SigmaWay Blog

Predictive Analytics, Artificial Intelligence, bots, data science – the waves of advances in data science keep on coming. Access to old data and not skill base or technology, turns out to be the biggest obstacle for powerful analysis insight which requires a tedious data preparation. Data Silos are something of a buzzword, a demon lurking in the enterprise which makes it prohibitively costly to extract data and makes company initiatives nearly impossible. Silos lead on to limited information, redundant data and interdepartmental inefficiencies. To make the data streamlined, accessible and impactful to the organization’s bottom-line, the development of silos must be mitigated in a progressive and pragmatic approach. Things aren’t as beautifully simple as the buzzword “data lake” might conjure. A combination of various methods including use of the right software, encouraging proactive communication, blurring departmental descriptions and roles coupled with the goal of integration at the background would lead to an integrated platform thus overcoming the problem of data silos. Focus on Wide Data Analytics and not only big data, stands indispensible to achieve a future state of mature analytical competency, however, silos aren’t entirely evil in the context of data management.

Read More at: https://smartdatacollective.com/how-to-eliminate-silos-in-company-wide-data-analytics/

Deep Learning Networks (DNNs) are some of the most powerful deep learning algorithms constructed from multiple layers of linear and non-linear processing units. Neural Networks interpret sensory data through machine perception, labeling and clustering raw input.

The advent of synthetic data will overturn the competitive advantages of machine learning that powerful tech companies get by amassing visual data sets of images and videos. Synthetic data is computer generated data that burlesque real data; in other words data that is created by a computer. Many initial startups face the “cold start” problem where lack of quality labels make it difficult to train computer algorithms. To resolve this problem, data simulators, which are highly flexible and versatile, are being used to generate contextually relevant data in order to train algorithms. However since, big companies exponentially expand their initiatives to gather relevant data, they do not face the same challenge.

Data simulation will bring parity between big technology companies and startups. The major challenge facing the startups is to leverage the best visual data with correct labels to train computers accurately so that they can compete against functionaries with inherent data advantage.

Read More at: https://tcrn.ch/2KfIraQ

This is an era where all of us are tech savvy and are connected to each other through social platforms linked through smart devices. The new generation expects real time response to their queries with personalized feelings and suggestions from the diverse brands they trust and engage with. Business enterprises are now resorting to chatbots or virtual agents that can manage consumers’ queries without any delay. A Chatbot or the “Chatter Robot” is a platform that imitates human conversations including text or spoken language by using artificial intelligence techniques such as Natural Language Processing (NLP), image, audio and video processing. It’s ability to network using a simple interface and engage with customers in a more natural and friendly manner has given itself a cutting edge across dimensions. Although chatbots and apps are intermingled with each other, they are different in the sense that the interactions with the bot take place sequentially (as a conversation), and the bot is used inside a chat app. Moreover chatbots have an identity of its own which is separate from its interaction with the user. 

Read more at : https://www.happiestminds.com/Insights/chatbots/

With the advent of smart phones, mobile BI generated attention which enabled distribution of critical business metrices, KPIs and data to remote workers. retailers, sales, marketers and small business owners keep a beat on the pulse of their business responsibilities using BI applications on mobiles. 

 In days of Symbian devices, accessing data on mobiles was cumbersome. Nowadays, mobile BI applications are accomplished either by accessing the application on mobile browser or a innate application designed for a specific mobile OS. Mobile BI is rapidly transmuting spaces in the software industry. Independent researches divulge high expectation for the growth of mobile BI. Mobile BI is one part of the BI puzzle. Given that BI is about making gainful decisions analyzing the right data, mobile BI enables access to the data by all including the remote employees. Mobile access to BI data enables a ‘game-like’ experience thus allowing businesses to remain nimble and intelligent.

Read More at: https://business2community.com/business-intelligence/is-your-bi-tool-designed-for-mobile-how-to-tell-why-it-matters-01266138

In the recent times, the outburst of embedded and connected smart devices and systems has created an opportunity to connect every ‘thing’ to the internet.In this regard the Internet of Things(IoT) is one of the most hyped- set of technologies and a system of interconnected devices, both mechanical and digital machines, electronics, software, sensors, objects, animals or humans that are given a unique identifier and the ability to transfer data over a network without requiring ‘human-to-human’ or ‘human-to-computer’ interaction.An IoT system consists of sensors/devices which “talk” to the cloud through some kind of connectivity and based on the processing of software it then automatically adjusts without the need for the user. The extensive set of applications for IoT devices include consumer applications, smart home, enterprise applications, infrastructure applications etc. If we look at the recent trend, we can clearly observe a significant trend in the explosive growth of devices connected and controlled by the internet thus automating maximum tasks in business enterprises and promising new jobs, where people can understand and leverage the IoT to create solutions that will be greatly in demand. However IoT has been criticized on several grounds such as insecure implementation, a significant and variable lag into the control loop introducing instability and an exposure of vulnerabilities that might be exploited by hackers.

