SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

This sections contains articles submitted by site users and articles imported from other sites on analytics

Role of Machine Learning in Financial Transactions

Since lockdown, online transactions have shot up. And online fraud has also shot up. It has become quite impossible for banks to detect frauds.  But by applying machine learning, fraud detection has become easy for financial organization. Machine learning is detecting email spam,  product recommendation, accurate medical diagnosis etc. Machine learning can also authenticate transactions using machine learning and predictive analytics. Read more at:



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CRM Analytics

CRM (customer relationship management) analytics comprises all programming that analyzes data about customers and presents it to help facilitate and streamline better business decisions.
CRM analytics offers insights to understand and use the data that is mined. CRM is used in Customer segmentation groupings, profitability analysis and customer value, personalization, measuring and tracking escalation and predictive modelling.
CRM analytics can lead to better and more productive customer relationships through the evaluation of the organization's customer service, analyzing the customers and verifying user data. CRM analytics can lead to improvement in supply chain management.
A major challenge is to integrate existing systems with the analytical software. If the system does not integrate, it is difficult to utilize collected data.
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Showcase your talent at a hackathon!


If you belong to the world of Data, hackathon is not a new word to you. Several organizations host hackathons online but how do you pick the right one for yourself? Especially if you are a beginner?

What you do in a hackathon is only an easier version of what your job as a data scientist would require. From personal experience, Kaggle community is a boon to budding aspirants! It does wonders in enhancing one’s skillset by providing a competitive exposure.

The dataset on Kaggle and other platforms is created for the purpose of competitions and giving the participants a taste of work that data scientists are expected to do. However, real world data is much messier than what you would work with on these platforms. Nevertheless, it’s a great way to polish and upgrade your skills.

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Virtual Reality and Analytics

If you’re not tracking VR analytics, how do you know what works and what doesn’t? How do you prove ROI?

Utilizing quantitative and qualitative data can put you ahead of your competitors.  After all, without analyzing your data within VR is just guess work.

Doing this gives businesses the ability to track users in a 3D space instead of 2D screens. Traditional 2D tracking metrics such as clicks, swipes, scrolls or taps are certainly not the best ways to capture the depth of data available in these 3D environments.

VR specific metrics include Eye tracking to see what draws their attention, user interaction with specific objects, tracking 3D spatial data and biometrics to measure the emotional state of users to name a few.

These metrics are used by businesses to develop better products, train employees effectively and more efficiently and to understand customer buying behaviour.

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Why do AI systems need human intervention?

Each one of us have experienced Artificial Intelligence (AI) in our daily lives- from customized Netflix recommendations to personalized Spotify playlists to voice assistants like Alexa – all of these show how integral AI-enabled systems have become a part of our lives.

On the business front, most organizations are heavily investing in AI/ML capabilities. Whether it is automation of critical business processes, building an omni-channel supply chain or empowering customer-facing teams with chatbots, AI based systems significantly reduce manual work and costs for businesses leading to higher profitability.

However, Machine-learning systems are only as good as the data the are trained upon. Many AI experts believe that AI should be trained not only on simple worst-case scenarios but also on historical events like the Great Depression of 1930s, the 2007-08 financial crisis and the current COVID-19 pandemic.

Today, as humans rely on AI, they cannot leave AI to function by itself without human oversight because machines do not possess a moral or social compass. AI is as good as the data it is trained upon, which, may reflect the bias and though process of its creators.

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How businesses are winning with Chatbots

No more will you hear about Chatbots being the next big thing. They’re already here and here to stay! Top domains where Chatbots are proving beneficial are:

1.      Ecommerce and Online Marketing: Messenger Chatbots have higher open rates and click through rates than Email, as a result of which many online marketers have begun using Chatbots as a way of getting website visitors’ information. Redirecting the customer to the correct sales channel, content gamification and relationship marketing are additional benefits it brings to this domain.

2.      Customer Service: The best use of technology right now is in automating the easy questions that get asked over and over again with a live agent takeover whenever the bot cannot answer a question. When the bot is stumped, it automatically sends the questions to a live agent, listens to the answer and then learn how to answer such questions in future.

3.      Travel, Tourism and Hospitality: Bots in this space are being successful on a number of critical fronts- they increase revenue, increase customer satisfaction, increase engagement and brand loyalty and lower costs via automation.

4.      Banking, Financial Services and Fintech: First and foremost, bots can help warn you about issues and dangers with your bank account. Bots can give you suggestions on what to do with your money- it can give you a cost breakdown of where you are spending or how can you move money around in order to save more money. Banks are also using chatbots internally to help automate tasks.

