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

Predictive Analytics in Call Centre

Call centres have started to explore new avenues using predictive analytics which improves their overall effectiveness and streamlines their operations. Traits that best prepare a person as a call centre representative are not clearly evident to officials but predictive analytics has made it possible to identify such traits. Call centres are merging CRM data into predictive analytics models as CRM has played a vital role in most organizations. The approaches used include analyzing variables contributing to customer discontent, building an automated system to transfer calls to representatives with varied expertise etc. Predictive analytics have been transforming industries and benefitting the aging ones by bolstering customer satisfaction and reducing churn rates.

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Artificial Intelligence in Religion

With machine learning making a huge leap of progress, it is being debated whether Artificial Intelligence and religion are compatible or not. Humans have been worshipping non-human entities from time immemorial and man-made robots would be superior to their creators. Religious modules have been entwined with technology to bear daily prayer apps and robot priest as the fruits. For example, the human robot named Peppa, an invention of Japan, could perform funeral entailing a cheaper cost than a human priest. Research at Faraday Institute of Science and Technology reported a strong correlation to exist between technology and religion which basically involved three stages-rejection, adoption and adaption. Down the lane, technology would become a ubiquitous part of all religion.  With a more objective analysis, AI provides a consolidated nutshell of all religious dialects.

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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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How big data is capable of predicting future online threats?

With the rapid rise of cyber crimes and online scams, people are getting insecure to share their sensitive data online. To encounter this appalling rise big data comes up with certain plans. So, how big data is used as a shield against online threats:

Network vulnerabilities

Detect irregularities

Detect irregularities in online behavior

Detect irregularities in device use

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TOPAZ: A worth gem news for enterprise data

Fast and high-valued data has become an urgent concern for business enterprises. Topaz for Enterprise Data encourages these ignited minds to:

 Understand the data even when they get diverted from the specific application, it also ensures data integrity and the quality of resulting code.

 Fast production of data for testing, training or business analytics that is capable of representing the actual data.

 Ensures security of sensitive business and personal data for relationships and characteristics.

 Easy conversion into required file type.

Topaz users can easily access all these competences from within a familiar Eclipse development environment.

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Using Business Intelligence for Betterment

Data is very valuable for any business. It can help in decision making, planning, and more. Here, Business Intelligence comes into action. BI uses various softwares, applications, tools and services that enables access to and analysis of data. This improves and optimizes decisions and performances. Using Business Intelligence can help you to get more value by improving customer service, employee productivity, and more.

Following are the few ways one could get more value from Business Intelligence:

  1. Build real-time BI into your customer-facing services
  2. Improve employee performance through BI
  3. Improve Customer Service
  4. Predict new revenue streams
  5. Automate budgeting and forecasting
  6. Shift the emphasis to analysis
  7. Embed BI into other platforms
  8. Cut time wasted on data gruntwork
  9. Bring unstructured data on board

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Bridging the Gap Between IT & Business

Business and IT are two completely different fields. Yet there is always a need to mix them for the betterment of both fields. However, there is a huge gap between these two and to bridge them a Business Analysts is comes into role. A business analyst is responsible to take IT and Business together by using data analytics to analyse the ongoing processes and methods, determine plans and requirements and recommend future plans on the basis of current studies. Now days, data is very important for a business. It can help in planning, decision making etc and thus business analysts are a need for an organization. It is important for a business analyst to be good in both hard skills and soft skills. He must be good at sharing the information he was able to figure out with the team. Similarly, he must have a strong IT background.

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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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Marketing analytics and its impact on the organisation

A recent survey states that the marketing companies will allocate their budgets to analytics. Though the top marketers report that analytics’ effect on company’s performance will remain moderate. There are two forces which couldn’t make this happen- the data used and the data analyst. This article discusses about why the organisations couldn’t realise the full potential of marketing analytics with their increased spending. Some of the main areas where the problems may arise are:
• The data challenge
• The data analyst challenge
• Algorithms and data resolving business plans
• Company’s goals
• Expanding skill boundaries

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The rise of analytics and artificial intelligence in India

India devotes only 0.6% of the GDP to research and development and it is  estimated that AI will increase global GDP by 14% by 2030. Of this trillions accrue to china and North America and only 957$ billion to India. The sector is dominated by American firms and around 70% of the AI research is carried out by non-Indian firms. The central government has increased the investment for digitalizing India. Also the ministry of commerce and industry has made a special taskforce on AI that encourages new policy development in these areas. AI is being used in the following areas:
• Finance
• Healthcare
• Manufacturing
• Crime detection
• Precision agriculture

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Boosting Healthcare Services by Big Data

In the predictive analytics market for healthcare, advancing Big Data processing tools, data mining and data organization are being increasingly used by market research firms to predict huge gains. Also by leveraging Big Data and scientific advancements, healthcare organizations are now being able to fuel the future of patient care. Despite the fact that there are several roadblocks to using analytics in this field there are also many ways by which Big Data can actually revolutionize the health field. Some of them are listed below as:


  • Big Data Boost on Precision Medicine and Research- By tapping reams of data from tools like Smartphone apps, genomics and mobile biometric sensors, precision medicines are now being able to treat individuals with therapies and treatment plans more specific to them
  • Tapping Big Data for Real-Time Infection Control- By using “sniffer” algorithms to assign risk scores, the data analytics pilots are identifying patients that are at risk for sepsis. Moreover the Big Data network is being able to predict and prevent infections out of the system thus squeezing the cost out and creating a safer environment for patients.
  • Cost Cutting with Patient Data Analytics- Predictive analytics is believed to cut 25 percent or more annual costs of more than 47 percent healthcare organizations over the next five years.


