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

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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Briefing Data Science

After Artificial Intelligence and Machine Learning, the next most emerging field in todays world is the field of Data Science. It is said to be the cousins of AI and ML and mainly deals with data. It intakes data, uses processes, algorithms and scientific methods to extract knowledge and valuable data from large data sets. This field is need of each and every type of organization. Whether it be business or an IT firm, every organization needs data for improvement. Thus, outcomes from the processing of data are further used for decision making and for improving current functioning.

People often gets confused between Data Science, Data Analytics and Big Data. The key difference between them is that Data Analytics and Big Data are components of Data Science. Data Science extract values from the output of Data Analytics and Big Data to solve problems.
The goal of Data Science is to extract business-focused insights from business. This could help organizations in many ways.

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Innovations Finds Hood Under Predictive Analysis!

What could be better than knowing what future lies ahead us? Predictive Analysis is one such branch of data analytics which can be used to make predictions of future unknown events and is growing with a rapid pace. On the other hand, innovation is an ongoing process which finds its application in almost every field. Without innovation, we would not have reached the platform at which we are now. A number of technological achievements have improved our lives.

These days, Innovation has found a guide in Predictive Analytics that helps to walk towards success.  Many innovations are made but majority of them never succeeds. Predictive Analytics is going to play an important role aiming towards new products ensuring greater economic stability and progress in coming years. 

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A Look into Future – Introduction to Predictive Analysis

In this world of competition, companies need to take advantage of available data and take a look about what might happen in future. Predictive Analysis is one such branch of Data Analytics that aims to make predictions about future outcomes using various algorithms and other data analytics tools. Methods like data mining, big data, machine learning are back bone of Predictive Analysis and organizations are able to decode patterns and relations which helps them to detect risk and opportunity. Financial Services, Law Enforcements, Automotive, Healthcare are few fields which have already adapted this technology. 

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The Change in Data Management for AI

Nowadays, Artificial Intelligence is not new to this world. AI is used in almost every field, especially in the field of business. To make business related decisions, you need data. This data is analysed and then plans and actions are decided. From identifying the problem to discover actionable business insights, right analysis of data can transform business operations and take it to higher level. 

However, with the power of AI you can automate this task of data analysing, transforming raw data into actionable business intelligence. One thing for which Artificial intelligence is hungry for is data! The more data you feed it, the better results it gives to you. But with the vast amount of data that AI requires, it also follows the concept of “Garbage in, Garbage out.” Feeding the right thing to AI should be the up-most priority to get results. For this reason, many companies are making changes in their data management space.

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General Data Protection Regulation

GDPR gives customers the right to control their personal data, to modify, restrict or withdraw consent, and transfer data. GDPR helps in reinforcement of customer’s trust and reduce regulations. With unified data governance, businesses will comply easily with law regulations that redefine client personal data usage. While without it they will be at a risk of potential data breaches and loss of client trust. With GDPR, companies are required to have technologies and data and analytics solutions which help them manage their data to provide a positive customer experience across channels. In future, these organizations will be able to secure a competitive advantage by gaining client consent to use personal data and improve the customer experience. GDPR will help in increasing awareness of the value of personal data. Read more at:


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Impact of Data Analytics in Healthcare

Data Analytics has widespread impacts on not only commercial sectors but also healthcare. For example: First is by checking on hospital activities by maintaining relevant databases can help in finding inefficiencies in service provision and the overall costs of a healthcare facility can be reduced. Second is that data analysis also helps in allocating funds efficiently thus reducing the chances of embezzlement. Third is a database of patients’ records and medical histories can be maintained, which can provide a communication medium for the patient and every other individual working on that case. Also, if a healthcare facility is operated in multiple units, analytics can help ensure consistency across all facilities and specific departments. Lastly, storing staff data and keeping a check on their performance is important. Read more at:


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Digital Transformation in Retail Business

Given the power of the consumers, their demands and the most extreme competitions in the market every business is fighting to survive. Recently the businesses have invested mostly in digital transformation. Since data play a very important role, retail businesses are using the following trends to analyze those data. Firstly, retailers are considering data from in-store technologies and analyzing them to measure their effect on consumer's decisions and thus providing the products demanded more by customers.  Secondly, by location analytics, geo-targeted push notifications are sent out to customers and products are arranged more effectively in the stores.  Thirdly, with the help of explanatory analytics and predictive analytics more demanded products are stocked up. And lastly, cross-platform analytics helps retailers understand the customer's demands and provide effective support to them. Therefore, we can say that data analytics are an essential part of growth. Read more at:


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Analytics in Perspective of Indian Healthcare Market

In spite of a lot of buzz about big data and data analytics, only few organizations like Amazon and Google have managed to exploit them to the potential. With the shortage of doctors, lack of infrastructure and only 5% Indian population covered by health insurance, healthcare system in India is in a delicate condition. If used in the right way, the data analytics in healthcare system can push the sector to excellence by retaining right customers and assisting in providing cost efficient operations. Data analytics can also assist the insurers to increase the coverage of healthcare insurance from 5% to 50% of the Indian population. Read more at


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