Read more at: https://medium.com/iotforall/iot-explained-how-does-an-iot-system-actually-work-e90e2c435fe7

Installment loans make it easy to deal with the financial urgencies. People with fixed monthly income should go for the installment loans. For the students, it eases the pressure of the payment of tuition fee, by giving flexible options for installment. Also for the long-term desires, one needs a large amount for which installment loans is a secured option with hassle-free funding. The online platform is making the whole scenario more convenient in order to attain the loans directly from the lenders.

Read more at: http://www.articlesfactory.com/articles/finance/how-installment-loans-provide-relief-in-bad-credit-situation.html

Companies need money to run and have treasury for the same, taking care of incoming and outgoing money. Corporate treasury works, comprising liability and an asset division.  Liability division raises working as well as long-term funds and asset division fulfill the purpose of investment of any surplus fund. The liability group gets the money from different sources and then it is deployed for the asset division. Short term funds are raised when there’s need for liquidity. The surplus of liquidity get invested in asset division and hence, the company ensures no opportunity loss. Two broader divisions are there- Back office and mid office. Keeping a check on interest rates is also an important task of the Treasury, in order to not have uncompetitive products in the market as per the price parity. The corporate treasury, therefore, plays a pivotal role in the functioning of the organization.

Read more at: https://www.mbaskool.com/business-articles/finance/17619-corporate-treasury-managing-business-funds.html

FinTech or financial technology entered Indian banking and finance sector a few years ago, but it's 2016 when it became noticeable. It not only enhanced the finance and banking of the country but also generated employment efficiently in the areas such as payments, retail baking, peer-to-peer debt financing, personal finance, asset management, institutional investments, remittances, and financial research and hence, the demand for data scientists is growing at a greater pace among FinTech companies along with Big Data, analytics. Artificial Intelligence and Machine Learning are among the key technologies driving the FinTech industry. Therefore, the FinTech industry gives much more opportunities for young students and professionals in the analytics field. It would surpass the traditional BFSI industry in terms of growth, as well as the size and with an adequate supply of skilled labor and the overall impact of FinTech on economic growth will be huge. Read more at: https://www.entrepreneur.com/article/314042

According to a recent study, the average small business only has 27 days of cash reserves on hand which implies that there is a possibility of serious cash flow issues when a client is tardy with their payment. Dealing with unpaid invoices can be tough and to handle all aspects of the unpaid invoices, we need to take care of few things. First, there is a need to avoid working with clients who don’t pay on time. There must be a clear understanding of the terms and conditions so that no client can take benefit of the doubt. Considering mobile invoicing make the things more convenient. There is a need to handle the issues in a professional and systematic way. If there are cash flow problems due to outstanding invoices, the option of invoice financing can be chosen. Approval for invoice finance is a quick process and hence can be useful at the time of crisis. Unpaid invoices result in a lot of time wasted chasing clients for payment. However, there are ways to get back the money from the clients, keeping the business financially sound. 

Read more at: https://www.business2community.com/finance/5-ways-deal-unpaid-invoices-2018-02022443

IoT and artificial intelligence are two of the buzzwords recently. According to IDC, spending of IoT is expected to exceed $ 1.4 trillion by 2021. And more the IoT means more the data and as the amount of data increases it becomes difficult to manage and analyze the data. Here, AI acts as a reliever. AI can help find patterns and analyze those huge stacks of data. So, overall, AI and IoT are considered common friends that have enough potential to build a bright smart future of upcoming technological world.

Read more at : https://www.informationweek.com/big-data/ai-machine-learning/ai-and-iot-techs-new-bffs/a/d-id/1332108

Great businesses are powered by great people. The term human capital signifies that the value of an employee can be measured and enhanced through investments in training, benefits, compensation and so on. And, of course, as the value of the workers has been growing in an organization, the software to manage HR functions and human capital has also evolved as well. Human resource information systems (HRIS) or human capital management (HCM), they arguably provide similar functions that are either part of, or integrated with, ERP software and go well beyond handling hiring, firings, payroll and benefits.

Read more at: https://www.cio.com/article/3269807/software/best-hcm-software-comparing-7-human-capital-management-platforms.html

Enterprise level organizations should try to incorporate blockchain into their system. At the same time they should not just get blockchain just for the trend, but it should be done with a strong sight towards companies’ scope. Blockchain can benefit these enterprises by simplifying the third party transactions and overseas value transfers. Blockchain can also provide incredible advancement to analytics systems which will inflate the data-input quality and insight outputs. 

Earlier the commodity trading advisor is supposed to trade commodities and futures for a managed futures fund. But now selection of investment products is more complex and varied which calls for the need of acute understanding of CTA, of these products. Role of today’s CTA is related to derivative analysis also and hence not only limited role to trading. Analysis is now, the catalyst for the inclusion of value added service to retain customers which includes structured products, risk management and OTC derivatives.

