Online social networks give vital information. If someone posts something on Twitter, Facebook, they generate a digital footprint. If someone reads it, or watches a YouTube video, they add to the data trail. We can analyze it to take business decisions. The information that we look for depends on business goals. Some companies use social media to increase sales, some concentrate on brand consciousness, others are focused on brand trust, or increasing customer satisfaction. An analyst records keywords which customers are discussing, use them into status updates or tweets to be more relevant to customers. They check which messages attract readers most so that they can reach new customers. There are different social media analytics tools. Some are Hoot Suite; span multiple channels, Twitter etc. Some tools include workflow; they allow social media managers to connect information to others. Customer feedback raises sentiment analysis. Read more at: 

http://www.theguardian.com/technology/2013/jun/10/effective-social-media-analytics

Big Data does not necessarily mean good data. And that, as increasing number of experts are saying more insistently, Big Data does not automatically yield good analytics. As everyone realizes, bad data equates to bad intelligence, which equates to bad decision-making and thus equates to bad things happening in your business. If the data is incomplete, out of context or otherwise contaminated, it can lead to decisions that could undermine the competitiveness of an enterprise or damage the personal lives of individuals. So how do we detect that? Firstly it's important to understand where your data originates. Has it been captured by your own work force? What measures were put in place to ensure that the very best job has been done and that the data being captured lives up to expectations? What are the requirements for the data your business needs and uses daily? Do you enhance your data from other sources (external or internal)? An example of how out of context data can lead to distorted conclusions comes from Harvard University professor Gary King, director of the Institute for Quantitative Social Science who was attempting to use Twitter feeds and other social media posts to predict the U.S. unemployment rate, by monitoring key words like "jobs," "unemployment," and "classifieds." To read more about the episode visit: http://www.infoworld.com/d/business-intelligence/big-data-without-good-analytics-can-lead-bad-decisions-225608.

Government today is suspected to be unworthy by most of us. And it is totally justified because of the increasing corruption and selfishness creeping in the society. US president Barack Obama has agreed to keep full transparency between him and the people and prove his credibility. He has signed an 'Open Data Policy' to create an open government. The idea behind is simple. All the government departments are required to update their expenses on the website USASpending.gov. People have full access to this website and can mine the data available for finding any expense that is unnecessary or excessive. Only some areas covering the expenditure of military and covert operations are unlikely to be revealed. Read more at: http://analytics.theiegroup.com/article/538f58943723a83b6f000205/Obamas-Open-Data-Policy

Big data is extending the range of data types in banks that can be covered beyond those common transaction data, and it helps to address problems. Some important areas in banking like fraud analytics, customer analytics and web analytics are also enhanced by big data. Today's improved technologies and frameworks enable banks to get customer data, graph data and geo-location data easily from customers, other banking channels etc. which in turn yields significant insights that can be used in customer marketing, risk management and infrastructure optimization. Big data projects are beneficial as they enhance areas like web security, compliance checks and customer analytics and thus cause the banks to make relevant investments in it. Banks need to know and understand the characteristics of the data they need and need to capture more information beyond risk and marketing data. If the users have sound idea of the nature of the available data, their strategies of making a rough analysis and then use the results to guide them in refining the analysis, will be more effective. This approach helps banks to analyze more data and gain insights that were previously difficult to achieve, without changing the current analytical infrastructure of the banks. Read more about this in Jaroslaw Knapik (Senior Analyst, Financial Services Technology)'s article link:http://www.cloudcomputing-news.net/news/2014/jun/16/big-data-set-to-boost-the-effectiveness-of-analytics-in-banking/

The biggest fallout of the big data revolution -- where every type of business gathers and analyzes data -- is a massive human resources shortage. Across the globe, thousands of data analytics jobs are not filled up because of a shortage of qualified manpower. Data analytics is not coding work but thinking work, described Dinesh Kumar, a professor of quantitative methods and information systems at the Indian Institute of Management in Bangalore. "The potential is huge, but we are behind in creating a talent pool," he said. Quality is a worry, and companies are finding it difficult to recruit top-class people, Kumar said. Data analytics as a job discipline became mainstream almost a decade ago, and the demand for trained professionals has been growing steadily since. Given India's reputation for the availability of professionals in varied disciplines at reasonable costs, global banks and financial services firms were the first to migrate their analytics work to India, followed by pharma and life sciences companies. Global retailers, consumer firms, logistics firms, consultancies, and engineering firms have all begun routing their data analytics work to IT services providers and specialized analytics service providers in India. In India, which has long been a hub for outsourced technology services work, the scarcity of analytics talent is particularly acute, as global companies send increasing numbers of data-related tasks to the country. To know more about this go to:

http://www.techrepublic.com/article/indias-high-demand-for-big-data-workers-contrasts-with-scarcity-of-skilled-talent/ .

