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

How to deal with Unpaid Invoices

According to a recent study, the average small business only has 27 days of cash reserves on hand which implies that there is a possibility of serious cash flow issues when a client is tardy with their payment. Dealing with unpaid invoices can be tough and to handle all aspects of the unpaid invoices, we need to take care of few things. First, there is a need to avoid working with clients who don’t pay on time. There must be a clear understanding of the terms and conditions so that no client can take benefit of the doubt. Considering mobile invoicing make the things more convenient. There is a need to handle the issues in a professional and systematic way. If there are cash flow problems due to outstanding invoices, the option of invoice financing can be chosen. Approval for invoice finance is a quick process and hence can be useful at the time of crisis. Unpaid invoices result in a lot of time wasted chasing clients for payment. However, there are ways to get back the money from the clients, keeping the business financially sound. 

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Commodity Trading Advisor- More than a Portfolio Manager

Earlier the commodity trading advisor is supposed to trade commodities and futures for a managed futures fund. But now selection of investment products is more complex and varied which calls for the need of acute understanding of CTA, of these products. Role of today’s CTA is related to derivative analysis also and hence not only limited role to trading. Analysis is now, the catalyst for the inclusion of value added service to retain customers which includes structured products, risk management and OTC derivatives.

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Classification using ML

Classification of data is very important in many organizations. They can be used to make decisions. But the task of classification can be very tedious. Now imagine a machine doing this job. Classification using machine learning is with the help of supervised learning approach and algorithms. Machine learns from the data input given to it and with the help of this learning, it classifies new observation.

For example, we want to check number of male and female members in an organization. Here we can train our machine to do this classification. 

Classification using machine learning is one of the trending technologies being used in various fields. It has many applications in many domains other than IT.

Various algorithms can be used to implement classification. There are two types of learners in classification – 
Lazy Learners - which simply store the training data and wait until a testing data appears. They classify the data based on most related data.
Eager Learners – that construct a classification model based on given training data.

Different classification algorithms are – Decision Tree, Naive Bayes, Artificial Neural Networks, K-nearest neighbor.

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Working with Machine Learning

Artificial Intelligence, Machine Learning and Deep Learning are relatively newer technologies invading the fields of information technology, business etc. Though developers are walking towards this era, currently the number of experts is relatively less. The company often makes mistakes by starting up with the technologies instead of focusing on business needs. They often make mistakes by assigning out of domain work to some. For e.g. Hiring data scientists and asking them to build something interested from given database. Rather than a team must be formed of product managers, data engineers, data scientist and DevOps engineers.A team of four will be a kick start to improve our process and giving better results. Now everybody has an opportunity to improve the models, optimise the deployment and scale the business. 

Talking about ML, many projects fail due to complex structures. This could occur because of working on wrong problem, to having wrong data, failing to build a model or failing to deploy it correctly. Read more at:

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Analytics is the global key

The old practice was to study the analytics from a historical perspective but in today’s marketing scenario, we need analytics that present a forward-looking view. There is more potential in data and analytics. Thus, that’s what the McKinsey Global Institute’s partners Michael Chui and Nicolaus Henke share their views with McKinsey Publishing’s Simon London. The age of analytics: Competing in a data driven world is a new McKinsey Global Institute research report in which Nico and Michael are among the co-authors of it. In the McKinsey Podcast, according to them data and analytics is important for customer engagement and satisfaction because a lot of data is available, connections are many and there are many understandable machine techniques and languages through which it’s easy and comfortable to analyze data and to take decisions differently. The data analyzed must be organizable in the ways needed. The data must be such that it develops helpful use cases.

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Three Steps for Business Automation

Digitalization has become a global movement and initiatives are being set to accelerate the rate of adoption. 2030 has been declared as the target and many initiatives are already on their road. One such automation process that can that can change everything significantly is the business automation process. From improving customer experience to cutting down cost, business automation has the power to help the firm grow. 

However, automation efforts are only beneficial if the CIOs make right investments and decisions. It is often seen that with a wish to automate, many leaders often make wrong decisions which could cost them potentially higher than excepted.

The three business goals that investments in automation will help are:

  1. Digitalize and optimize operation
  2. Create and act upon advanced insights
  3. Drive business technology innovation.

