SigmaWay Blog

Every financial and social interaction includes credit information, current & past borrowing behaviors and the ups & downs of a company's cash flow. Nowadays, business owner obtains an alternative loan based on data stream which is easier. Lenders look at many things when assessing the total collection of data from a business:

• Marketing data

• Omni-channel content creation and 

• Social media activity, content, shares and interactions

These factors will help in creating opportunities for short term loans and other types of business lending. 

For more read the article written by Sean Mallon ( Blogger) : http://www.smartdatacollective.com/seanmallonbizdaquk/398935/can-big-data-help-your-business-get-funding

Graphs are not just nodes or links. They are powerful data structures anyone can use to represent complex dependencies in their data. Graph applications are used in various places ranging from cancer research to large-scale cyber threat detection to collaborative filtering recommendation systems. In the world of data-intensive analytics, memory bandwidth is the primary performance restrictor. Because graph algorithms display non-locality and data-dependent parallelism. When you crisscross a large group, you are constantly asking for from main memory. For these problems, GPUs provides superior bandwidth to memory and can deliver significant speedups over CPUs. GPUs are very fast for graph processing and analytics, where memory bandwidth is a problem. The memory bandwidth of GPUs provides a new way to speed up data-intensive analytics and graph analytics. For more read the article written by Brad Bebee ( CEO, Blazegraph) : https://devblogs.nvidia.com/parallelforall/gpus-graph-predictive-analytics/

Computer systems are quite similar to living organisms. The living part of the system is not so much the hardware or even the software it is the data. We seek to build computer systems based on ourselves and take this as models. Non computer science based behavior patterns and structures provides a language for discussing the patterns, behaviors and structures of systems. An artificial neural network is inspired by the biological nervous systems, such as the brain. It is composed of a large number of highly interconnected neurons to solve specific problems. An Artificial Neural Network or ANN is configured for pattern recognition or data classification. For more read the article written by Bruce Robbins (CEO, Xcipi) : http://www.smartdatacollective.com/bruce-robbins/397616/data-alive-through-deep-learning-and-intelligence

According to a new study by Cisco “Security in India: Enabling a New Connected Era”, 72% of the Indian companies rely strongly on web Security whereas this figure is 52% for non-Indian companies. This means Indian organizations rely more on tools rather than strategies for protection. Therefore, reliance on tools may also put them at risk. Around 65% of the Indian organizations use mobile security against 49% of the non-Indian organizations. Indian organizations are also rapidly using cloud base security. Use of cloud security system in the coming times will lead them to threat problems just like other countries - China, Russia and U.S. These countries use advance persistent threat (APT) to steal data from consumers and competitors. To know more read article of TOI 

http://timesofindia.indiatimes.com/tech/it-services/Indian-companies-use-strong-defence-tools-against-cyberattacks-Cisco/articleshow/50697170.cms 

Social media refers to the use of virtual social networks, peer review and other social tools in purchasing process. According to a study, 64% of smartphone users use their phone to shop - and most of them look for reviews and recommendations from a number of sources to help them make their buying decisions. Reviews in social media affect consumers of different ages and races. Shoppers embrace the Internet for a variety of reasons like convenience, price, and selection. Read more at:

 http://www.emarketer.com/Article/How-Social-Media-Influences-Shopping-Behavior/1013718

Cryptology plays a crucial role in cyber security. There could be use of sign language and different cypher code to encrypt the sensitive data. The decipher mechanism helps to decode the message. In ancient times, Julius Caesar used to send his secret messages to his army in cypher code. Later this process is followed by Germans during the Second World War. In modern organizational context, cyber security is the most important parameter. So, to protect organizational data they can adopt cryptology to encrypt their data and hide them from the normal visibility level. The reader can go through the article written by Mark Ward (Technology correspondent, BBC News) and understand more about cypher process and the levels of challenges at:

http://www.bbc.com/news/technology-34312697

Exchange traded fund (ETF) is a fund that trades on an exchange like a stock. Some of the features of bond ETFs in the market place are:-

• The market price of these shares will fluctuate as the collective price underlying securities.
• Due to uncertainty of conditions investors sometimes sell shares at the same time which results in crashing the market price. Market of these shares is very volatile.
• There is a complete transparency in pricing, centralized trading, and visibility to all as a result there is more connectivity between buyers and seller. Therefore, trading ETF bonds is less complicated and more efficient as compare to individual bonds.