Read more at: http://www.articlesfactory.com/articles/finance/the-role-of-a-cta-commodity-trading-advisor.html

The companies that manage the human capital in the most effective and efficient manner are considered to be the most successful companies. Gary S. Becker, a Noble Prize-winning economist, coined the term “human capital”, saying that “the basic resource of a company is the people”.  With time, corporations are recognizing the importance of human capital more than ever. Companies are now beginning to understand that to stay on top of the global economy; they need to put more and more emphasis on developing and retaining their people. Cloud Computing infrastructure is the new salient trend in the IT industry which helps not only to expand business globally also provides a sophisticated and successful strategy to make business more reliable and flexible. Organizations need cloud-based HR system in upcoming days.

Read more at: https://www.hr-jump.com/blog/2017/10/31/why-is-human-capital-management-important-in-organizations

With many companies still stuck to take advantage of data, analytics has to be number one question because this is a key stage to implement AI successfully. There is a sequence of evolution in analytics, starting from descriptive to prescriptive. 

Nowadays, organizations tend to skip traditional analytics and shift into AI. Many enterprises use the descriptive analytics, applying BI techniques: combine all your data to get a quick review on what’s going on in the company. 

Without the insight that analytics brings, it will be hard to assess the outcome of any artificial intelligence system. Analytics keeps AI transparent, responsible and may help increase the productivity of AI systems. Unproductive information control leads to the unnecessary operational costs. AI analytics helps to find out cost savings and prepares a report with the help of main ROI metrics to keep up productive decision making.

From food to clothing, to real estate, and to everything; now AI and machine learning is everywhere. They can recognize foods, value of real estates, things in your images and many more. They are really good at whatever it does. There have been varieties in application of AI and it will be interesting to integrate all those stuffs into one general intelligence.

CES 2018 showed the present scenario of the robot industry. The best of the companies presented the best of their futuristic gadgets, mostly smart home integration. The current progress is ready steady and mesmerizing but we are still about a decade behind having a real human like butler, or in better words a real human like robot friend sitting next to us. 

Read more at: https://www.popsci.com/personal-robot-butlers-ces-2018

Classification of data is very important in many organizations. They can be used to make decisions. But the task of classification can be very tedious. Now imagine a machine doing this job. Classification using machine learning is with the help of supervised learning approach and algorithms. Machine learns from the data input given to it and with the help of this learning, it classifies new observation.

For example, we want to check number of male and female members in an organization. Here we can train our machine to do this classification. 

Classification using machine learning is one of the trending technologies being used in various fields. It has many applications in many domains other than IT.

Various algorithms can be used to implement classification. There are two types of learners in classification – 
Lazy Learners - which simply store the training data and wait until a testing data appears. They classify the data based on most related data.
Eager Learners – that construct a classification model based on given training data.

Different classification algorithms are – Decision Tree, Naive Bayes, Artificial Neural Networks, K-nearest neighbor.

Read more about them and various evolution methods at https://towardsdatascience.com/machine-learning-classifiers-a5cc4e1b0623

Artificial Intelligence, Machine Learning and Deep Learning are relatively newer technologies invading the fields of information technology, business etc. Though developers are walking towards this era, currently the number of experts is relatively less. The company often makes mistakes by starting up with the technologies instead of focusing on business needs. They often make mistakes by assigning out of domain work to some. For e.g. Hiring data scientists and asking them to build something interested from given database. Rather than a team must be formed of product managers, data engineers, data scientist and DevOps engineers.A team of four will be a kick start to improve our process and giving better results. Now everybody has an opportunity to improve the models, optimise the deployment and scale the business. 

Talking about ML, many projects fail due to complex structures. This could occur because of working on wrong problem, to having wrong data, failing to build a model or failing to deploy it correctly. Read more at: https://medium.com/@guyernest/the-flywheel-of-machine-learning-systems-50aa6d992382

Internet is a vast place where one could get and post information globally. Many search engines help you to find what you want using different search algorithms. Ever since the first search algorithm was discovered, many new searching algorithms are being invented and used to make searching process easier. However, there are times when text-based searching becomes really exhausting. Take an example of flower. You are very fascinated by a flower you saw in wilds and is very curious to know about it. You start searching about it using it properties like colour of petals, number of petals, description of leaves etc. This would be very tedious and still there is no surety whether you will get results or not. 

Now imagine for searching with the help of picture. You just click a picture and rest will be done for you. This is known as Visual Searching and to achieve that Machine Learning is used. This type of searching can be extensively used in various domains. Initially a large amount of dataset will be required to train your machine. However, by using the concept of neural networks, this could be achieved and used. Read more at: 

Read more about it at https://medium.com/gsi-technology/ml-in-visual-search-part-i-d54cf4f2b509