5.      HR and Recruiting: Chatbots can engage applicants and pre-screen them and make sure they’re qualified by asking a few questions. They also help in easing the process of on boarding new employees.


What other uses could be coming next? Read more at:

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Data Science to boost your Brand

By definition, Data Science is a multi-disciplinary field that uses scientific methods, algorithms and systems to extract knowledge from structured and unstructured data. It processes enormous volume of information to draw meaningful conclusion and help businesses grow and expand.

Some of the biggest advantages analyzing data can give to your brand include improving efficiency, cut costs, boost sales, better recruitment, identifying opportunities and targeting the right set of audience to name a few.

Focusing on the practical ideas, four ways you can use big data to raise brand awareness include:

1.      Personalization: Analyzing consumer related information helps in understanding their preferences on an individual level. You can customize offers so as to fit each user individually.

2.      Choose the most relevant marketing channels: Brand awareness largely depends on marketing strategy and the channels you choose to promote business. For instance, Instagram marketing may help you attract younger users while marketing on LinkedIn gathers business professionals.

3.      Create better content: Data analytics enables you to learn about the buying persona such as education, relationship status, professional status, personal interests, demographics, leisure time activities, etc.

4.      Quality reporting: Using data science you can figure out the strengths and weaknesses of the brand, website traffic, social media performance and many other features.


Have you ever thought about incorporating data science into your business strategy?

Details at:

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IoT explained!

Internet of Things is described as a digitally connected universe of everyday devices which are embedded with internet connectivity, sensors and other hardware which allow communication through web. From health tracking Fitbits to Smart blackboards, IoT has made everything around us smart. On a smaller scale, it would be switching on a TV using your phone and on a larger scale planning smart cities with sensors all over.

Why is IoT so important?

The sensors installed are capable of sending information and/or receiving information and acting upon it. These are beneficial as they help improve and innovate lives of customers, businesses and society at large. Businesses have invested extensively in R&D to innovate and develop out-of-the -box products.

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Random forests: a collection of Decision trees!

In literal sense, a forest is an area full of trees. Likewise, in technical sense, a Random Forest is essentially a collection of Decision Trees. Although both are classification algorithms which are supervised in nature, which one is better to use?

A Decision Tree is built on an entire data set, using all the features/variables while a Random forest randomly (as the name suggests) selects observations/rows and specific features/variables to build several decision trees and then average the results. Each tree “votes” or chooses the  class and the one receiving the most votes by majority is the “winner” or the predicted class.

A Decision tree is comparatively easier to interpret and visualize, works well on large datasets and can handle categorical as well as numerical data. However, choosing a comfortable algorithm for optimal choice at each node and decision trees are also vulnerable to over fitting.

Random Forests come to our rescue in such situations. Since they select samples and the results are aggregated and averaged, they are more robust than decision trees. Random Forests are a strong modelling technique than Decision Trees.

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Cyber security tips

Cyber security is evolving, and it is not advisable not to follow the techniques that you have already used to mitigate risk. This article link gives you some suggestions to get your company well-equipped for 2020 and all the threats it could bring. They are: Implementing AI Into Your Strategy, Keep All IoT Devices Secured, Teach Employees About BEC Phishing Scams etc. Read more at:


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Predictive analytics and IoT Data

It is a fact that Internet of Things or IoT is becoming predominant in business as you can gather data to analyse at real time. But, if it is not used diligently, it will be a cost and not an asset to the company.  Traditional business intelligence shows only IoT data from the past but with predictive analysis tells what to do with that data now. This article tells us some common useful applications of predictive analytics on streaming IoT data. Read more at:



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How to keep customers happy with the help of ERP system

It is a fact that ERP or resource planning platform can manage the key business activities like production and supply chain operations. But, nowadays, this ERP system when integrated with machine learning technology, also plays a vital role in keeping your customers happy. And this ERP system can have greater control over the products they eventually receive, thus cutting down on errors. Read more at:


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How Artificial Intelligence is redefining industry

The Artificial Intelligence industry is growing at an exponential rate and the artificial intelligence market is projected to grow in value to $191 billion by 2024 with a CAGR of 37%. Artificial industry has the capability for machines to make decisions based on logic, data, and information from the past is impacting several industries. From self-driving vehicles to chatbots, thus transforming the way business is done on a global scale. Read more at:


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Trade-off between Opaque and Transparent AI

