SigmaWay Blog

Predictive analysis is now coming to the mainstream. Companies are trying to recruit people with the knowledge of maths and economics together with the business. Evolution of analytics is changing its pace. Organizations are treating the data as their key assets and trying to analyze those to gain more from their business. Initially company didn't realize the importance of data analytics. But now it has become a common trend of trusting their data to the cloud as it seems more secure. Read more at: https://www.cio.com.au/article/620089/slow-evolution-predictive-data-analytics/?fp=16&fpid=1

Farmers are not getting the proper price for their products and a lot of produced are not being traded in the market due to lack of proper information symmetry, backward integration, insurance facilities, and infrastructural development. Proper data analysis can help in this respect and save many lives. Data analytics can integrate satellite, weather, and IoT analytics with the agriculture sector and machine learning and parallel computing techniques can be used to get a better idea about the crop phenology. Read more at: https://yourstory.com/2017/05/satsure/

Prosumer- the proactive energy consumers who design and customize their products using smart devices to manage consumption, add renewables to the mix and look for personalized service from his or her utility. The energy consumer gets connected to utilities in terms of both demand and supply and this makes them more vulnerable to ransomware attacks. The energy security benefits are designed to provide security, adaptability and personalization to the consumers with the help of Augmented Intelligence (AI) which help the organization to communicate with consumers and the press. Enterprises can succeed by focusing on consumer personalization, security and the technologies. This can be done with the help of automation, predictive analytics and machine learning. Read more at:https://securityintelligence.com/personalizing-energy-security-with-robust-analytics/

Nowadays, the big data and analysis are in demand as the political and economic conditions are full of uncertainties. Organizations of Asia Pacific are investing rapidly and heavily in digital labor, cognitive automation or robotics process automation to make them technologically advanced and place them ahead of the rest of the world. According to a CIO Survey, it was found that many technology executives are turning this uncertainty into opportunity and are becoming the driving force in making their organizations nimbler and digitally innovative. Technology leaders are becoming influential, as chief executive officers and boards turn to them for help in navigating through these uncertain times. The global security crisis has increased the demand of technological improvement. Read more at: http://www.todayonline.com/business/big-data-analytics-skills-hot-demand-amid-tech-boom

Acquisition -in the world of advertisement, this refers to the ability to attract new customers. According to an IAB report,2017 will witness the growth of native advertisement while the virtual ads will become a popular format. Customer's desire changes every minute and this will assemble a massive set of data. To get the best analysis of this data, big data analysis will give a better understanding of the customer and in turn will help the company to find the target customer. Thus, the use of data analysis is profitable and cost effective. Read more at: https://customerthink.com/how-big-data-analytics-are-empowering-customers-acquisition-in-native-advertising/

Right data insights now have the potential to be a valuable resource to a marketer. It helps brand observe consumers, provide seamless brand experiences, etc. Goals of data driven marketing are: Real time personalization, App-based marketing, Location-based Marketing, Virtual assistants, Using Big Data to predict trends. So, ultimately its goal is to understand and target the suitable audiences. Most marketers have realized the need to be data driven. But to be successful they must choose their own internal data sets and apart from that utilize third party data to gain an in-depth view of consumers across the customer journey. Social Media Data, mobile data and DMPs provides deeper insights into customer's preferences. Read more at: http://www.business2community.com/big-data/state-data-driven-marketing-use-data-fuel-new-revenue-streams-01856921#RcsPHSImrRGOfL8c.972

A decentralized network help banking database connecting unique chain to store transaction records. The technology allows the database to bypass the disruption of the payment network that are often slow, cumbersome and expensive also help in making strong fraud to die out from the system. The financial banks taking up the bonds and shares to get replaced in a decentralized structure. Furthermore, the middlemen are cut through the technology, moreover, it also cuts the cost of transaction records. Looking towards the new area of transformation the bitcoin technology is far approachable to adjust the use of database in decentralized form. Read more at: http://analyticsindiamag.com/blockchain-technology-the-immutable-database-revolution/

