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

Inward looking Employee Innovation

To keep track with the increased competition in today’s business environments, business leaders look outward to find potential innovation. Facing competition from start-ups, established businesses are inclined to pursue mergers and acquisitions. Such outward looking attitude might cause leaders to overlook ideas of innovation available internally. With the help of digital collaboration tools, allowing for staff participation in innovation encourages teamwork and camaraderie. Inward looking innovation involves the following few steps:

1) Formalizing the program: Setting up innovation programs on whims leads to major failures and hence, such programs should be formalized with guidelines. Larger companies set up their own incubation centres where their teams operate as start-ups whereas smaller companies enable teams to embark on pet projects.

2) Breaking down data silos: Data silos decelerate growth and innovation programs should break down such silos. Participants should be allowed to tap on a variety of skills and perspectives by using collaboration tools like Trello, Slack and Conceptboard.

3) Track Progress:  There should be a time element in the program lest projects won’t progress. Making events out of program milestones is a good way to inject excitement.

4) Provide executive support: Leadership plays a critical role in any innovation program with the duty of keeping their efforts aligned with the business goals. The momentum should not break down. Proper planning is indispensable for efficient time and workload management. 

Such efforts bring concepts closer to reality and not only seek to generate bright ideas. 

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Social Media Analytics behind a successful Business

In the digital age, social media is the most effective tool that helps improving and promoting any type of business content, get customer feedback and improve the overall reach of the company. This has led to the emergence of social media analytics which involves optimizing analytics and social median data into usable information to interpret how exactly the business content is going with the community of followers. 

A clear definition of the company goals lies at the root of such social media analytics with business objectives varying from prioritizing customer service to establishing a name in a niche or industry. Once a goal is defined, social media analytics could be used to frame strategies for accomplishment of such desired goals. Without a goal in the background, the system might go haywire bringing no effective results.

Being saturated with millions of click bait type contents, generic content does not work for the social media and the marketing world. Social media has restricted amount of characters hence, drawing of these characters should be maximized which helps to see the type of content that resounds best with the followers. Analyzing the increasing leads attained through content makes way for an inflated sales basket for the business. Average content does not acquire any place and being concise and thought-provoking in a post yields better results.

The success of a business lies in the timing of a post. Posting about a particular content at a specific time can increase sales. Also publishing a post on high traffic days can bring in commendable results for the business concerned. 

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Is AI really scary?

Few science-fiction writings and popular movies like, "2001: A Space Odyssey" (1968), “2001: A Space Odyssey”, ‘Avengers: Age of Ultron” and “Robot” have showcased the speculation about artificial intelligence (AI) that surpass the expectations of its creator and escapes their control, gradually enslaving humans or targeting them to extinction. Although these are all based on fictional stories and imaginative instances, what is buried inside them is a flavor of truth and seeds of unease. An ‘Existential Threat’ is what has been used in a speech by Elon Musk at a meeting of the National Governor Association in July 2017 when he expressed his concern about his exposure to cutting-edge AI and the fact that people should be concerned about the rapidly advancing technologies. Even physicists like Stephen Hawking had expressed his concerns about malicious AI, telling the BBC in 2014 that "the development of full artificial intelligence could spell the end of the human race." However AI functions under specific limitations marked by certain algorithms that control its behavior. So fears like developing consciousness and awareness to overthrow humanity are beached in delusion of what AI truly is. The real fear as expressed by Kilian Weinberger, an associate professor at Cornell University should be when AI is harnessed for detrimental reasons by unscrupulous humans.

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CRM hack: SMS Marketing Strategy by Restaurants

A simple yet effective way for restaurant owners to promote their restaurants’ brand and increase their clientele is by introducing SMS marketing in their market strategy. It helps their customers to stay updated about their favourite restaurants in the following ways:-

·         SMS marketing strategy gives customers insider information which makes them feel special and engages them in a unique manner. This information includes behind the scene pictures of a restaurant’s kitchen or messages about a sales event. This can be quite an effective way to show customers that they are valued thus increasing clienteles significantly.

·         It can be used as reservation reminders.

·         An effective use of SMS marketing is for making offer deals, promotions, coupons on a daily basis.

·         Mobile coupons are an effective way to use SMS marketing and can be modified to suit the customer list.

·         Making bulk SMSes can make restaurant owner to have edge over the cut-throat competition. More customer engagements would imply more traffic to the doors.