According to Gartner, more than 80 percent organizations will fail to develop a consolidated data security policy which will result in potential non-compliance, security breaches and financial liabilities. As big data is transforming the way of storing, processing and accessing data, so to avoid uncoordinated data security policies and security chaos Chief Information Security Officers (CISOs) need to develop and manage an enterprise data security policy which will define data residency requirements, business needs, stakeholder responsibilities, data process needs and security controls. At first they need to evaluate current implementations of Data-centric Audit and protection solutions against data security policies, then they need to find out the gaps in the current implementation and finally review the risks with business stakeholders against those potential DCAP solutions. To do so, CISOs also need to build a good partnership with those stakeholders to develop a new management structure. Read more at:http://www.informationweek.in/informationweek/press-releases/296090/-centric-security-focus-gartner/

Big-data analytics impacts any organization economically, but often data scientists hope for benefits.The reality of where and how data analytics can improve performance varies across industries. Customer-facing activities- the greatest opportunities lie in telecommunications. Here, companies benefit by focusing on analytics models which optimize pricing of services, maximize marketing spending by predicting on where product promotions will be most effective, and identify ways for withholding customers. Internal applications- In industries, like transportation services, models focus on process efficiencies-optimizing routes. Hybrid applications- Some industries need both. Retailers use data to influence next-product-to-buy decisions and to choose the best location for new stores or to catch flows of products through supply chains. Companies operate along two horizons: capturing quick wins to build momentum while keeping sight of longer-term. Open data- swelling reservoirs of external data. Models are often improved combining these data with the existing ones for better business outcomes.. Read more at: 

http://www.mckinsey.com/insights/business_technology/views_from_the_front_lines_of_the_data_analytics_revolution

There are significant opportunities to make use of big data techniques. Unlike technology and consumer retail sectors in which advanced analytics has been implemented, it can also be used in other industries like insurance, health care, banking and public sector. In insurance, data can be aggregated from public sources and specialist data providers, allowing companies to better target customers and frame policies accordingly. Banks are increasingly using big data to generate a much deeper view of their customers, combining the information collected from all of customer's interactions with bank with selective third-party data like paying patterns for mobile phone bills, tracking trends on social media platforms such as Twitter. Read more about this aspect in Dominic Barton (global managing director at McKinsey & Co.)'s article link:http://blogs.wsj.com/experts/2014/03/28/sectors-where-big-data-could-make-an-impact/?KEYWORDS=analytics 

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

Today, companies have the option of collecting information about consumers which was never available in the past. This information is collected through internal sources, such as company websites and sales records, and external ones, such as social media, smartphones and tablets. This vast amount of information on consumers is increasingly referred to as big data. When a consumer visits a website for the first time, cookies are sometimes uploaded on his browser containing a unique ID, making it possible for the company to identify him during his next visits. Once identified, it will be possible to link the consumer to all information the company stored about his profile, which makes personalized marketing possible. Today, because of prescriptive analytics models embedded into their operational systems, websites and apps can analyze consumer information in real time in order to offer personalized travel options instantly. In the next few years, we will witness a gradual move to 1-to-1 marketing in the online travel category, with each consumer treated in a different way in terms of the whole marketing mix. To know more about this visit:

http://blog.euromonitor.com/2014/05/big-data-unique-ids-and-prescriptive-analytics-revolutionising-online-travel-marketing-part-1.html  .

Text mining, which is sometimes referred to "text analytics", is one way to make qualitative or "unstructured" data usable by a computer. Also known as text data mining, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, sentiment analysis etc. Text analysis involves information retrieval, analysis to study word frequency distributions, pattern recognition, tagging, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The main goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. To read more about text mining: http://www.scientificcomputing.com/blogs/2014/01/text-mining-next-data-frontier.

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