SigmaWay Blog

Statistics is everything now days. We believe in numbers not in assumption. How digital marketing is so relevant these days and why can be answered by all these seven key statistical figures.

1. According to “Brandlive”, 44% out of 200 executives use live video streaming activities to generate leads. 25% of them has evidenced a progress.

2. The social network said 3 million businesses have now advertised on its platform, up from 2.5 million just six months ago. A year ago, Facebook had 2 million advertisers.

3. Internet connectivity in the smart phone section has increased by 200% in one year

4. BuzzFeed chief Jonah Peretti said the viral site now has 6 billion monthly content views, up from 100 million in 2012.

Artificial intelligence is like machine learning. But they are completely different and some concepts related to it has to be cleared.

1. Artificial intelligence refers to a broad set of methods, algorithms and technologies that makes the software 'smart' that seem human-like to an outside observer.

2. Machine learning has some elementary engineering sensibility whereas artificial intelligence is more correlated to automation and sophisticated data handling.

3. Machine learning covers multiple technology, whereas AI is a process which only refers wide variety of algorithms and methodologies that enable software improvement.

4. Machine learning is more into deeper analysis, segmentation and networking in contrast to standalone operation of artificial intelligence

Wearables are now more than a gadget. With the latest designs, it is becoming a fashionable accessory. Wearables with fashionable accessories in a range of sizes, styles and finishes, screens, leather, semi-precious stones and 18k gold is not for men now. Wearable production companies are now catering their product also to women, especially the working class. To know more, please read the article by Sophie Charara (Writer at Wearable) -: http://www.wareable.com/fashion/wearable-tech-fashion-style

Apache Arrow, a new open source project, is designed to provide high performance in-memory analytics across different systems. It eliminates cross system communication overhead cost and can improve performance 100 times. It is based on the code of Apache Drill. Developers from other Apache projects. It removes the burden of serialization and deserialization of data. It also supports complex data in addition to relational data. Read the complete article here: http://www.cio.com/article/3034279/big-data-gets-a-new-open-source-project-apache-arrow.html

Nowadays, manufacturers are using technology to monitor parts and operations in order to reduce expenses which includes analytics. The result is, a continuous streaming analytics engine that provides automated alerts on the forecasted failures identified by the predictive models. From here, companies can schedule planned maintenance with a high confidence level. Predictive monitoring is particularly suited for critical machinery which is complicated to maintain in terms of labor costs, or machinery with components that are expensive or difficult to obtain quickly. Spare parts and components can be reduced because needs are predicted weeks to months in advance. Read more at :http://www.designnews.com/author.asp?section_id=1386&doc_id=279921

EMR (electronic Medical Record) adoption, big data and other trends are helping a lot in the generation of data in the healthcare industry. But data is not what drives the healthcare industry- managing this data does. In healthcare, data analytics is done in use cases. Multiple use cases are created according to the need like one while patient got admitted in some department, a use case gets created. To provide best services to patients timely, immediate access to patient’s data without the barrier of time or location. Read the full article here: http://www.computerworld.com/article/3038315/data-analytics/accelerate-time-to-value-with-healthcare-discovery-analytics.html

The National Biotechnology Development Strategy 2015-20 plans to make India a world-class bio-manufacturing hub, with intent to launch big missions, creation of new biotech product and creating a strong infrastructure for R&D. The catalyst in this mission is harnessing the power of big data. IT and healthcare have always worked together. Big data and data analytics have helped a lot in cancer research, drug safety, genomics and clinical research. Big data helps in solving the complexities of healthcare that looked impossible to solve before. It helps in making sound decision within a short period of time and with cost effective measures. The error rates have also decreased enormously leading to a significant increase in efficiency. Read the full article here: http://tech.firstpost.com/news-analysis/national-dept-of-biotechnology-sees-big-data-propelling-it-to-100-bn-industry-by-2025-293213.html?utm_source=also_read

Artificial intelligence opened a new horizon to give reply to E-mail instantly. Reading long emails in a busy life is a tough. So an automated answering system can reply a mail by analyzing the tone of a particular mail. A particular word based sentiment analysis can be a useful tool to facilitate this process. Google has already started to offer 'robot email' AI in its Google Inbox online extension for Gmail. The new service is called the Smart Reply and it will help answer some of your emails for you. There are still some constraints. This automated system has some issues regarding long multi sentimental messages. These messages has to be replied manually. 

