SigmaWay Blog

Subsets of AI are diversifying and algorithms are growing advanced. AI had an alarming impact in many instances. Certain applications of AI are called black box because it is difficult to understand how the result have been generated. Decoding the black box technique involves optimizing a given function in isolation, and sharing it as necessary. This makes the work a lot easier and scales the data. Firms need to make people aware of AI's applications in order to make it more transparent. AI cannot be completely trusted with certain applications. In future, we have to embrace AI and develop trust on it because it has many advantages and black box is a positive step in this direction. Read more at: http://analyticsindiamag.com/making-sense-black-box-artificial-intelligence-trust-ai-completely/

Small businesses are vulnerable to hacking. Hackers attack smaller businesses assuming that they can quickly get in and steal customer data. So, to strengthen the customer data security the following can be followed. The first thing to keep in my mind, is it as data grows the security system should be improved. Next is building up the online sales. Once it can be shown that the threats of hackers and viruses are taken care of, customers will rely on the company more and thus consume online. Third is to use the right technology to protect the data. Installing some software which will constantly monitor the system and give alerts if someone tries to break into the secured information can be helpful. Additional protection measures such as authenticator tabs and biometrics should be considered. Fourth is that only technology shouldn't be relied upon. Fifth is risks shouldn't be underestimated i.e., companies should always be prepared beforehand and have proper security measures. Lastly, it is important for the companies to know where the data is located, this way, they can be prepared in the event of a natural disaster or if any other problem hits the location where the cloud servers are located. Read more at: http://www.analyticbridge.datasciencecentral.com/profiles/blogs/tips-for-reducing-fraud-and-bolstering-customer-data-security

Principal Component Analysis is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possible correlated variables into a set of values of linear uncorrelated variables called principal components. The goal is to explain the maximum amount of variance with the fewest number of principal components. PCA transforms the initial features into new ones that are linear combinations of the set of variables. For this analysis, first the original values should be normalized and the covariance matrix should be formed. The eigenvalues and eigenvectors should be calculated and the eigenvector with the highest eigenvalue has to be chosen. For the highest eigenvalue the data set matrix has to be multiplied and finally the mean can be put back which was removed in the beginning. However, if the original data set is correlated the solution can be unstable. Read more at: http://www.datasciencecentral.com/profiles/blogs/introduction-to-principal-component-analysis

In the time series, after isolating trends and periodicity, a normalized time series is left. To check whether the data follow some well known stochastic process, model fitting is done. If the model has an autocorrelation then it is de-correlated and after de-correlation it is checked whether it behaves like white noise, or not. The article further explains how to remove auto-correlation in a time series with the help of first order autocorrelation and linear algebra framework in PCA with an example as well. Read full article at: http://www.datasciencecentral.com/profiles/blogs/how-and-why-decorrelate-time-series

Data science and mathematics together makes learning more interesting. Passionate data scientists towards mathematics solve many modern math problems using data science. This article gives selection of 12 interesting articles, about mathematical problems, math-free algorithms and statistical theory. Most of them can be understood by the layman. Some of them include R code and some include processing vast amounts of data. Some of the articles are: Simple Proof of the Prime Number Theorem, Fascinating Facts and Conjectures about Primes and Other Special Number,Factoring Massive Numbers: Machine Learning Approach.

Read more at: http://www.analyticbridge.datasciencecentral.com/profiles/blogs/10-interesting-reads-for-math-geeks

The main responsibility of DevOps and IT operations teams include solving problems and facing challenges which is becoming tougher by each day. This is where real-time and centralized log analytics come to the rescue. It helps them in understanding the essential aspects of their log data, and easily identify the main issues. While Artificial Intelligence (AI) was a big thing a few decades ago, it is now being commonly used. As IT operations becoming more complex, AI is becoming a powerful and essential tool. One solution can be to have a platform that has collected data from the internet about all kinds of related incidents, observed how people using similar setups resolved them in their systems and scanned through your system to identify the potential problems. Cognitive Insights can be introduced, this technology uses machine-learning algorithms to match human domain knowledge with log data, along with open source repositories, discussion forums, and social thread. Read more at: http://readwrite.com/2017/05/15/artificial-intelligence-transform-devops-dl1/

