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

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Rules to Follow in Data Analytics

Analytics is one of the major jobs performed in companies these days. Daily operations are carried out involving data that presents us with results which helps an organization to carry out further processes and helps in decision making. Effective business intelligence is the product of data processed. This data is raw and can be either structured or unstructured. 

Firstly, one needs to manage data before processing it. Rules are to be set for the analytics process which can offer better insight and an easy processing. Below are the five rules that can help in managing your data more effectively:

  1. Establish Clear Analytics Goals Before Getting Started
  2. Simplify and Centralize Your Data Streams
  3. Scrub Your Data Before Warehousing
  4. Establish Clear Data Governance Protocols
  5. Create Dynamic Data Structures

The field of data analytics is always evolving and thus it is important to create a proper structure that can help in future. By establishing them we can enhance the quality of data processing.

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Learning Neural Network and Deep Learning

Artificial Intelligence is a combination of various sub topics. Whether it be Machine Learning, Deep Learning, Neural Network etc, each one of them finds shelter under artificial intelligence. Below are the basics of two important topics – Neural Network and Deep Learning.

Neural Networks: Neural network is modelled in the same way as human brain. It compromises various algorithms and aims to find relationship in data provided by us through processes that mimics like human brains. It is one of the trending technologies and is finding its applications in various fields like trading, medicine, pattern recognition etc.

Deep Learning: Deep Learning is basically a network composed of several layers. It is also known as ‘stacked neural networks’. It is a subfield of machine learning and is inspired by the structure and function of brain. It can be widely used in classification, clustering, predictions etc.

We are walking towards the era where technology will dominate and the main technology will be artificial intelligence. Hence it is important to get an insight of what we will be with in future. For a beginner, these topics might be confusing but they form the crust of artificial intelligence.

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Reasoning for Slow Pace of Digital Transformation

Digitalizing is becoming the need of hour in every business. Every organization is trying to in cooperate technology as per there needs. Companies believe that cloud storage, analytics, mobile and social advancement are all the tools they require for digital transformation. However, this is not enough. Digital Transformation is still lagging behind even after great efforts by organizations. One reason behind it is the fact that one could not match the speed at which technology is growing. 

Following are few challenges that organizations faces while trying to keep digital transformation up to date:

  1. Lack of vision and leadership
  2. IT and business don’t see eye to eye
  3. Little to no engagement
  4. Transforming ops is hard
  5. Governance is lagging
  6. Critical functions are being shortchanged
  7. Shying away from the cutting edge
  8. Metrics misalignment
  9. Failure to change culture
  10. Not failing enough

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Finding Data!

Data is very important for various technologies. Whether it be Artificial Intelligence or Machine Learning, Data analysis or Research work, Data is mandatory to implement them. However, the task of finding right data is very tedious and time consuming. One needs to find data that is most appropriate in terms of information available, size and other factors. 

Every day a huge amount is data is generated on internet. To our help there are few open data sources that are free to use. This data can be in raw form which might need further processing. But to start with the process and to get a data set, one could visit below mentioned sites that provides data for free: 

  1. Kaggle
  2. UCI machine learning repository

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Aiming to Become A Data Scientist? Read This!

Data Sciences is a very vast field and in recent times, there is a high demand of professionals in this field. Dealing with data is not easy. Data sets available with companies are very large and to extract meaningful data is a tough job. Thus, the job of data scientist is becoming very important for decision-making and is based on automation and machine learning. The main role of data scientist is to organize and analyse data. Other than this, data can help in predictions, pattern detection analysis etc. All this can be done the help of some software which is specially designed for the task. The responsibilities of data scientist begin with data collection and ends with decision making on the basis of data.

To know more about the key roles of data scientist, requirements and skills visit:


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Dealing with Predictive Analytics Challenges

One of the most trending and look for technology, Predictive Analysis is a powerful tool that can help us to forecast and predict what lies ahead us. However, it is usually accompanied by few issues that user encounters while using it. They might not be visible during early stages of development but they can become great concern when they will not be able to deliver results to customer. Prevention is always better than cure and thus it is recommended to study the technology well before use. 

