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

Use Data to Your Advantage

Big Data in theory is easy to understand. But, unearthing insights from streams of data into action and make a difference to your business is not easy. Companies have now found out how to not only capture data but also connect them to create BI which helps in getting feed back into the business to give them a competitive advantage. Therefore, a set of dependable best practices can help you take advantage of the big data momentum. Read more at:


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Automating repetitive work

Smart work is vital for any business as you and your employees can focus on more important tasks. Sometimes, new businesses struggle from lack of time and resources to grow their business proficiently. Automating tedious and repetitive tasks through software can save time and thus progressing overall productivity as automation means automating one or more work processes with the help of technology to reduce or eliminate human intervention. To know more about automation, read at:


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Use of Predictive Analytics in Education Sector

The role of education as well as education institute has changed over the years and is becoming more daunting to achieve. Universities, nowadays, have a lot to do to equip their students for the jobs that are to come. Universities can improve the quality of education is one way to do is to use predictive analytics. This article explores how big data is influential in improving the quality of education by allowing educators to monitor progress, alter curriculum based on data and customize the learning experience. Read more at:


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Improving customer service through automation

Customer service is very important for any sales team but, they can be overwhelmed by poor ticket deflection and routing. They are equipped with all answers but, if they need to sort through through issues outside of their filed, then they are not maximizing the ways in which they can help customers. This is where ticket deflection and routing come in to make the most of the time and effort of your customer service personnel. This article explores the ways ticket deflection and routing can help customer service teams. They are: Automated routing improves efficiency, Ticket deflection increases self-service usage, Quick self-service access makes it easier to meet SLAs and Sentiment analysis for smarter routing. Read more at:


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All about web presence analysis

Web presence analysis helps you determine how your business is viewed when someone searches for your products and services. It is imperative for all businesses should develop an extensive digital footprint as part of online marketing activities. The bigger the footprint, the more likely your business can be found when someone is searching and if your business is not visible in the search engine results, you lose an opportunity to attract a customer.  To know more about web presence analysis, read :


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Decoding the Mystery of Perfect Ads!

Advertisement is one of the major ways through which businesses can attract customers. A lot of money and time is invested in order to create ads. However, these days a helping hand has come for rescue and is successfully able to attract customers by presenting customized ads. Machine Learning Algorithms, Artificial Intelligence and Deep Learning have come into play. With the help of these technologies, customized ads can be created based on the current searches done by customer. For example, you recently searched for “affordable mobile phones”. These learning algorithms tracks it down and soon starts displaying mobile phones ads presented by various companies. Other than that, Data Mining also plays an important role in this. Among various data that is available on world wide web, data mining algorithms browser and stores valuable data. 

Read more about this at


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Why is machine learning becoming inevitable for organizations

These days, developments in data analytics and business technology is that of machine learning. Machine learning is becoming a business-critical technology, machine learning makes computing processes more efficient, cost-effective and reliable and thus accelerate every aspect of business decision-making. Machine learning is implemented in most industries where it presents a great opportunity to improve upon existing processes as it has the ability to process highly complex, time-sensitive, and fluid data for applications in virtually any industry, such as: Tracking customer/client satisfaction in real time, analyzing and responding to market trends, tracking and setting prices according to demand, combating fraud, calculating risk, improving customer service through real-time etc.  Read more at: Read more at :


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Latest trends in Big Data

Analytics and precisely, big data has been a game changer for organizations across industries and revenue size as it helps companies to process data of great complexity and size at a speed and with accuracy so that the management can take better decision. Big data technology can help huge unstructured data to be properly indexed and searched through those legacy records in record time. There are many other circumstances where big data can push an organization’s success and help it make its processes smoother and more efficient. Read more about the latest trends in big data at:


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Importance of IoT in automobile industry

According to Wall Street Journal, millions of cars are rolling off dealers and with built-in connectivity, automobile companies are gaining access to unique amount of real-time data that in turn allows them to track everything from car location to how hard it is braking and whether the windshield wipers are on or not, leading to a potential beyond vehicle operation and automaker information. These days, new vehicles have a robust tracking technology that dynamically tracks the vehicle’s movements. This valuable location analytics offer understanding the patterns of the driver like where they are going based on past habits, time of day, route taken, etc. Read more at:


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Importance of Proper data analysis and usage

Data is like an untapped stream of gold. If used properly, it can help any business to start climbing of the stairs of success real fast. History has plenty of examples like Hershey’s, Caterpillar, Vermont Electric Power Company and many more. These companies began to store, segregate and analyse their data. The data includes their operational activities, sales, purchases, monitoring employees, data about competitors and many more. Proper usage of this data can bring a digital transformation in the organisation. Data analysis often results in cut in operational cost. But its benefits doesn’t just limit till the cost cut. Data analysis can also show a path for revenue generation.



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New job trends in cyber security in IT sector

Cyber security is of utmost important to any organization and of late there has been a tremendous rise in cyber security jobs. This high demand comes from expansion in the interconnectedness of gadgets and computing systems, thus increasing the potential points of intrusion. So, here is a list of some trends in cybersecurity that will affect jobs in the IT sector. They are: Artificial Intelligence, Internet of Things Openings Outbound and Opening Inbounds, Cloud Services and Mobile Devices and Third-Party Apps. to know more, go to:


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Data Loss – A Threat to Company!

Data is the most valuable asset for any company and any person dealing with this data needs to be cautious. Modern businesses rely on data. They store, process and access data for information gathering and use it for decision making. According to reports of 2017, a single mistake in handling this data can result into loss of nearly $3.6 million. 

However, data can be loss due to various. Few of them are:

1. Human Error

2. Hardware Failure

3. Theft

4. Online Crime

5. Natural Disaster

The best way to deal with this is to take prevention and keep an up-to-date recovery plan and a 3-2-1 backup strategy, i.e. there should be three copies of data, kept in two different mediums, and at least one of the backups should be off site.

Read about it at:


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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.

Read more about them at:


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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

Read more about them at:


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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

Know more about them at :


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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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