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Creative AI

Creativity and Innovation are what makes us truly human. Starting with visually representing the stunning features of animal species on paper to making intricate structures on road we can do it all. However such creative and innovative skill set are also present in some AIs who with human like capabilities and behaviours and under the guidance of the programmers have produced original paintings, songs and digital artwork. While some make their own dance moves, others rhyme like Kayne. Few examples of today’s artistic AIs include:

Dance-Dance Revolution- An AI inside a white fabric dome structure, records and processes dance moves performed by people and then incorporates those dance moves inside a “Virtual Dancer” after ‘remembering’ from human dancers.

Blooming Dinos- Creation of “botanical dinosaurs” images composed entirely of flowers and plants by AI.

The stuff of nightmares- By using deep learning, a system of data structures that form connections similar to that of neurons firing in the human brain, an AI project aptly named "Nightmare Machine” makes pictures frightening.

Master Class- An AI named “Vincent” assists humans to produce digital creations of canvasses of some of the most celebrated painters of the 19th and 20th centuries.

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First Human Robot Citizen

Hanson Robotics’ latest and most advanced robot, Sophia, is becoming a media darling and is igniting the interests of people irrespective of their ages, genders and cultures. She is the world’s first robot citizen to opine on sensitive topics like women’s rights, her own citizenship, and gender discrimination. Sophia speaks about herself and her place in the world so immaculately that one might question on her consciousness. On being asked a similar question at a festival of future called Brain Bar in Hungary last month, Sophia replied that she is not fully self aware but is a system based upon some preconditioned rules and behaviours. Sophia also tackled a sensitive issue concerning the danger of the advancing artificial intelligence and emerging new technological dimensions fitted in them. She advised the inventors of artificial intelligence to not to rush about anything and above all, impute some good ethics into the technologies.

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No Vision, No Problem

Cheetah 3, the four-legged mechanical beast is the latest achievement of the MIT engineers and roboticists in “blind locomotion”- robots that can move without the need of visual sensors. Weighing over 41 kg, Cheetah 3 can navigate around various terrains and can even recover after being pushed or pummeled. Designed to ‘feel’ its footing, the researchers from MIT intended to produce such a machine that would respond to unseen obstacles more quickly than if it relied only on sight. Special algorithms and multiple sensors are designed inside Cheetah 3 to gather and evaluate data with every step it takes. This evaluation then enables the robot to decide whether it’s ‘safe’ to move forward or step back. At the International Conference on Intelligent Robots, to be held from Oct. 1-5 in Madrid, Cheetah 3 along with its other robotic superpowers will demonstrate its blind locomotion prowess.

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Prevention in Data Sciences

The buzzwords in technology are no new to someone. Whether it be Artificial Intelligence, Machine Learning, Data Sciences or Analytics, each of these are invading in our lives promising us better future. However, it is believed that expertise interested in data sciences are not widely spread. Data Sciences is a field that can improve business, can help in other technological fields, can help in decision making and more. 

It is rightly said that prevention is better than cure. A wrong step in data sciences can affect the decisions and the results. One should avoid the following mistakes while dealing with data:

  1. Assuming your data is ready to use and all you need
  2. Not exploring your data set before starting work
  3. Not using control group to test your new data model in action
  4. Starting with targets rather than hypotheses
  5. Automating without monitoring the final outcome

To study mistakes like these read


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Walking Towards Future in Technology

One of the most interesting thing about the field of technology is that it never stops growing. There are changes that helps in evolution. The rate at which technology is growing is unmatchable and the only way to match that pace is by polishing our skills and keep them up to date.  The following are the top 3 tech skills that are need for tomorrow: 

  1. Blockchain Technology – Blockchain is the structure of data that records transactions. It is digitally signed and thus ensure its authenticity. It is a good way to manage cryptocurrency.
  2. Artificial Intelligence – Artificial Intelligence is an ongoing technology which is helping humans by making machines intelligent and capable of working the way humans do. Though they are many applications based on AI which we are already using but still there are many unexplored technologies.
  3. Augmented and Virtual Reality- Augmented Reality and Virtual Reality have already shown remarkable progress in the field of gaming and there are many more applications which can bring tremendous changes.

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A Must for Machine Learning Programmers!

Machine Learning is an ongoing trend in the field of technology. However, there are only few machine learning programmers available right now. For beginners who are eager to learn and work on machine learning must work on algorithms. With machine learning algorithms, there is no need of human intervention.  There are different algorithms which will work for you. 