Following are few tips that one should use to avoid and resolve common project challenges:

  1. Create and execute a formal strategy
  2. Ensure data quality
  3. Manage data volume
  4. Respect data privacy and ownership
  5. Maximize usability
  6. Control costs
  7. Choose the right tools

    To read more about them visit:


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AI Contributing Towards Medicine

Artificial Intelligence is spreading its wings and is coming into rescue in various fields. One such field which comes into rescue for humans is the health care sector. Combination of these two fields can bring great advancement in health care sector. Artificial Intelligence and Machine learning have already come into action in medicine. Following are the top 4 applications:

    1. Diagnosing Diseases: Not all diseases can easily be rectified. This could be time consuming and expensive. Here, various Deep Learning algorithms prove to be a solution. This focus on automatic diagnosis, making diagnosis much cheaper and accessible. 
    2. Developing Drugs Faster: Drug development is a time taking and a tedious task. It involves analytics and various rounds of testing. AI has already aced in speeding up the process.
    3. Personalizing Treatment: Same medical procedure can not be carried out on every patient. Choosing the course of treatment can be a difficult and a great responsibility. Machine Learning can automate this task. It can help in designing the right treatment plan.
    4. Improving Gene Editing: This is a technique that relies on targeting and editing specific location on the DNA. A careful selection needs to be made. Machine Learning models have successfully been able to predict target and effects successfully.

To


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How big data can help in customer service domain?

In every enterprise the customer care services are the most essential part because it not satisfies the customers but it also leads to more productivity. To build the customer loyalty and drive your business to heights it’s important to care the customer service domain. Services of new customer care software platform which involves big data has took this domain to a new level.

For more information go to:



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AI based First Forex trading robot

Foreign exchange market is the word which is enough capable to draw attention of many people. Some people treat it as a fast money making technique while some people says it’s a way to enter the gambling world. Foreign exchange market is just a virtual platform which involves trading of different currencies. So what forex trading is? How AI and robotics can be involved in trading? What actually forex robots can do?

Want to know? Visit:


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How to become a data scientist?

Data science comes with a new era on IT industries. From AI, ML, big datadata analytics and many more data science is proving its importance. With the emerging business plans on big data the requirement and demands of data scientists are also getting higher. Here are the guidelines to the students who want to pursue data science as their career. 

 Education background should relate to computer science.

 Beginning of your career experience and work focus

 Learning opportunities and certification

 Mid-career experience and certification

 Data science expertise and professionalism

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How AI and ML can contribute towards developing an intelligent cloud?

With reference to the aura spread by artificial intelligence and machine learning every enterprise is making their ideas with the relevance of these two giants. We are aware of the concept of cloud computing that it provides the storage and networking space over the internet which eventually reduces human efforts and cost. This sounds absolutely awesome. But what’s next? Can cloud be developed as “Intelligent Cloud”? Introducing the union of AI, ML and cloud, this will definitely raise the standards of cloud computing in future. The intelligent cloud will have the ability to learn from the enormous amount of data, builds up predictions accordingly and end up analyzing situations. This platform seems to perform tasks with high speed and provides greater efficiency.


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How Artificial Intelligence is helping in startup plans?

Since last few years many startups has come up with some fantastic ideas which expands the scope of AI all over the world. Indians are also implementing various brilliant ideas of AI in their business startups. Here is the list of top 10 AI based startup plans which are leading the race.

1) build multi-lingual chatbots for businesses

2) Fablulyst: helps all the online shoppers to buy based on their inputs.

3)  Artivatic data labs: helps large businesses and developers to come up with intelligent products and solutions with minimal development time and efforts.

4) assists people in their daily core tasks

5) TAO automation: provides automated consultation which helps people in further optimization process.

6) build up a product which is capable of solving various analytics problems.

7) AskArvi: smart personal insurance platforms

8) ThirdWatch: E-commerce fraud-prevention platform

9) Embibe : educational startup, uses self-learning algorithm and machine learning

10) Mad Street Den: improve retail experience all over the world.


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How ActiveOps is securing G-cloud for betterment digital operation management?

ActiveOps is one of the leading providers of digital operation management, but somehow it couldn’t find any place to store G-cloud data with security. But now ActiveOps has come up with G-cloud 10 framework which assures and deliver greater efficiency and productivity by combining software and services.The G-cloud is made up with the ideology that most of the public sector can have the maximum advantages of cloud based automated services with more groundwork and prognostication.



