SigmaWay Blog

A firm needs to build a firm base that gradually can destroy others. The amount of information produced by social media platforms is an excellent way to measure a business’s both real and potential value. 56% of marketers named the ‘inability to tie social media to business outcomes’ as the central challenge to measuring ROI from social media. Gauging customer demand prior to launch using social media helps minimize risk, especially for entrants. Social media data can be correlated with an organization’s KPIs to know their impact upon one another indicating its usefulness. Allowing big data analytics capabilities to the social media platform helps firms to cross-reference social data to other data streams from their business. Read more about this article at: http://channels.theinnovationenterprise.com/articles/social-media-analytics

Micro lending is a way through which micro finance institutions (MFIs) provide micro credit to support entrepreneurship in rural areas. According to a Wall Street Journal report, experts have come up with ways that would make micro lending more effective. Instead of providing loans solely for investment purposes, MFIs could also lend for personal expenses if the borrower has the ability to pay back the loan with interest. Also, lenders should employ research to measure the success of the financial programs, making sure that the client not only returns the debt but does so while making a profit and not by selling his assets. Another way to make micro lending effective is to take up a holistic perspective. Quoting Iskenderian “Micro lending will be more effective if there are other safety-net and asset-building products in place—like insurance, savings and pensions—so that families can be secure and repay that loan.” Last but not the least is to make use technology for money transfer and big data analysis of mobile phone usage thereby easing out the whole process and saving time. Read more at: http://online.wsj.com/ad/article/mlf-5-ideas-to-make-microlending-more-effective

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

Big data analytics is the joining of two or more sources of information, so that it can be comprehended from the comparison of the new, expanded data set. Success with healthcare big data analytics relies on vendors, team of experts, and people who understands health management. Collecting and leveraging patient-generated health data from IoT devices will be the key in health care analytics. To know more how IoT helps in healthcare big data analytics, read this interesting article by Jennifer Bresnick (author) at: http://healthitanalytics.com/news/why-healthcare-big-data-analytics-needs-the-internet-of-things 

•  Big Data enables various capabilities like forensics and the analysis of long-term historical trends. By collecting data and analyzing historical trends, you would be able identify when an attack started, and what were the steps that the attacker took to get a hold of your systems. These techniques could play a key role to detect threats at an early stage. Big Data provides  opportunity to consolidate and analyze logs automatically from multiple sources rather than in isolation. This enhances intrusion detection systems (IDS) and intrusion prevention systems (IPS). Integrating information from physical security systems, such as building access controls and even CCTV, could also enhance IDS and IPS to a point where insider attacks and social engineering are factored in to the detection process. This presents the possibility of significantly more advanced detection of fraud and criminal activities. Big Data could result in far more practical and successful SIEM, IDS and IPS implementations. Read more at-         http://www.techrepublic.com/blog/big-data-analytics/how-big-data-is-changing-the-security-analytics-landscape/

Companies want to have an edge using analytics but struggle due to unavailable technologies, outdated software, cumbersome systems, complex integrations coupled with huge costs in infrastructure and personnel. The cloud has transformed the way organizations look at big data and analytics solutions. It has been predicted that investment in cloud over the next five years will grow threefold. • Open source platforms like Apache Spark provide simple and fast data processing capabilities. These though powerful, due to expensive hardware, long lead times are difficult to deploy. The cloud allows immediate usage of open source platforms without initial investments.
• Cloud -based software are user friendly, simple to grasp and sort in comparison to on premise counterparts and release cycles are shorter.
• Cloud-based analytics provide a platform where hard business problems can be effectively tackled.
• Processing and extracting data are easy from a synced centralized location.
• Better connectivity leads to better productivity, deployment of data pipeline is easy. Read more at:

http://insidebigdata.com/2015/05/08/5-reasons-data-analytics-in-the-cloud-will-take-center-stage-in-2015/

Big Data as a Service (BDaaS) means outsourcing various big data functions to the cloud, which includes supply of data, analytical tools to interrogate and analysis the data. Advantages of outsourcing your analytical activities are:

·         Organizations can save money spent on components and infrastructure and time.

·         Storing large quantity of information requires an ongoing investment of time and resources.  But by using BDaaS, organizations can concentrate on business only.

·         When data is stored on a BDaaS service provider server they are responsible for it.