Fast data is the application of big data analytics to smaller data sets in real-time in order to solve a problem or create business value. The goal of fast data analytics is to quickly gather and mine structured and unstructured data so that customer experience can be improved by creating a more streamlined process for marketing strategies and customer service implementation. It has been observed that fast data analytics helped businesses turn their raw machine data into actionable insights by tracking transactions, identifying issues with hardware and software, and reducing customer complaints. It also helped in staying compliant with government regulations, avoiding preventable losses and improving the personnel’s efficiency by pinpointing errors. Thus, fast data analytics services significantly improve business’ customer experience by solving issues faster and more efficiently. Read more at: http://www.datasciencecentral.com/profiles/blogs/how-you-can-improve-customer-experience-with-fast-data-analytics?xg_source=activity

Social media analytics or SMA, is the practice of gathering data from social media websites and analyzing that data to make business decisions. The most common use of social media analytics is to mine customer sentiment to support marketing and customer service activities and turns the vast amounts of semi-structured and unstructured social media data into actionable business insights. Depending on the business objectives, social media analytics can take four different forms. The first two are reactive in nature, while third and fourth are proactive in nature. First is descriptive analytics. Descriptive analytics gather and describe social media data in the form of reports, visualizations, and clustering to understand a well-defined business problem or opportunity. Second is diagnostic analytics, it can distill this data into a single view to see what worked in the past campaigns and what didn't. Third is predictive analytics, it involves analyzing large amounts of accumulated social media data to predict a future event. And the last one is prescriptive analytics, it suggests the best action to take when handling a scenario. Read more at: http://www.analyticbridge.com/profiles/blogs/4-types-of-social-media-analytics-explained

All organizations face pressure to improve performance. This is difficult as there exists risks which reshapes the businesses. As the risks become more intertwined, managing them becomes difficult and leads to chaos. GRC helps the businesses to achieve task of managing everything under one umbrella. GRC helps simplify the complex and huge data. Most businesses are implementing AI systems to speed up the investment decisions. Systems will be able to automatically collect data from various data streams and channels. Also analyze it against the company’s existing datasets and operations, making suggestions regarding the changes. As technology evolves, algorithms improves and probability of errors reduce. Cyber risk is a new threat. As companies face greater pressure a more advanced GRC technology is to be adopted. Read more at: http://www.itproportal.com/features/the-road-ahead-the-coming-rise-of-artificial-intelligence-in-governance-risk-and-compliance/

The present business environment is volatile and full of uncertainties. Therefore, a need arises to improve efficiency and profitability. Though many organizations rely on traditional techniques, predictive analytics is the new trend of managing risks and monitoring frauds which eliminates all the guesswork. Predictive analytics help us in reaching the source of fraudulent transactions and in dealing with future plausible attacks. Lack of corporate transparency and missing public trust should be dealt with by using advanced tools for managing huge data and ensuring accountability. Predictive analytics helps in building the customer profile to know his credibility which is useful for banks. Read more at : https://blogs.metricstream.com/ready-predictive-analytics-revolution/

From gyms to the front desks of Medical practice center, analytics are used everywhere. Most of the sectors have been semi-automated. Some small businesses, however, have failed to use analytics. Except these exceptions, most of the businesses have been successful in combining data science and cloud technology. Data analytics are essential for medium and small scale companies as well to be successful and data centric transformations are now trending. Read more at: http://www.zdnet.com/article/using-analytics-for-health-commerce-and-more/

Consumer Intelligence is largely focused on improving customer behaviour and enhancing consumer products which are tailor made to match consumer expectations. AI helps industries to introduce new product features by finding patterns in huge datasets. There are two types of categories in consumer AI : front end bots and AI assisted human agents. Chatbots take care of customer text queries. AI in enterprises has been useful in Enterprise Resource Planning. Enterprises are conducting predictive analytics in developing AI applications. Enterprise AI can be of two types- Applied AI and Artificial General Intelligence. Though comparing these two enterprise AI is complex and requires much more expertise  than consumer artificial intelligence. Read more at: http://analyticsindiamag.com/enterprise-ai-vs-consumer-ai-understanding-two-differ/

