SigmaWay Blog

Big Data gained popularity during mid-1990s. But what exactly ‘’Big Data’’ means? It generally refers to the voluminous data, which the conventional data processing system is unable to process. It is widely used because of its advanced fraud detection and prevention techniques. The severity of frauds in credit card industry, insurance fraud and email based frauds are not new. Using big data to understand the card usage pattern of every customer, building specific fraud detection models, analyzing the geographical location of a person, can help companies prevent fraud. Pattern analysis with the help of big data helps banking sector detect fraud beforehand. Big Data helps prevent Medicare fraud  within minutes! Thus Big Data helps companies perform tasks on a much broader scale than a human being can actually do. Read more at: https://channels.theinnovationenterprise.com/articles/how-big-data-is-being-used-to-improve-fraud-detection

In the modern era, with almost all industrial sector adopting new technologies, healthcare sector is no exception. Artificial Intelligence has changed the way in which we seek medical care.The use of artificial intelligence in medicine has reduced manual tasks and increased efficiency and patient care. With robots already assisting in various surgeries, few other AI systems used in medical sectors are WOEBOT,DA VINCI, PARO etc. As of now, there is always a high skilled surgeon in total control of the robots. It is believed that we have to still go a long way before robots have the sensitivity to perform surgeries on their own. They lack the common sense and the experience that humans possess. Thus a balance between human and machine is required. Read more at: https://community.nasscom.in/communities/iot-ai/artificial-intelligence-in-the-healthcare-sector.html

In the era of extracting insights from data, Sentiment Analysis is used to compute opinions, sentiments, views, etc. expressed in text format. Text polarity which recognizes sentiment inclination of text as positive or negative, Ranking (numbers in a range) the sentiment of the texts and Aspect based sentiment analysis which identify sentiments towards specific aspects in text are three broad divisions of problems in sentiment analysis. Supervised learning based sentiment analysis first trains and then tests the data. Unsupervised learning based sentiment analysis require sentiment dictionaries which can be created by lexicons or by a corpus based approach. Application of linguistic and statistical methods, training domain adapted models, aiming for aspect based sentiment analysis, identification of biometrics, images and sound as sources of sentiment data, etc. are some useful pieces of information shared in the blog. Contextual understanding, sentiment ambiguity and texts involving sarcasm and comparatives pose serious threats to performance of sentiment analysis system. 

Read more at: https://medium.com/seek-blog/your-guide-to-sentiment-analysis-344d43d225a7

A large number of tables with thousands of rows and columns. Data keeps flowing in from multiple live sources and is rapidly changing . This is what characterizes Big Data. Datasets are becoming vast and hence more complex for analysis. Without the correct tools,it would be difficult to use this data to draw insights. In this regard,three trends have come to the forefront- IoT,querying techniques and cloud computing.The advent of IoT has digitised everything. There are sensors and wires that chanel data which is then manipulated and analyzed to get results.Querying data i.e. extracting information from data is the first step before analysis and Cloud has come to play a major role in data storage. The three trends connected have paved a path for growth in technology.

Read more at: https://www.sisense.com/blog/waking-up-the-world-of-big-data/

AI is redefining the way oncologists look at cancer treatments.Deep learning has proven useful in understanding the trajectory of tumors in the distinctive case of cancers . According to two doctors,AI is making its mark in the healthcare industry.CNN(convoluted neural networks) have been trained to recognize and categorize cancers.CNN is used to diagnose tumor volume and segment tumor with higher precision. They have an advantage over semi-automatic methods,because the former can independently identify common features. Studying and interpretation of sample slides are slow and unreliable. DNN can be used to quicken the process and give robust results.A deep learning model teaches itself and becomes more accurate as it studies clinical data so it can predefine the form that a tumor will take.AI in healthcare has revolutionized diagnostic methods that aid better treatment.

Read more at: https://www.aiin.healthcare/topics/research/5-ways-ai-being-used-advance-cancer-research

In the words of Favio Vazquez, “Data Science is not just knowing some programming languages,math,statistics and having domain knowledge.’’Data science enables a person to evaluate the data and predict the present and future of the world. The solution to the optimization problems must add value to the business or organization. Mastering the craft is what it takes to be a successful data scientist. Having a good knowledge of statistics or math is never enough; practicing hard so that one can apply the tools to solve real business problem is the key. Interpretation is an important part of data science. Through modeling, we can decode the complex data and transform it into something useful and simple. Finally questioning and analyzing our understanding make us better learners.  Read more at: https://medium.com/swlh/the-3-biggest-mistakes-on-learning-data-science-f782e1a8abec

Apart from breaking the internet and keeping people entertained, memes play an important role in social media marketing. Correct memes make businesses socially relevant. However before using memes one must become familiar with it and know the proper use. Misusing a meme can have an exact opposite impact on the business. Identifying the target audience is another important factor. It is futile to use those memes that customers cannot relate to.  Memes should be used sparingly- they should help enhance the content of the company on social media.Creating original memes instead of sharing the popular ones can help promote the brand more effectively. Hence memes can be a great idea for social media marketing if used in the right way. Making audiences laugh and keeping them satisfied can help build customers’ trust on the brand and hence the company.Read more at: https://www.business2community.com/social-media/how-to-use-memes-in-your-social-media-marketing-02208299