Artificial intelligence (AI) is going to steal your job and. If we do not prepare now, we may face a future where AI runs free and dominates humans in society. The AI revolution is going underway. So, how to survive the AI revolution. First, recognize AI. It is already here. For example, Google suggestion, Facebook timeline ranking, YouTube suggestion are some of the examples of AI. Second, identify where it is growing. AI is particularly good at any task that requires an enormous amount of repetitive processing. If this is your job, then it is the time for looking for an alternative for survival. Third, plan an action for AI revolution. One thing you can do is oppose this AI or just make yourself comfortable with it. In other words, be ready to upskill where possible. AI can learn very well, but it cannot learn flexibly (yet). You can. There are new jobs now available that did not exist five years ago. So if you allow AI to grow, you will find that it will help to increase your standard of life given that you prepare well. Read more at: http://dataconomy.com/2017/05/survival-guide-ai-revolution/

Sentiment Analysis for Product Rating is the system that detects the hidden sentiments in the comments posted by the consumers and rates the product accordingly. This system uses a sentiment analysis method. This is an E-Commerce web system where the registered users will view and comment the product. And then the system will rank the product after analyzing the users’ comment. Comments will be compared by with the keywords stored in the system database. On this basis, system will specify whether the product is good, bad or worst. This application also works as an advertisement which makes many people aware about the product. This system is also useful for the users who need review about a product. Read more at: http://nevonprojects.com/sentiment-analysis-for-product-rating/

Big data may be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It can also help to improve the market and customer relations. By collecting data on your company, it is easier to know what the customer wants, the services they like and also helps in improving operations and make them more efficient and saves time and cost. Companies, big and small alike should find ways to mine all the information and use it to their advantage. For further information, please visit :

 http://it.toolbox.com/blogs/this-is-it/combine-big-data-and-your-business-75513

The Big Data analytics remains a highly significant factor when there is a point of mobile marketing. Every business is aware of the role of mobile apps and big data analytics in establishing a brand image and marketing their products. The apps's anywhere-anytime nature helped the businesses acquire more insights on the user data based on input, usage patterns, and the user behavior. This huge reserve of mobile user data is used further for the purpose of optimizing the mobile user experiences, to drive as well as build the mobile traffic, to drive more user interaction and engagement and to push the business conversion. Some critical facets need to keep business in their mind: i) Data driven approach to marketing must be cross disciplinary. ii) focus must be on the right KPIs and importance must be given to insights between lines than just numbers. Read more at: http://analyticsindiamag.com/mobile-apps-leverage-big-data-drive-sales-marketing/

Organizations do the required amount of substitution between 'defensive' and 'offensive' uses of data and also between control and flexibility in its use. Defensive data is about minimizing downside risk. Activities include ensuring acceptance with regulations and using analytics to identify and uphold fraud. Defensive efforts are applied to ensure the integrity of data flowing through a company's internal systems. Data offense emphasizes on supporting business goals like increasing revenue, profitability, and customer satisfaction. It includes activities which produce customer insights & market data to support managerial decision making. Each strategy has its own working infrastructure. Elements of data strategy  that should be taken into account are: Data definitions, Data ownership, data access, data assessment. Read more at: https://thefinancialbrand.com/65419/data-strategy-playing-offense-defense/

Microsoft is illuminating several places with big data and analytics offerings. In Finland, Microsoft along with CGI  developed a data driven smarter transit system, which saw Microsoft utilize the city's existing warehouse systems to create a cloud-based solution that could assemble and analyze travel data. Boston serves as another example where Microsoft is working to spread information about the variety of urban farming programs. Microsoft has also partnered with Athena Intelligence in developing the hill city of San Francisco. As a part of this, Microsoft is influencing Athena's data processing and visualization platform to gather valuable data about land, food, water, and energy.For further information.Read more at:

 http://analyticsindiamag.com/big-data-analytics-now-used-greening-planet/

Marketing Cloud integration assimilates all aspects of marketing in support of a company's market goals.To begin with,security and privacy are the fundamental building blocks of trust between a company and its prospects, customers, partners, investors and employees.Next,finance and monetization are the next most important fundamental to the successful relationship between all stakeholders.Marketing collaboration and marketing analytics also are indespansable for the effeciency of the marketing cloud architecture.Web, Content Management Systems, CRM and eCommerce Platforms are core marketing platforms.Careful evaluation of current capabilities and a realistic assessment of available resources  helps in appropriate  selection of the systems needed to enhance and optimize each of these core platforms and marketing mix tools.In many cases,the task of assessing these capabilities is beyond the focus of many marketers and executives.IT management will be called upon in these instances to implement the integrations needed to optimize marketing cloud operations.Read more at : 