Random forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. It is called random because there are two levels of randomness; at row level and at the column level. In spite of it being such a convenient process to deal with large datasets it has a few disadvantages. In case of smaller datasets linear regression is a better method than this. Next is that any relationship between the response and independent variables can't be predicted. Also, this process is very cumbersome and can't take values from outside the datasets. Even then, random forest is advantageous because keeping the bias constant it can decrease the variance in the datasets and it helps us ignore most of the assumptions like linearity in datasets. Read more at: http://www.datasciencecentral.com/profiles/blogs/random-forests-explained-intuitively

Records were exposed globally, killing security running for support. A wannaCry attack leaves hints to the user on a network system. Moreover, how the security is protected or responsible by the fact of undercovering its data and information more strongly not be damaged to expose, also how the data miners are fighting against these cybercrimes. AI refining the threat of cybercrime and developing more ideas and innovation to counter such attacks. Not only that AI and quantum computing are challengers, but also a way with uncertainties. Read more at: https://www.ft.com/content/1b9bdc4c-2422-11e7-a34a-538b4cb30025

This year was good for SaaS startups in the country. Indian markets adopted newer technologies into its systems. More opportunities for SaaS players are expected from the BFSI sector. SaaS companies use business model that provides software solution over the internet and they charge the customers according to the usage of software since most of the financial solutions have gone online. The software uses around 2000 data points to evaluate the credit score and corresponding interest rate. Many big data analytics startups provide solutions to industries. Read more at: http://economictimes.indiatimes.com/small-biz/policy-trends/digitisation-push-makes-bfsi-sector-attractive-to-saas-players/articleshow/56325400.cms?from=mdr

Self-service analytics is an approach to data analytics that enables non-tech savvy users or business users to access data for more informed decision making.  For success in self-service analytics, employees should have the culture of using data to start, propagate or conclude every conversation. A few areas required to support this cultural change are, organizational readiness which will help in determining the type of self service tool required for the organization. Next is data readiness i.e. continuous feedback about data quality practices should be given. Third is data security readiness i.e. data security, compliance and data access should be carefully examined during making a transition to self-service analytics. Fourth is that users should be adaptable and willing to use new technology. And lastly, data shouldn’t be interpreted just by preparing charts instead it should be used to make theoretical interpretations. Read more at: https://www.blueoceanmi.com/blueblog/self-service-analytics-need-cultural-change/

With all the talk about predictive machine learning and deep learning applications, one can lose sight of the data engineering, some might call it data art that is needed to prepare the data to work on. Many questions go into the planning for deep learning applications like should the processing be disturbed; how much noise obscures the signal arriving from internet of things devices such as cell phones. In the case of mobile phone sensors, data preparation for deep learning applications can present unique problems, data preparation can involve considerable preprocessing. Insurance and other industries are entering the golden age of sensor data, but the data needs preprocessing because the data initially is very noisy. Given the Data, the algorithms will figure out the right transformations of the data. Read more at: http://searchdatamanagement.techtarget.com/news/450419925/Data-prep-for-deep-learning-applications-means-careful-planning

Many data analytics firms are working for the betterment of the farmers. These companies integrate satellite, weather, and IoT analytics with the agricultural sector. They use its proprietary machine learning and parallel computing techniques, to resolve complex relationships like crop growth and soil health. Using analytics farmers can opt for a smart sampling procedure using satellite – based crop clustering techniques, which reduces the time for identification of these plots and optimize their locations. While the former requires timely crop intelligence, crop insurance companies need highly accurate assessment of risk. The satellite imaging analytics serves two purposes: First, it ensures that the farmers receive a fair and immediate compensation for crop loss due to adverse climatic conditions. Second, it enables insurers to settle claims speedily due to the availability of data in near-real time without any manual intervention. Read more at: https://yourstory.com/2017/05/satsure/

Programming languages and Mathematical algorithms are not sufficient for a successful career in Analytics. Communication skill plays a vital role. Explaining the results of the analysis performed in a simple language is as important as ability to analyze the data. According to the author, the key aspects of communication are simplicity, narrative and action. Secondary research is another way to stand out in the analytics profession. What counts is the simplified presentation and explaining the consequences of several course of action to build a successful career in analytics. Read more at: https://www.forbes.com/sites/metabrown/2017/05/30/if-you-want-to-succeed-with-analytics-effective-communication-is-a-must/4/#1a1f627d4c44