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Few Softwares to Simplify GST

The herculean task of shifting to the new digital regime of taxation is making the country abuzz with the over explosion of information. This has mostly bewildered the entrepreneurs and SMEs as to how to make a business compliant to GST. Few GST compliant software solutions that can be used by a GST-ready entrepreneur are listed below.

·         CLEAR GST SOLUTION BY CLEARTAX- Priced at Rs. 10,000, the first cost effective well built cloud GST solution software released by Cleartax solutions is Clear GST. However it has few weaknesses including the need for separate accounting software for financial statements, is useful for small businesses only, no availability of offline backup and so on.

·         TALLY ERP 9 BY TALLY SOLUTIONS- Priced from Rs. 18000 to Rs. 54000(GST feature free if already having a license), Tally ERP 9 will be easier and more delightful to use than others with the only weakness of having no basic accounting system of debit and credit.

·         GEN-GST BY SAG INFOTECH- Priced at Rs. 2500, this software is different from others in the sense that it is available on all platforms like desktop, cloud, SAAS and so on. Some of its weaknesses involve its application to small businesses and need for separate accounting software for financial statements.

·         PWC INDIA- A GST solution with end-to-end automation aimed at helping companies to become GST compliant has been recently launched by PWC, the company which targets big business corporations instead of people.

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Hybrid Cloud Model and its Applicability in Healthcare Organizations

Adopting a hybrid cloud model, a cloud computing environment using a mix of private cloud, third party public cloud services and on premises, has significant benefits if used in healthcare organizations. It not only allows them to take advantages of existing IT services and quick scaling out new resources but also ties various systems together for a seamless end-user experience. Moreover the journey towards the cloud does not need a complete infrastructural haul. However when considering a transition to a hybrid cloud model, the IT decision-makers should think through several considerations including leader requirements for shifts in culture, licensing, processes, system and host of other issues. Few tips for any healthcare organization to smoothen the path towards a hybrid cloud model are listed as follows:

·         Assessing and evaluating the cost-saving structure specific to the healthcare organization.

·         Identifying with the effects of cloud control and its management.

·         Preparing beforehand for impacts on the Service-Level Agreements.

·         Maintaining Software Licensing Compliance.

·         Proper Skills Developing to Support the New IT Environment.

·         Preparing Healthcare Staffs for Cultural Shifts.

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Big Data: A goldmine for automotive industry

As per the reports by SNS Telecom & IT, Big Data investment will cross $3.3 Billion by the end of 2018. Real time and historic data is rapidly gaining attraction towards diverse range and various sectors. There are many key findings in the report generated by SNS which ensures the widening scope of Big Data. 

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How AI is a blessing to all the organization?

We are aware of the spells by AI in changing the appearance of business process both online as well as offline. AI has replaced many human functions in the industry through automated systems in field of communication and answering phone calls, emails etc. Technology giants such as Microsoft, Google, Amazon are investing their more power, time and money in strengthening their AI sector. So how AI is helping these organizations?

   Want to know, visit:


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How deep learning is a benefaction for police?

Investigation is crucial part for police because it only helps to catch the real criminal. But after collecting all the evidences and information, the case becomes more difficult to figure out as each information tells a different story. Nowadays when technology is on the peak of its uses, deep learning is giving its contribution by helping police through video footage analysis. Videos gathered from different number of sources are analyzed by a smart software which implements algorithms of deep learning for detection. Want to know, how it works? Visit:



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Real time challenges that needs to be overcome while implementing big data

It’s a difficult approach when it comes to the adoption of analytics facing real time challenges. It’s tough to analyze big data and assemble it in a single row. There are times when the minutes and seconds count are very crucial and no delays can be accepted. With the demand of more precision and accuracy we just cannot avoid the risk presence while accessing the data. But real time analytics demand much of our efforts and hard work to overcome the challenges and get that precision.  

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Expanding the scope of data access across the organization

We are aware of stepping up standards of AI and its importance for data scientists. But, we cannot resist AI to limit its scope till technology only. With the advances, AI is setting its root in business organizations too. AI as a business tool comes up with many advantages to expand data access.

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