·         Pay only for services you use on the basis of time used or the cost of compliance and data protection.
Read more at: http://www.forbes.com/sites/bernardmarr/2015/04/27/big-data-as-a-service-is-next-big-thing/

According to Stefan Groschupf (CEO of Datameer), big data analytics can prevent healthcare industry from fraud as technology is providing powerful means to catch and prevent perpetrators.  Data-analytics will help in securing patient privacy and mitigating prescription fraud.  According to 2014 analysis of Standard & Poor's 500-stock index companies, it was found that healthcare and pharmaceutical companies have worse security performances. Big data analytics can be used to combine, integrate, and analyze data at once regardless of source, type, size, or format and identify   patterns needed to address fraud and compliance-related challenges. Big data can help to combine multiple data sources, analyze data and quickly deliver insights, pharmacies, doctor offices, and hospitals can track abnormal activity to mitigate prescription drug abuse. Read more at: http://venturebeat.com/2015/03/31/big-data-analytics-can-prevent-health-care-fraud-heres-how/

vRad (Virtual Radiologic) released its latest Radiology Patient Care Indices, which will be available for free and unrestricted use.  It is the U.S.A.'s largest telemedicine company and radiology practice. It enables hospitals, health systems and radiology departments to use normalized data for comparing their own usage of CT imaging in the Emergency Department with similar organizations across the U.S. However, using analytics to improve measuring, benchmarking and proving overall healthcare value is not simple. One must know what to measure, how to measure it, and what benchmark goals to opt and a sound knowledge of radiology analytics. Read more at: http://www.vision-systems.com/marketwired/2014/10/14/vrad-continues-empowering-radiology-with-access-to-big-data-analytics-and-insights.html

According to Jason Bowden (a contributor to Business 2 Community), branding is a means by which a company is able to build their own identity and to communicate what their products and services are. In order to promote branding, brand managers always try to point out that their marketing campaign's focus is on consumers. These brand companies can learn more about their target customers with the help of big data analysis. The entry of data provides plentiful information that helps these companies in determining the various aspects that will yield better profit. Big data provide a window of opportunity for brand companies to know what products their target customers want. By instilling big data analytics, it is easier for marketers to know about their competitors and thus improve their digital marketing. There are other benefits in using big data. To know more about these benefits, follow: http://www.business2community.com/big-data/infusion-brand-marketing-big-data-analytics-0994537

Big data generated by drones is useful in every sector including monitoring data of animal cruelty on farms and surveillance data from military drones. The drones' usage needs a revolution in big data cloud services. However, flying a drone and taking pictures is the first step in data collection process. Since software to reason directly from video feeds is still in a research phase, drone data handling needs to be improved. The use of a cloud-based in-memory computing platform can enhance analytics, processes, and predictive capabilities. Amazon recently proposed to increase sales and revenue by providing the delivery of food using drones. By gathering data on a large scale, service providers will be able to process unique levels of details and turn it into usable information. To know more, go through Abhishek Sharma (author of InfoQ)'s article: http://www.infoq.com/news/2014/09/drone-data-big-data-analytics

Big data is a key infrastructure in the Internet of Things (IoT), but it's far from the only piece of the fabric. In the coming global order, every element of the natural world, and even every physical person can conceivably be networked. Everything will be capable of being instrumented. If you think that the world of driverless cars, robots carrying out maintenance in hazardous locations like oilrigs, or advertising that reads and responds to individuals' unique facial expressions sound like science fiction. As these trends come to fruition, each of us will evolve into a walking, talking, living beneficiary of the Internet of Things. These are all developments happening today and they're prompting a new exciting phase in analytics that needs to be addressed now. Those that embrace data will be more likely to be surfing on top of the wave of creative destruction, instead of having it crash down on top of them.

Read more at: http://blogs.computerworld.com/business-intelligenceanalytics/23447/internet-things-what-it-and-what-does-it-mean-analytics

The launch of big data analytics has brought confidentiality concerns as a rising subject for the customers. Obtaining data is important in order to improve its ability to connect their business to the target consumer by analyzing customer behavior. Casting uncertainties from the consumers on how entrepreneurs use their personal information while collecting data endanger the trust that every business enterprise should build. The consumers are aware of the value of their private data. To a reasonable extent the consumers are willing to share. Privacy is more important to the consumers and they expect marketers to give it owing admiration. Businesses that do not respect the privacy of their customers will be likely to mislay their faith. some strategies to be taken to re-establish the trust of customers between business and the customers. Many customers are prepared to share their personal data if they are guaranteed of their privacy. Read more at: 

http://www.socialmediatoday.com/node/216026

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 

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/

With the advent of healthcare industry, the flow of data has increased by leaps and bounds. Now with the presence of analytics, huge number of unstructured data can be easily analysed to find out patterns and behaviours which in turn helps the companies associated with healthcare to take more sound and logical decisions.

To know more kindly visit:-

http://www.healthcareitnews.com/news/healthcare-data-goes-big-great

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

Big data analytics is a strong process to enrich any business process. Ford automobile company is applying big data analytics to improve their business. In doing so, they polish the metrics from the company's best processes across myriad manufacturing efforts and through detailed outputs from in-use automobiles-- all to improve and help transform their business. Big data is allowing deeper insights in improving processes, quality control, and customer satisfaction. To know more on this topic, go through the article by Dana Gardner, president and principal analyst at Interarbor Solutions.

http://www.zdnet.com/ford-scours-for-more-big-data-to-bolster-quality-improve-manufacturing-streamline-processes-7000010451/