To read, follow: http://www.forbes.com/sites/adrianbridgwater/2016/02/18/can-artificial-intelligence-answer-our-email/#4e1fd3197bc2

In case of an upcoming cyber-attack, there is a chance of huge cash outflow and there is no alarm mechanism which will caution. The only way to find and eliminate the new set of undetectable tools is computer behavioral analysis. AI can adapt itself based on what it sees to both better identify and more quickly eliminate a threat. The scanning can be far more comprehensive, the response faster and the result itself far more secure.

To read, follow: http://www.cio.com/article/2886748/security0/artificial-intelligence-may-save-us-from-new-breed-of-cyber-threats.html

Google has developed a new artificial intelligence system which can recognize the geo-location of a picture. It can also identify a particular monument and area by using sophisticated mapping techniques. This will not only improve an intelligent image search, but also provide a more accurate mapping facility. The system doesn’t exactly spit out GPS coordinates. Instead the team divided the globe into about 26,000 squares of differing sizes based on how many photos from the data set were taken in the area. Huge image search and detailed mapping system is space consuming and particularly troublesome for smaller devices like smartphones and tabs which have space constraints. In order to minimize these issues they are trying to compress huge data within 377mb only.

To know more, follow: http://www.forbes.com/sites/ericmack/2016/02/25/this-google-a-i-can-figure-out-where-that-photo-was-taken/#1bff3c4e34f7

Retailers need to connect, track and collect data from assets and customer shopping patterns. Business intelligence tools have become must-have resources. Now firms became more proficient in modern analytics management, including the front-end requirements for data preparation. The retail industry has not incorporated analytics on a large-scale, and many of the entities have deployed the technology are still in the early stages of use. The starting point for retailers is market basket analysis, which is heavily dependent on third-party data. Retailers are also reported-dependent than other industries. By first getting data preparation and initial archival-related strategies right, retailers can better position themselves for long-term success with their analytics investments. For more read the article written by Jon Pilkington (Chief Product Officer, Datawatch Corp.) at :

http://risnews.edgl.com/retail-news/Retail%E2%80%99s-Latest-Ventures-are-Rooted-in-Analytics104638

Analytics help retailers move to a unified commerce experience and allow for more targeted, cost-effective marketing. Advanced analytics is used for the optimization of merchandise planning. Real-time analytics is integrated with customer and product information across all channels. Retailers have the ability to use science and algorithms to optimize pricing strategies to reduce markdowns, improve demand forecasting to optimize inventory, localize assortments and optimize space planning. Advances in software and the occurrence of cloud-based solutions enable retailers to think about accelerating their upgrade/replacement cycles. It will help limit overstocking and discounting. With real-time analytics, retailers can market to consumers on a 1:1 basis based on customer context. Retailers have the ability to know what customers have in their closet, purchase behavior and preferences. For more, read the article written by Forrest Cardamenis : 

http://www.luxurydaily.com/analytics-offer-solutions-to-merchandising-and-marketing-problems-brp/

Customer service is the most effective part of any company. It not only helps to improve customer retention rate, but also helps to understand new market trends. Big data analytics and business intelligence play a crucial role here. Simple web crawling into a social site helps to understand the on-going sentiment and current market trend. Different social responses, video hits, ads and newsletters help to optimize the search results. In other word this process facilitates smother recommendation system and customer centred predictive modelling. To make a better prediction we can use –

1. Connectors help to integrate diverse data sets in different formats

2. Lightweight search facilitates even non-technical data analysis

3. Queries optimizes the search dynamics

4. Enterprise search also scores with its semantic search, which means that the context is recognized and included in the analysis and structuring of the data.