AI can be classified into Opaque and transparent Systems. Opaque AI is the black box where it is not evident why AI operates in a certain way. Though it is effective,it just means that there is higher risk associated with predictions and insights. Transparent AI is when technology does explain how it reaches its decisions using data at hand.But a company often prefers opaque AI, if the insights provided help in actually growth of the company. The need for transparency is a constraint on AI. And opaqueness might prove more effective. There is a trade-off between the two. When GDPR comes into effect,banks in The EU will be legally obliged to explain how they operate. Opaque AI will not work here ,although it might be more effective.Businesses should be able to control the kind of AI to be used in a given situation,its ethics and accuracy. Read more at:


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Leadership Strategies in Algorithms

As the phrase goes, “everything that can be digitized, will be digitized”, is fast replaced by “If something can be run by algorithms, it will be”. Algorithms are supposed to be performing the following tasks: • Reading resumes: With natural language processing, resumes can be read faster and with more careful eyes. • Using spreadsheets: Soon the analysis made by experts using spreadsheets would be taken over by AI. • Hiring consultants: Since the analysis will all be done by algorithms, hiring consultants is really not needed as before. Hence, for coping up with the changes, one needs to get acquainted with the programs, rent a machine learning expert to design algorithms or make it on your own and invest for the future by learning new software. Read more at:

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AI application by NASA

NASA has used AI in human spaceflight, scientific analysis and autonomous systems. Multiple programs like CIMON, ExMC, ASE, Multi-temporal Anomaly Detection for SAR Earth Observations, FDL, robots and rovers are currently available at NASA. It is now working on overcoming the barriers that once blocked it from innovations in AI and Machine Learning. Although Machine Learning has been in existence for 60 years, benefits couldn’t be reaped by NASA because according to Brian Thomas, a NASA agency data scientist and program manager for Open Innovation, they are being held back. Read more at:

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Big Data in Economic Prosperity

Big Data, if utilized properly, is believed to become the historic driver of progress. It plays an important role in the fields of public security, healthcare, poverty, to name a few. Video surveillance and facial recognition using big data is far more effective than reviewing the footages manually, which can be erroneous. It also helps in avoiding cybersecurity threats. Predictive models using big data can predict for future attacks even before their occurrence. With the application of big data in healthcare sector, there has been a shift from treating illnesses to proactively maintaining our health and taking certain measure for preventive care. It plays an immense role in the education sector as well. By understanding the needs of each district, it gives schools the opportunity to build innovative educational techniques. Big data solves urban transportation problem by enabling government agencies develop alternate routes to ease traffic. It helps in alleviating the dangers of food scarcity. It is time to embrace big data as it opens up opportunities to encourage economic prosperity. Read more at:

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Visual Data in Decision Making

With every passing day, data and not instincts, are used for the expanding of business. Data is the new gold, as it helps in determining trend, offering better customer experience, responding better to market demands. However, given the data size is so big, Data Visualization is opted for, making the interpretations easier. The major reasons that data visualization is crucial are: • Data visualizations amplify a story with pictures and visuals. • Data visualizations makes difficult data comprehensible. • Data visualizations help in decision analysis. Read more at:


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Financial Analysis in Businesses

Financial analysis s beneficial for businesses in the following ways: • Cutting costs: Financial data relating to investments and cash flows are analysed. • Making investments: Financial analysis helps in predicting the returns from investments, thereby enabling the companies to go for profitable investments only. • Forecasting the future: The future of the company can also be forecasted. • Following business trends: Financial analysis relies upon the current business trends and success rates of businesses in the sector. Such analysis helps in recovering faster in case the market suddenly drops. • Management: Financial management is also tracked by the financial analysts which helps in increasing efficiency overtime. Read more at:


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Predictive Analysis

Predictive modelling software is known for training the model with the dataset with known results to predict outcomes for the new data. The two common types of predictive models are, classification model (example, predict outcome when a component fails) and regression models (predicts a number). The benefits of predictive analysis are: • Improved production efficiency: It allows for effective inventory forecasting, production rates for meeting demand, and the like. • Improved Decision making: It identifies patterns and trends for the data, enabling easy decision making. • Enhanced risk reduction: Predictive analysis, as the name suggests, enables prediction about the future. This is most helpful for a firm to save it from the upcoming risks. • Enhanced fraud detection: Being aware of the trend, a change becomes helpful in detection of fraud. • Targeted, personalized marketing campaigns: Predictive analysis helps in knowing the structure of the market and helps in closely targeting and personalizing marketing campaigns to attract customers. Read more at:


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