If you wish to read more on ETFs, click on the link below for full article by Matthew Tucker: http://www.investopedia.com/partner/blackrock/articles/investing/041516/what-happens-bond-etfs-stressed-markets.asp

The challenges faced by the insurance industry are same as other companies. Insurance industry involves generating, storing, and making large & complex sets of data to create efficiencies and improving their bottom line. In a recent survey, 54% of 48 U.S. and P/C insurance executives said they use predictive modelling for underwriting/risk selection, and that usage is expected to grow by 40% over the next two years. Predictive modeling helps in claims triage, underwriting appetite and strategy, market-share analysis, and litigation propensity. Predictive analytics can boost companies' profitability by: 1. Developing a clear analytics roadmap across business units. 

2. Monitoring their outputs against what is happening to avoid the situation where underwriters push back on predictive models.

3. Developing an enterprise-wide model monitoring program to ensure models are recent and recalibrated on a consistent basis. 

4. Looking outside the industry to see how other organizations measure ROI.

For more read the article written by Loren Trimble and Michael Kim(Contributors) : http://ww2.cfo.com/risk-management/2016/03/predictive-modeling-can-make-insurers-better-risk-managers/

The need for wearing body cameras for police personnel has increased. Police departments are also taking the help of specialized data mining solutions to predict and prevent misconduct. The problem with this, is that, it leads to officers being treated differently based on actions they are yet to take, and might never take at all. Predictive analytics is playing an important part in modern policing and in ending crimes which are less serious than police misconduct. For more read the article written by Graham Templeton ( Writer ): http://www.extremetech.com/extreme/224560-new-analytics-can-predict-and-possibly-prevent-police-misconduct

Nowadays, new solutions are based on IoT, analytics, and business intelligence and we are surrounded by more applications of big data, analytics, sensors, and business intelligence. According to Mary E. Shacklett (president of Transworld Data) writes in her article about some examples of behind-the-scenes technology that is redefining the world. Some of them are - 1: Weather and political prediction systems for global supply chain and financial market impacts

2: Merchandising

3: Police suspect identification

4: Robots for cleaning your floors and your carpets

5: Medical patient demographics

6: Helping the blind see

7: Crime scene forensics

8: Building information modeling (BIM)

9: Self-driving vehicle systems

10: Inventory management . To know more, read the article written by Mary E. Shacklett (president of Transworld Data) : http://www.techrepublic.com/blog/10-things/10-ways-big-data-analytics-and-sensors-are-helping-behind-the-scenes/

Previously, CRM was unable to provide insightful data on customer behavior. The Internet of Things (a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifier) changed the scenario. The CRM providers have been inspired by IoT and they are successful to improve the customer relationship and meet customers' needs. This new trend is known as CRM of Things. It creates new marketing opportunities for businesses, and helps to improve customer service. Read more at written by Inside-CRM : http://it.toolbox.com/blogs/insidecrm/how-crm-can-benefit-from-the-internet-of-things-71582

The goal of customer-centric CRM is to improve customer value. Every customer is unique and their needs are also different. Organizations should customize their services and products according to customer needs, for gaining customer loyalty. A customer's contribution towards the net profit of a company determines the customer value. Customer loyalty is the most important thing for any company. Surveys will them to measure loyalty. Customer identification and prioritization is also important as it helps to identify customers that provide the most value. There should be well-planned strategies for customer acquisition and retention. Customer-centric CRM can improve revenue, decrease costs and enhance the customer experience of a company. For more read article written by Inside-CRM : http://it.toolbox.com/blogs/insidecrm/crm-and-the-concept-of-customer-centricity-71614

To understand the crime pattern, data are used by police from the past. The objective is to predict acts of terrorism based on the large amount of surveillance data. The technology will collect data on ordinary citizens, including information on their jobs, hobbies, consumption habits, and other behaviors and will flag unusual behavior that could signal a potential terrorist attack. Police in both Los Angeles and Manchester ran trials using a computer algorithm to predict where crime would most likely to take place. Their aim was to find patterns in criminal behavior by analyzing crime data to prevent crime through predictive policing. There is a need for research into machine learning and other artificial intelligence technologies to identify human faces in surveillance video. The police can analyze large amounts of crime data, i.e. 'big data'. Enterprises are now using big data technologies to detect and fight cyber threats. Big data analytics and shared threat intelligence can increase the organization's effectiveness in security measures. For more read: http://www.cxotoday.com/story/how-big-data-can-be-used-to-predict-crime-patterns/