http://it.toolbox.com/blogs/integrate-my-jde/marketing-cloud-integration-76575

Each and every sector of the economy is about personalized experiences. Business analytics and business intelligence is now a necessity. Data driven marketing strategies directly translate into conversions and revenues.  It is high time for the financial services industry to adopt the same.  The financial institutions are lagging in terms of consumer expectations and level of customization. Exclusive focus should be on their consumers and their specific needs.  In fact, successful implementation can add up to 14% to the annual revenue. Read more at: https://thefinancialbrand.com/65396/banking-data-analytics-marketing-personalization/

After being cautious of big data for several years, the marketing and advertising technology sector is adopting it cautiously nowadays. Big data analytics has emerged to be a handy tool for these sectors as it allows a more sophisticated analysis of things to get deeper insights into a consumer's behaviour pattern.  The data is gathered using consumer’s digital footprints like googling and facebook liking etc. Then, this information is used by companies to identify the behaviour of the existing online users to offer them the best targeted deals and services which are specially tailored for them. Therefore, this allows the marketing and advertising sector to create more focused, targeted and, effective campaigns by saving money, targeting the right consumers for the right products and increasing the rate of conversion of the deals offered. Read more at:https://www.entrepreneur.com/article/293678

With the increasing availability of data in the present digital age, a new science of opinion mining is emerging. It is based upon the use of Artificial Intelligence (AI) to mine public opinion for sentiments as well as the topics driving that sentiment. This can be carried out in two ways: the first one involves the exclusive use of AI to structure the data, while the second one involves the use of AI along with processing of data through a team of people to verify the data for sentiment and the topics driving the sentiment, since AI could struggle to understand the nuances of human emotions. So, the field of data mining can be used by governments, global organisations, media and businesses to shape their strategies efficiently and, measure the public’s/consumer’s satisfaction of their policies, products, services and brands.Read more at: http://www.business2community.com/big-data/opinion-mining-future-data-analytics-01849821#SoExOgmWJWYF7RIM.97

Advanced data resources like big data analytics is required by every business. Major areas of sales and marketing getting help from big analytics are customer analytics, operational analytics, product innovation, etc. Relation between big data and mobility allows mutual growth. Mobile apps promotes growth of big data. Apps have all the materials to steer marketing initiatives. Reserve of mobile user data is used to optimize a variety of things. Data driven insights play a major role in mobile marketing. Specific user data along with location data allows more personalization in marketing. High volumes of data is giving more control to analytics and real-time analytics can provide more advantages. Most businesses use data driven marketing approach. Big data pushes the benefits of mobility with advanced approaches. Read more at: http://analyticsindiamag.com/mobile-apps-leverage-big-data-drive-sales-marketing/

The cost of acquiring customer base is very high in the first 12-24 months of business incorporation because we do not know the  characteristics of our target group .This is why Cost to Income Ratio (CIR) is high. To lower CIR we need to collect transactional data, website data and logistics data. Having the data for 8-12 months we can understand the characteristics of existing customers by building up a model and selecting significant variables having an impact. Running a classification algorithm would fine-tune our target group. Customer Lifetime Value (CLV) depicts the quality of customers. Customers with higher CLV would help business to grow.Read more at: http://analyticsindiamag.com/optimize-digital-marketing-spends-drives-traffic-ecommerce-site/

Cybersecurity is a threat to the globe. The quicker it can be dealt with, the better it is. In order to quickly find the problem, the organizations have to deal with the available big data more efficiently. Edge analytics is the concept of extending edge computing to data analysis. The advantage is that analytics can be done closer to the devices which actually generated the data so that the predictive maintenance can be carried out and security problems can easily be dealt with. This is important in situations where waiting for sensor systems to transmit data to a remote cloud is just a wastage of time , for example the case of train safety.Edge analytics can act as a veritable antidote to the malicious act of hackers. Read more at:  http://www.csoonline.com/article/3195922/data-breach/is-edge-analytics-the-front-line-of-cybersecurity.html