifications. Weather Analytics, an organization that gives atmosphere information, evaluates that climate affects more than 33% of overall GDP, influencing the farming, tourism, angling, amusement, and air transport commercial enterprises, to name simply a few. Dubious climate conditions likewise affect little entrepreneurs. Moreover, public safety is of vital concern when officials aim to understand the impact of extreme weather events such as hurricanes, tsunamis, or wildfires. To know more about this aspect go through Per Nyberg (Senior Director of Business Development at Cray)’s article link: http://www.informationweek.com/big-data/big-data-analytics/3-ways-big-data-supercomputing-change-weather-forecasting/a/d-id/1269439

The supply chain is a great place to use analytical tools to look for competitive advantage, because of its complex nature and also because of the prominent role supply chain plays in a company's cost structure and profitability. Data analytics is the science of examining raw data and drawing conclusions about information. It is used by many business houses to facilitate better business decisions and verify or disprove existing models or theories. Relying on traditional supply chain execution systems is becoming increasingly more difficult, with a mix of global operating systems, pricing pressure and increasing customer expectations. There are also recent economic impacts such as rising fuel costs, global recession, supplier bases that have shrunk or moved off shore, as well as increased competition from low cost outsourcers. All these challenges potentially create waste in the supply chain that is where data analytics steps in. To know more about the role of data analytics in supply chain visit:http://www.industryweek.com/blog/supply-chain-analytics-what-it-and-why-it-so-important .

At this point when individuals consider saving rare species, they consider remote jungles, researchers and individuals anchoring themselves to trees. The stereotyped thought is that creatures in the wild are extremely hard to track and that the main way that individuals do this is through a basic following framework with a little specimen making presumptions for the more extensive group. Big Data and the complexities of data analysis could not be further from this, with the collection of massive data sets combined with complex predictive models and algorithms creating insights. The idea that enough data could even be collected to make a useful analysis is hard to imagine.  However, this has changed as of late as HP have collaborated with Conservation International (CI) to make Earth Insights. This system has been intended to give an early cautioning framework for creature numbers amongst jeopardized species over the world. Through the utilization of cameras and atmosphere sensors, the framework can gather information from around 1000 of these gadgets and use it to group data on population numbers. To know more about this aspect go through Dan Worth (news editor of V3)’s article link:http://www.v3.co.uk/v3-uk/news/2318103/hp-big-data-tools-help-wildlife-charity-save-the-planet 

In recent times, the quickest and the easiest way to reach out to the huge market is through paid search marketing like advertising on Google AdWords or through other engines. Research shows that almost 75% of the North American population remain online. Marketing Analytics allows you to monitor campaigns and their respective outcomes, enabling each dollar to be spent as effectively as possible. According to a study in 2008 by the Lenskold Group "companies making improvements in their measurement and ROI capabilities were more likely to report outgrowing competitors and a higher level of effectiveness and efficiency in their marketing". In search marketing, one of the most powerful marketing performance metrics comes in the form of keywords. It is the keyword data contained within each click which can be utilized to inform and optimize business processes, monitor industry trends, provide customer support and identify the product design. A successful online marketing strategy relies on a winning AdWords campaign. The strength of your AdWords campaigns will dictate how well you rank in Google; without a decent ranking, your site will never be seen by prospective clients. As was published by E-Consultancy Research, it's generally well known that "a paid search campaign will add prestige and credibility to an organization. To know more about marketing analytics go to: http://www.wordstream.com/marketing-analytics .

Fraudsters are more competent, better made, and creatively excellent than whatever possible time in the later past. Their adulteration arrangements include complex frameworks of individuals, records, and events. The evidence for these schemes may exist on multiple systems, incorporate various data sorts, and deliberately represent hidden activity. So an analyst has abundant investigative focuses on these frameworks with no true approach to join data or results. To prevent and uncover deception, one needs a solution that is more exceptional and advanced than hoaxers. A basic venture in fraud detection analytics is visualizing the patterns in your data between people, places, frameworks, and events. These data mining and profound analysis capabilities provide more context and better information, enabling more accurate data segmentation and data labelling, which further improves pattern recognition. To read more about it: http://www.21ct.com/solutions/fraud-detection-analytics/.

When Kolkata Knight Riders (KKR) clinched their IPL victory, they must have been thanking the technology that powered some of the decisions right from team selection strategy to competitive analysis. The SAP Game Analytics solution helped KKR to analyze the strengths and weaknesses of each player competing in the IPL, and also helped KKR increase team readiness and performance against their opponents. SAP HANA based platforms - SAP Auction Analytics, SAP Game Analytics, and SAP Lumira enabled KKR to evaluate players during the auction, derive post-game analytics following each of the team's games, and drive fan engagement respectively. Read more about this aspect at:http://www.informationweek.in/informationweek/news-analysis/296068/analytics-helped-kolkata-knight-riders-win-ipl-2014

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 

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/ .