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The Ten C’s of A Data Scientist

Data Science is a new field of interest and used in every sector. Whether it is a business, production line or a tech company, each of them wants someone to analyse their data. This would further help them to make decisions. Even though there is so much need of data scientist, still the number of data scientist is low. There are many characteristics that could define a good data scientist. 

Few of them starting with C are: Curious, Careful, Clever, Confident, Creative, Capable, Communicative, Considerate, Candid and Collaborative. 

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Journey Science Analytics

With existing systems and technology it is often difficult to manage massive amount of data by various industries and organizations. Keeping in mind several aspects such as cost reduction, customer experience improvement and increase in conversion rates, companies often need to derive meaningful conclusions from the data collected and establish a connection between them. This linked data is also referred to as journeys. Journeys improvise on well-grounded business decisions by pointing out the improvements needed after looking at the customer transactions as a whole. Journey Science facilitates anyone, to be a Journey Scientist, regardless of their skillset and enable them to easily generate an intelligent dataset that includes the commonly understood business context in an enterprise. Leading telecom businesses are implementing the Journey Science concept through a multi-channel, end-2-end view and linking together structured and unstructured data back to their strategic objectives, and quickly modifying them to ensure they cope up with the evolving customer demands efficiently.

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Ever wondered what Business Intelligence is?

In today’s business world, companies generate a lot of data. Now, we now have things like social media and cloud based business services and they all generate tones of data, which is the biggest challenge for BI. Business Intelligence (BI) refers to the tools, technologies, applications and practices used to collect, integrate, analyze, and present an organization’s raw data in order to create insightful and actionable business information. The purpose of Business Intelligence in a business is to tackle with the questions as to what are its main benefits. Read more at:


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Importance of processes for successful business

In an organization it may seem that the processes are ordered and it runs smoothly, but the affects are not always in line with the expectations. Companies expect and get results from process improvement, Business process improvement (BPI), also known as functional process improvement is a strategic planning methodology which aims at identifying the operations or employee skills which would eventually lead to smoother procedures and more efficient workflow for the overall growth of the business. Its main purpose is to meet customer demands and business goals more effectively. Read more at:


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The World of Artificial Intelligence

The world of artificial intelligence is so much exciting. So basically Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. In the year 1956, John McCarthy coined the term “Artificial Intelligence” and defines it as "the science and engineering of making intelligent machines." Precisely AI is a machine with the ability to solve problems that is usually done by us humans with our natural intelligence. AI is a broad branch of computer science. The goal of AI is to create systems that can perform intelligently and independently. Read more at:


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Big data in small business

The online commerce industry has been growing at an unprecedented rate which is attributable to big data. Alongside exploring the benefits of deep learning and predictive analytics, small businesses, working with smaller budgets, should consider using big data to reap the most bangs for their buck in terms of highest profitability. Over the past few years, e-commerce companies have invested titanic amount in big data and have earned record profits. For example, according to Statista, Amazon in 2017 netted a profit of nearly $178 billion as compared to $136 billion in 2016. Walmart, too, experienced nearly a 44% increase in revenue attributable to more resource allocation to e-commerce. Small businesses primed for growth are also capitalizing on the surge in online payments and big data e-commerce to get their slice of the profits as well. A research by eMarketer, estimated e-commerce sales to rise upto $2.8 trillion by this year. Surely, e-commerce will continue to smoothen its way to dominance with consumers on board. Small businesses can extract certain benefits like building brand awareness, improving customer confidence and greater net impulsive buys. The propagation of big data ecommerce options is one of the biggest which has paved the way for more valuable payment systems.

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Big Data in human relationships: A qualitative approach

With the development of data-related technology in recent years, objective data analysis has not been able to overcome the hurdle of managing human relationships. It is indispensable to focus on qualitative human characteristics to opine about an employee in addition to performance numbers. In recent times there has been a sheer rise in demand for this kind of an analysis, even from the business perspective which would assist managers to coordinate and understand employees better. The major challenge facing data is to tackle an issue that has historically been impossible to be reduced to numbers. With a large volume of data, collected across multiple dimensions, even a rudimentary algorithm can point out parameters that could predict human behaviour. At the intersection of multiple data related disciplines including data mining, statistics, machine learning etc, a sophisticated system could be produced to make sweeping predictions about a group’s future behaviour. In a nutshell, predicting human behaviour needs complete understanding of the complex personality traits and emotional states. Moreover we aren’t naturally acclimatised to study subjective factors  that is easy for predictive analytics algorithms to parse. The transition from quantitative to qualitative data analysis calls for a more intensive method of data collection. By analysing the human behaviour, analytics businesses can become better managers, negotiators etc.