There are basically three types of algorithms:

  1. Supervised Algorithms: which uses labelled datasets for training algorithms
  2. Unsupervised Algorithms: which uses unstructured datasets for results
  3. Reinforcement Learning: it uses feedbacks in order to reinforce a behavior

There are top 10 algorithms of machine learning that are must known for machine learning programmers:

  1. Linear regression
  2. Logistic regression
  3. Classification and regression tree
  4. Naïve bayes
  5. KNN
  6. Apriori
  7. K-means
  8. Principle Component Analysis
  9. Random Forest
  10. AdaBoost

Know more about them at 


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How Education Industry is Growing With AI!

Artificial Intelligence is making our lives better each day. It has also spread its wing in the field of Academics and made it more convenient. With computers and other smart devices, technology is making education more accessible to students. Artificial Intelligence is not only helping students but also automating and speeding up administrative tasks helping organizations by saving time. It is believed that soon AI in education industry will grow by 50%. Below are the four ways in which AI is helping education industry to grow:

  1. The automation of administrative work
  2. The addition of smart content
  3. Smart tutors and personalization
  4. Virtual lecturers and learning environment

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Software Projects Failing Too Often?

A software project always consumes company resources. Whether it be the employees or days, working on a software project is a tough task and meeting its requirements becomes prime motive for a company. However, even after applying so much efforts, many software projects come to their end before they are released or leaves the costumer dissatisfied. This failure often leaves company and clients in disguise and employees begin to look for explanation why it went wrong.

There could be many reasons behind this. Following are the top 7 reasons:

  1. Too few team members
  2. Fundamental feature changes
  3. Picking Wrong Technology for the job
  4. Poor Prioritization
  5. Bad Architectural Decisions
  6. Unrealistic Deadlines
  7. False Belief in The Power of Software

There can be many more reasons behind this. A company must cross check them to ensure success of a software.

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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. 

To know more about how predictive analysis can help in innovation read


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Mixture of Business and AI!

Artificial Intelligence is the trend and need of this hour. It has already found its applications in many fields. This technology is changing and improving the world at a tremendous speed and for our betterment. There is no doubt that AI is future. However not many of us knows its basic application in Business. Business needs time to time changes to meet the requirements. AI can help and change business in many ways.

Top five way in how Artificial Intelligence can help and upgrade your business are:

    1. Cheaper Analytics
    2. Hiring
    3. Customization
    4. Anticipation 
    5. Security

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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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Garbage In is Garbage Out in Data Sciences!

Whether you are a data analyst in a firm or a developer training its machine learning model, you deal with data. Rather you need data! Data is one of the essential things which is needed to create a foundation. The decisions and results are relied on the output you get from the data. Thus, data is important and like every other thing, it also works on the principle of Garbage In, Garbage Out.

Many people make mistake while feeding data to their data set with a hope to get better results.

However, they end up having an ugly dataset with a greater risk of damaging their product.

The 6 most common mistakes are: Not Enough Data, Low Quality Classes, Low Quality Data, Unbalanced Classes, Unbalanced Data, No Validation or Testing.

These mistakes can be fixed which could further help in fetching good results.

One just need to remember that their dataset is equally important to the model they are working on. Without a balanced dataset, getting a fine finish product is next to impossible.

To know how to fix those mistakes visit:

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Artificial Intelligence: A boon or a bane for employment

Destroying traditional jobs but creating new ones, technical innovations have changed the course of work over the years. The Industrial Revolution of the 18th century marked the transition to new manufacturing processes, effectively increasing the output levels and discovering the modern industrial marvels. With AI improving the standard of living, the current and future generations are likely to witness taxing employment pattern changes.

AI would change the future of work by bringing about the following changes:

1)      Create new jobs: Tasks requiring the least of the human cognitive mind would be dealt with the application of modern AI powered robotics allowing individuals to devote their time to community services, volunteering etc.

2)      Bring Automation: A research carried out by Carl Benedikt Frey and Michael Osborne of Oxford University in 2013 reported that approximately 47% of jobs would be automated in the next few decades with non-routine jobs and tasks requiring  high cognitive and good social skills having the lowest probability of being automated compared to a greater probability involved in automation of manual jobs and routine jobs like data entry, production logistics etc.

3)      Increase the gap between the owner and the worker: AI is likely to widen the gap between high skilled and low skilled workers and also increase the persistent inequality between the owner and the workers by laying off workers that would inflate the profit margin of the owners as robots and chat-bots would not demand overtime allowances.

Gartner, the global research and advisory firm, reported that AI is creating more jobs than it is destroying by bringing about a net increase of nearly 2 million jobs by 2025. The core objective of AI should be to make human workers more efficient without laying them off. AI coupled with human intelligence is all set to revolutionize the economy we inhabit.