With Google describing it as “Intelligence exhibited by machines’’, Artificial Intelligence (AI) is the ability of a machine to think and learn smartly like humans. With its extensive use in business, industries, healthcare, it has become an indispensable part of our lives, making it fun and easy. With the world pacing rapidly, can we think of day in our lives without a Google assistant or a Siri or an Amazon Alexa? AI has the potential of bringing about changes and development that is beyond human imagination. However it remains highly doubtful if AI can solve issues like poverty and epidemics. AI can be a potential job destroyer boosting unemployment and inequality. Having said that, we should remember that nothing can beat human understanding and problem solving skills. Afterall we live in a world where relationship is the priority. By keeping us updated and gaining the necessary knowledge we can definitely survive in a world of transformational change! Read more at: https://www.business2community.com/strategy/artificial-intelligence-disruption-or-opportunity-02175867

    Considering how stubborn established companies are with their business operations, introducing Machine Learning which will bring a difference in business environments and in how people work, is extremely difficult. Data Science, AI and Machine Learning are different concepts where Machine Learning helps the other two prosper. It is a means of programming an algorithm wherein businesses feed in examples of real data. According to Maxim Scherbak, the author of this article, the new task for ML-driven organizations is preparing data for Machine Learning models. This implies that it will make Business Analysts different rather than making them redundant. For successful implementation of Machine Learning algorithms (which are mostly viewed as black boxes) they need to be backed by thorough testing. Leaving rule based approaches behind, businesses need to learn to trust these algorithms to shine in the era of AI. 

Read more at: https://towardsdatascience.com/when-business-analytics-meets-machine-learning-10ecaada9d8

A/B testing (also known as Split testing or bucket testing) is mostly used by websites and mobile apps as an effective data based method to test and compare alternatives wherein one variant’s significantly better performance proves its efficacy and suggests its implementation. To run A/B test, after deciding the objective, the variants of the landing page are to be determined, where the variations should differ only in the element that is to be tested. The test, then needs to be performed to compare how the users respond to the variations followed by reviewing the results and making changes based on the results. Some of the benefits of A/B testing to mobile developers are higher conversion rates, analysis of engagement metrics, decision making based on data and statistics, ability to view app store experience from user’s point of view, better use of resources, insights into customer behavior, etc. Ultimately A/B testing makes businesses improve if they run multiple experiments and understand their users’ behavior.  

Read More at: https://splitmetrics.com/resources/what-is-ab-testing-and-why-it-matters-for-mobile-developers/

Last fifty years have seen great advancements in the medical field but along with it came hand in hand the errors in interpretation of medical scan reports, with 2% false positive rates and 25% false negative rates. AI, however, has come forth for rescue. While there have been arguments that deep learning would replace radiologists soon now, it is nothing more than a myth. With deep learning, the doctors would be able to detect the problems quickly and operate accordingly. One example is Zebra Medical Vision which has helped doctors for five years now in making the right decisions by developing deep learning algorithms for interpretation of medical images and working with data. It will help automate every visual aspect of medicine, help doctors by giving them a second opinion and also provide assistance and augmentation in case of lack of any skill. With such feedbacks, the probability of correct diagnosis increases many folds. The augmentation of Artificial Intelligence in the medical fields will be revolutionary. Read more at: https://www.forbes.com/sites/gilpress/2019/04/22/ai-startup-zebra-medical-vision-enlists-deep-learning-to-save-lives/#68aa9baa1035

With the advancement of technology, the advancement in the hacking sector has also taken place. All types of data are being hacked and misused. To solve this problem, biometrics have come into the play. Smart phones already have finger print and face and voice recognition facilities. These features will now come in the financial sector which would disable hacking or duplication (as can be done with ids and passwords) and hence keeps the data safe. The recent big problem of hacking of the ATMs can be curbed using the biometric technology. Beyond physical characteristics, behavioral biometrics can also track how a consumer swipes a mobile screen or uses a keyboard and recognize fraudulent activities. However, the institutions are not that flexible in terms of adopting the new technology due to coordination problems. The customers, on the other hand, are keen to use this new technology which keeps them safe. Read more at: https://finance.toolbox.com/article/biometrics-play-growing-role-in-security-and-convenience-for-financial-services

These days people find ways to use coupons were ever is possible especially on online delivery through food apps, buying any products through eCommerce apps etc. Its one of the best strategy used by any companies these days. By using coupons the consumer have a mental satisfaction that they receive cheap products and this lead to the loyalty towards the company. As today's time is a technology time so to avail coupons is very easy especially through your mobile phones, anyone any where can use it. Companies find various ways to attract them with their new offers, advertising through shows, banner ads on bill boards etc which leads to increase of sale.

Read more at : https://www.business2community.com/marketing/6-ways-to-improve-your-coupon-marketing-strategy-and-increase-sales-02192134

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 https://www.techworm.net/2018/06/how-machine-learning-algorithms-help-businesses-target-their-ads.html

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.

Read more at https://www.informationweek.com/strategic-cio/digital-transformation-turning-data-into-dollars/a/d-id/1332319

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: https://www.bigdatanews.datasciencecentral.com/profiles/blogs/five-ways-your-business-is-at-risk-of-data-loss

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.

Read more about it at: https://www.sisense.com/blog/data-management-rules-analytics/

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: https://www.technotification.com/2018/08/neural-networks-deep-learning.html

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: https://www.cio.com/article/3274447/digital-transformation/why-digital-transformations-are-lagging.html

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
3. data.gov

Know more about them at : https://www.technotification.com/2018/04/building-data-science-models.html