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Outsourcing the crowd

Technological innovation occurring all across the countries has made it easier to crowd source it to provide with better and faster services, funding and navigation. Powered by social media, new tech and web 2.0, Crowd sourcing is a sourcing model by which it engages a ‘crowd’ or group for a common goal — often innovation, problem solving, or efficiency. Being a blend of two words- crowd and outsourcing, there is no doubt that crowd-sourcing is helping various organizations to tap new ideas and solutions in order to encourage deeper consumer engagements, co-creation opportunities and reduced costs. It cuts across various dimensions and enhances all social and business interactions. Not only it is used by private organizations the way they work, hire, research and market but also by Governments to empower people and to enable them to voice their opinions in crucial matters of the country. Crowdsourcing also has the potential to revolutionize the system by touching across various fields such as science and technology and education. 


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How merging big data, IoT and cloud computing can lead us to a better future?

The buzzing technology giants, big data, IoT and cloud computing are completely separated disciples. But the union of these technologies is leading us towards an advanced version of our world. According to IDC, “Within the next five years, more than 90 percent of all Internet of Things data will be hosted on service provider platforms as cloud computing reduces the complexity of supporting the Internet of Things ‘data blending’.” Nowadays IT sector is shifting itself from product orientation to information based outcome orientation. The coupling of these technologies is helping our healthcare industry to become smarter, advances in self service analysis, beneficial for industrial IoT, adoption of edge-computing and so on.  

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How big data is complementing AWS platform?

We all are aware of outstanding facilities by Amazon web services in terms of data solutions and cloud computing. Everyday thousands of companies and industries are collecting and accessing terabytes amount of data.  The fabulous features of Amazon’s framework, the manageable & cost-effective databases and the storage facilities, simple and cost effective Amazon’s redshift data warehouse, business intelligence and analytics are actually endorsing big data analytics.

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Can domain be transformed through AI’s affection towards cloud computing?

AI and cloud computing is actually complementing each other. As we know, a typical computer can never hold the large amount of data processing by AI. We need a better alternative, so by combining AI with cloud AI can make use of dispersed hardware on cloud. Though this business companies can analyze and interpret massive amount of digital data. 

The future with the union of AI and cloud will definitely comes up with revolutionary innovations and technologies. Some best innovations can be integrated & server less platforms, the intelligent edge, data analytics and so on.

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What is the need to replace RPA implementation with OCR?

The enormous amount of data is increasing day by day. But, unfortunately it is reported that the inability to analyze the current data obstructs the businesses to take advantage of the services.  With the use of OCR (optical character recognition) our collected data makes some sense. OCR depends completely on manual checking. Instead of using OCR we can implement RPA (robotics process automation).

So, how we can use RPA? For more information go to:



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We all are aware of the elevation in technology due to AI, ML and Robotics. Now lets’ see its impact on in the side of business management.

  Applying AI to a machine makes it capable to mimic human abilities. Nowadays, machines with their highly advanced skills of problem solving, planning, perception etc are able to recruit employees of the company. These machines are firstly being used by recruiters to do their jobs more effectively and efficiently, but also it helps the candidates to find most relevant job as per their qualification and job requirements.   

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Visualising a three-dimensional from a two-dimensional snapshot

The world is really moving at a fast pace and is revolving around artificial intelligence. Do you think is it possible to have a three dimensional figure just by looking at a two dimensional figure? Yes, it is possible. The researchers can make a three-dimensional figure just by having few two-dimensional figures. But this system which they have made is not available for commercial applications. This system can be used for self-driving cars and household robots. This system has a very good grasping power as it is able to learn in a very smart way from little observations.

For more information visit : 

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