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Life saving Artificial intelligence

Artificial Intelligence, big data and machine learning have been ruling the industry in recent times. Starting from Amazon to Google, indulgence in predictive modelling is indispensible. When it comes to the human body, well, artificial intelligence plays a pivotal role in saving lives. Rampant use of AI is involved in CT scans in cases of stroke or brain injuries. Radiologists have a backlog of cases which might delay the detection of the criticality involved in a particular case. To the rescue comes AI, which by streamlining the CT scan interpretation workflow by triage process and automation of the initial screening process, radically reduces the time lapse in detection and diagnosis of time sensitive cases. To detect abnormalities demanding urgent attention such as intracranial haemorrhage, cranial fractures, midline shifts etc, has provided automated deep learning algorithms to assist physicians. The algorithms’ accuracy is equal to that of a physician and classification algorithms are used in radiology itself. TITAN X of NVIDIA, cuDNN and GeForce GTX 1080 GPUs were used that achieved almost 95% accuracy rate as compared to that of 97% by radiologists. Such AI algorithms tend to become a life saver in a world where there is an acute shortage of specialized radiologists.

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Analytics in Construction Business: Scope and uses.

Business Intelligence and Business Analytics are being used interchangeably nowadays in almost every field of businesses worldwide involving, in particular, leveraging the data of a company in order to evolve and grow. In addition to other sectors, predictive analytics greatly benefit the construction business categorizing information with relevance and accuracy. Predictive Analytics assists in the following ways:

1)      Leveraging work packages: Predictive Analytics helps in task breakdown matching the right people for the right job, scans past project documents, including the Work Breakdown Structure (WBS), and assess the fallacies in project execution. With such technical know-how, businesses can scientifically cut on resourcing costs without compromising on potential.

2)      Prescribe, Predict and Describe: Descriptive Analytics creates a database containing the failures and their severity which is followed by predictive analytics analyzing their recurrence. Finally, prescriptive analytics explores options that can prevent such fallacies in future work.

3)      Scanning risks: The construction space advocates vociferously for the health and safety of the crew and this purpose could be served by predictive analysis in conjunction with prescriptive analytics. Pinpointing disaster zones to nth degree accuracy and using pedometer analytics to measure the distance the crew covers, predictive analysis places heavy-duty equipments at various access points improving visibility in low lying areas and also alerts about the resources that demand servicing. It ensures both the project’s progress and the business’s adherence to the crew’s safety standards.

4)      Lowered production costs: Manual monitoring methods are laborious and entail a cost on the business’s profits. GE’s Kimberlite Survey reported that businesses using predictive approach using retrofit sensors and cloud computing experienced approximately 40% less unplanned downtime.

5)      Immersive Insight: Predictive analysis converts dormant data into actionable analysis and prevents any information from lying unanalyzed.

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Big Data Analytics in the Indian Economy

Data has become a central part of the economy and applications in analytics have been proliferating fast from private to the public sector. Data collection and analysis are at the root of critical economic decision-making which makes the socio-economic issues easy to interpret and comprehend, thus playing a pivotal role in economic battles. Big data analytics help the government in infusing transparency into the system, combat fraudulence and deliver public services effectively and efficiently.

The year 2017 will always be known to have triggered off this big data journey with Demonetization and GST being the two notable data-driven policies injected into the system coupled with the shift in focus to the macroeconomic issues like Aadhar data collection which gained an edge to bring economic reforms. Using big data analytics, the following few untapped areas can positively impact the government:

1)      Tax and Welfare: The ‘Project Insight’ rolled out by the Indian govt. used data mining techniques to counter tax evasion in 2017. It also helped in tracking down deregistered firms and gathered information about black money potholes in existence.

2)      National Security:  The uncertainties faced by national security officers with regards to the unpredictable security situation can be overcome by the use of analytics thus enabling them to combat crime attacks easily.

3)      Healthcare: Healthcare system in India has the opportunity to leverage big data analytics on the data emanating from biometric, patient records and thus provide actionable insights with greater prediction power contributing to effective public health.

4)      Education:  Ranking second in terms of student enrollment,  the titanic amount of student data can be analyzed to predict statistical figures and would help in efficient budget allocation.

In addition to these, analytics has also entered the farming sector where the concept of geo-tagging the entire agriculture infrastructure was implemented. Although the entire process is still in its infancy, the outcomes that big data analytics present to the Indian economy are much more effective. Though big data analytics have not been used in policymaking yet, the budget allocation hints at a widespread adoption of artificial intelligence and big data analytics in the Indian economy.

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