According to a report, analytics and big data are going to see most vigorous hiring. Established firms and startups are offering handsome income to talented data scientists. Analytics and big data sector have been on consistent growth over last five years and are expected to increase at a compound annual growth rate of (CAGR) of 33.2% and 26.4 % respectively. The demand for data professionals has increased. It has been predicted that in coming times data science is going to have most exciting jobs. Read the full article here: http://economictimes.indiatimes.com/jobs/analytics-big-data-to-see-robust-hiring-high-pay-packets-report/articleshow/51105814.cms

Google, Amazon, and Apple all are tech giants. But apart from being this, they also have one more thing common and that is they all have their data center in rural areas. But this is more of an exception than a rule. Most data centers are located in or close to a city. These giants have done this due to the cheaper rate of lands available in rural areas. But these places lack good telecommunications and power infrastructure. Moreover, there are enough facilities to attract right human capital to such places. It’s better to have a data center in a city than a village. Read the complete article here:  http://www.cio.com/article/3036080/data-center/cities-not-cornfields-draw-data-centers.html

Predictive analytics and statistical analysis are based on the concept of relationships between observed and future actions. When analyzing people, we observe a small sample of data on people and build a predictive model to identify a number of shared traits they have. Micro-targeting is the idea of finding relationships among variables to recognize the target audience's shared traits. It helps to identify the right people. The steps included to predict purchase behavior and design a campaign to expand customer base are: 1. Create dataset. 

2.  Once we have a dataset loaded, we will analyze the people who have purchased the cloud solution and find what they have in common with one another. 

3.  To do this, our first step is to create a dependent variable. 

4. Then, we build a predictive model which takes that variable containing cloud purchase information, and compares it to other variables in our data set. 

5.  The regression model we build then compare our cloud purchase variable to whichever other variables used for analysis, and then gives us statistical correlations for each variable. 

The most important issue in IT industry is a cyber - attack these days. Advanced security measures and sophisticated firewall systems are not free from bugs. Hence it is not difficult to crack complicated coding and launch a potential cyber-attack. Sometimes it is observed that these attacks take around one month time to get sorted out. So the question is how did these hackers are getting access to data of a particular company? The answer is simple, it's an in-situ type attack, i.e. hackers stay inside the firm and get familiar with encryption pattern, firewall dimensions and sophisticated filters. The solution is to identify these red flags. That can be only managed if all small potential deviations are identified and listed. These create a huge data pool which can be implemented to automate the alert system. By this process small attacks can be taken care off in no time and protect company from higher damage.

To read, follow: http://www.informationsecuritybuzz.com/articles/data-analytics-in-the-early-detection-of-cybercrime/

In healthcare, information comes from multiple sources and it does exist in multiple forms like images, video, text, numerical data, multimedia, paper, electronic records or what not. These all are very important in the healthcare industry. Data mean the world to them.  So it is really necessary to have the right analytical tools to analyze such huge volumes of data. Moreover, jargons used in healthcare are often alien to patients or a layman. Inconsistent definitions, unstructured data is very hard to aggregate and maintain. Hence, if big data analytics used wisely can help save hundreds of billions every year. Read the complete article here: http://www.forbes.com/sites/sap/2016/02/22/can-big-data-analytics-save-billions-in-healthcare-costs/#4fdcfd7f6253

Few news organizations have developed "editorial analytics" approaches that move beyond the generic use. They develop a customized approach to analytics aligned with the specific editorial priorities. Editorial analytics differ from generic approaches in three ways.

First, editorial analytics are customized to the specific editorial priorities and organizational imperatives of a given organization. 

Second, editorial analytics are used to inform both short-term day-to-day decisions and long-term strategic development. 

Third, editorial analytics are always evolving to keep pace with a changing media environment. 

Editorial analytics represent a significant improvement in news organizations’ capacity to understand the media environment in which they operate. It also helps journalists to reach people.

Chemical industry deals with lots of data sets. Some of these data are relevant to product development and the rest of them mostly helps to identify external parameters governing the production process. Generally, big data help to facilitate cost cutting issues. It brings a new product to the market and improves industry environment. Intellectual sensors are integrated with the research and material related informatics. This process minimizes the time required for new innovation. Sensitive sensors can also help to predict the upcoming situation by measuring current situations. Big data works in the back end. This ensures worker's safety by analyzing the toxic level, heart rate, etc. Intelligent sensors diagnose the past chemical data and all possible combinations to predict the new combination. It smoothens the R&D process. Therefore, automation, safety guide and intelligent forecasting are the key mantra for success in the chemical industry.

 To read, follow:  https://www.environmentalleader.com/2016/02/23/how-big-data-is-changing-chemical-manufacturing/