The Internet of Things (IoT) generates semi-structured or unstructured data in real time. Organizations take advantage of cloud because big data can be best managed in the cloud. By utilizing fog computing, organizations can decrease time to action; reduce costs, infrastructure and bandwidth; and can get greater access to data. The advantages of the decentralized method of fog computing and IoT analytics cover both the organization and the end user. One of the benefits of centralization is to focus and understand the data location and the accessibility. The decentralized method is associated with flexibility and agility. This tends to describe the data management trends and applications. Read more at the article written by Jelani Harper (blogger) : http://data-informed.com/the-internet-of-things-and-the-necessity-of-fog-computing/

The development of mobile, IoT, and the cloud has increased the need of analytics to solve challenges in the customer, product, operations, and marketing domains. The established companies need to restructure their business and technology to increase their sales. Organizations need to involve cross-functional teams to establish data governance. Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. Now in the era of Analytics 3.0, when tools make decisions and measure the impact. For more read the article written by chandramohan Kannusamy (Technical Architect) : http://data-informed.com/analytics-3-0-and-data-driven-transformation/

The best way to improve the probability of desirable outcomes is to predict the unknown future results. Predictive analytics help organizations to become forward looking and proactive. It uses a number of predictive modelling and analytical technique for the prediction of the future. One of the modelling techniques is a response model which doesn't predict influence. It only predicts the desirable outcomes of one method without making any prediction of alternative method. Healthcare organizations will be more successful if the predictions for treatment decision results in the desired outcome. For more read the article written by Eric Siegel (founder of the Predictive Analytics World Conference):

http://data-informed.com/drive-influence-with-uplift-modeling/

A proposed framework focuses on the development and application of electronic healthcare predictive analytic (e-HPA) applications. The framework is built on "earlier frameworks of model development and utilization" to show opportunities for e-HPA, ways to address challenges and ideas for motivating stakeholders to adopt and refine the framework.

Five areas provide the structure for the framework, which includes:

1. Data barriers and model development

2.  Transparency and model evaluation

3. Ethics

4. Regulation and certification

5. Education and training. 

It is the task of healthcare leaders, e-HPA practitioners and other stakeholders to ensure an infrastructure .The infrastructure promotes effective use of predictive analytics to improve patient outcomes, satisfaction and the value of healthcare resources. For more read the article written by Katie Dvorak(an associate editor for FierceHealthIT and FierceHealthcare) at : http://www.fiercehealthit.com/story/proposed-framework-focuses-electronic-healthcare-predictive-analytic-applic/2016-03-08

Cyber security is the biggest issue for the business enterprises who is in the field of networking and cyber space. But, problematic situation is arising where there is a mismatch of investment in cyber security than the growing threats in cyber space. This indicates that most of the organizations are either not aware about the upcoming threats or financially reluctant on investment. Security budget is not easy to predict and more skillful persons are required to predict the upcoming threats. 

To read, follow: http://www.computerweekly.com/news/4500279662/Cyber-security-budgets-not-rising-in-line-with-threats-say-security-pros

In order to make management decisions, all companies in various industries work on large transactional data. For example: healthcare, retail, pharmaceuticals and so on. Some of the benefits big date provides:-

1. Big data analytic makes operations more efficient and frequent.

2. It appreciates the quality of services by analyzing the whole data and not just sample subset. As a result, the companies make more precise decisions.

3. Marketers target the right customers using big data. Through focusing on data based strategies

4. It gives an opportunity to new companies because they can analyze the existing industry data.

5. It also gives a competitive edge to those companies who use it decision making.

To read more: http://timesofindia.indiatimes.com/tech/it-services/How-Big-Data-is-changing-the-way-we-do-business/articleshow/50417589.cms

A.I is like a magic to people at times where machines are doing super cool stuff like humans. How do they do it? People have associated many misconceptions with A.I. So here are few things which people get wrong about A.I. : Automation is not A.I. ; Humanoid robots are not the face of A.I, computers are; A.I. is about intelligence rather than consciousness;  A.I is not all tech; Biggest myth buster will be this - TERMINATOR is NOT future A.I.; Misunderstanding of Machine learning and; Underestimating the power of A.I. Read the full article here: http://www.techrepublic.com/article/the-7-biggest-myths-about-artificial-intelligence/