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Cyber Attacks: A potential threat

A potential threat that all businesses must be aware of is that of cyber attacks. Several data breaches have occurred in the past few years, the Equifax Data Breach in 2017 being the most noteworthy one. As a result, the legal repercussions have grown more stringent tightening the regulations on cyber security. Businesses could face severe consequences which includes consumer lawsuits, criminal charges for evading regulations and potential business shutdown. Consumers have sued companies on the ground of leakage of their confidential information furnished with respective businesses. The Health Insurance Portability and Accountability Act along with few other acts have strongly enforced cyber security regulations. Failure to abide by these brings in huge losses for the business concerned. To avoid such powerful legal ramifications, small and large businesses both must adhere to cyber security regulations.

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Value through Data Monetization

Businesses having their analytics processes in place now resort to data monetization to reap benefits to the next level. Data monetization refers to deriving financial benefits from data sources. The largest tech companies nowadays are geared towards delivering the best user experience and collects proprietary data on which researchers perform analysis to derive insights and plan subsequent plan of action. Companies deploy block-chain ledgers to reduce processing loads and specialize in combination of big data and block-chain to bring increased profits. Even the smallest businesses benefit from e-mail monetization by leveraging data collected through the cheapest avenues possible. From managing a blog to small e-mail lists, linking helps in generating leads that turns data into money.

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Innovation and Location Analytics

Almost 97% of the large companies leverage analytics and such data-driven businesses have emerged to be nearly 6% more profitable as indicated in a study by IDC. Location can be an address, a delivery route, a service boundary or a sales territory and along with disruptive technologies such as machine learning, artificial intelligence, location analytics helps in brisk innovation of the players in the financial realm. It’s required to move towards the analytical maturity curve implementing a 360 degree approach. With the help of location analytics, the real change can be observed between growth of new applications and innovations, taking the trend from static to dynamic content followed by connecting content. There are certain prominent cases where location analytics has helped in increasing profits that include Tamecco, the Tokyo-based company that focuses on customer satisfaction to achieve the moto that location-based analytics assists a lot. Among others are the Grey Jean Technologies and Teemo with the tagline of “Drive-to-Store” engaged in revolutionizing retail advertising. It is expected of industries to benefit from technologies like geospatial intelligence, forecasting and visual analytics.

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People Analytics led growth

The major challenge facing any business organization is to use the massive amount of people data to nurture relevant insights on improving business performances. Analytical techniques varying from reposting to predictive analytics to experimental research help in directing company HRs. Following the comprehensive implementation of the cloud HR systems, companies are using data in all facets of workforce management and operational improvement, moving beyond employee engagement and retention. HR has been emerging as the “intelligent platform” with analytics becoming a critical part of the high-performance hiring in the talent acquisition trend. In order to reduce unconscious bias in hiring, businesses resort to the use of interview data and careful parsing of job posting language. With commensurate technological advancement, analytics will deliver personalized recommendations.

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Artificial Intelligence in National Security

Artificial Intelligence coupled with machine learning algorithms has taken the defenSe industry to great heights by bringing in this paradigm shift from descriptive to prescriptive analytics where soldiers have the knowledge of potential ceasefire beforehand. Data mining and machine learning algorithms help in analyzing unusual movements round the border thus making combat systems more effective at the decision juncture. The US Defense department has approximately spent US$1.6 billion in cyber defense. Although India is not far in the race, it is catching up for the time being. Many India based analytics firms are providing technologies to measure border infiltrations, detect smuggling and possible future agitations. However, India is progressing in the race of cognition of AI with machine learning to give a new shape to the country’s military services.

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Major Data Disruptions: A growing concern

Big data, AI and Machine Learning has given us an era where technology drives humanity but such digitization comes with certain cons as well. Handling a titanic amount of data seems to be harmless as long as businesses know how to use it and what purpose the data is serving. There should be a thin line between the actual required data and the data that has been irrelevantly collected. Big data has held us high but has failed to keep up with the privacy laws. Major data leaks and cybercrimes are the two prominent threats that growing technology has brought with it. Identity theft, classified as nuisance data breach by the index, has resulted in the loss of nearly 200 million records last year. Data manipulation and data integrity attacks are becoming popular as the world becomes more compact and technology dependent. To ace with this, the company must implement measures such as encryption, user access key etc. Fighting with the big data flood is not a company’s cup of tea as it involves various risks to triumph over. The big data storm must be dealt with carefully with proper planning and execution. 

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The concept of distance metric

Multi-task metric learning was introduced by Caruana in 1997. The performance is improved by considering multiple learning tasks and sharing information with other tasks. The metric is used as a measure of similarity or dissimilarity and there are various distance metrics such as Euclidean distance, cosine similarity, Hamming distance, etc. there are various evolving challenges in obtaining training data set which has become a costly process. To overcome these problems multi-task has to be introduced. This article includes the basic concepts, strategies and applications of metric learning.

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The race of embracing data-driven culture

The companies are of the concern for big data and its use. The big companies fear that the emerging start-ups in the fintech space will disrupt them. The surveys of 2018 on AI show that both AI and big data will be disrupted by start-ups. Exploring data and making as much use of it is in the hands of the companies. Machine learning is one of the best tools to make use of the data very effectively and quickly. Generally, companies are investing in traditional analytics, big data and AI. The companies are not making fast efforts to move towards a data-driven culture.

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The future of people analytics

People analytics is the sharing of data across the employees of the companies to make the companies grow faster. The managers of the company then become serious with their work. According to a survey by Deloitte, 32% companies are ready to adopt people analytics. However the start-ups doesn’t find these necessary as they are expensive tools. Some of the situations which a company can avoid using people analytics:
• Poor candidate experience
• Mismatched employees
• Incorrect assumptions of employees
• Missing trends
• Costs of employees
Therefore stopping an unemployment claim is also a part of savings for the company.

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