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

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

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

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

Digitalization has become a global movement and initiatives are being set to accelerate the rate of adoption. 2030 has been declared as the target and many initiatives are already on their road. One such automation process that can that can change everything significantly is the business automation process. From improving customer experience to cutting down cost, business automation has the power to help the firm grow. 

However, automation efforts are only beneficial if the CIOs make right investments and decisions. It is often seen that with a wish to automate, many leaders often make wrong decisions which could cost them potentially higher than excepted.

The three business goals that investments in automation will help are:

1. Digitalize and optimize operation
2. Create and act upon advanced insights
3. Drive business technology innovation.

Read about them at https://www.techrepublic.com/article/how-to-automate-your-business-3-critical-steps/ 

Data Science is a new field of interest and used in every sector. Whether it is a business, production line or a tech company, each of them wants someone to analyse their data. This would further help them to make decisions. Even though there is so much need of data scientist, still the number of data scientist is low. There are many characteristics that could define a good data scientist. 

Few of them starting with C are: Curious, Careful, Clever, Confident, Creative, Capable, Communicative, Considerate, Candid and Collaborative. 

To know further about these words visit: https://medium.com/@tableaucoach/characteristics-of-a-data-scientist-ten-cs-4e3b185cc7cd

With existing systems and technology it is often difficult to manage massive amount of data by various industries and organizations. Keeping in mind several aspects such as cost reduction, customer experience improvement and increase in conversion rates, companies often need to derive meaningful conclusions from the data collected and establish a connection between them. This linked data is also referred to as journeys. Journeys improvise on well-grounded business decisions by pointing out the improvements needed after looking at the customer transactions as a whole. Journey Science facilitates anyone, to be a Journey Scientist, regardless of their skillset and enable them to easily generate an intelligent dataset that includes the commonly understood business context in an enterprise. Leading telecom businesses are implementing the Journey Science concept through a multi-channel, end-2-end view and linking together structured and unstructured data back to their strategic objectives, and quickly modifying them to ensure they cope up with the evolving customer demands efficiently.

Read more at: https://www.datasciencecentral.com/profiles/blogs/journey-science-in-telecom-take-customer-experience-to-the-next

In today’s business world, companies generate a lot of data. Now, we now have things like social media and cloud based business services and they all generate tones of data, which is the biggest challenge for BI. Business Intelligence (BI) refers to the tools, technologies, applications and practices used to collect, integrate, analyze, and present an organization’s raw data in order to create insightful and actionable business information. The purpose of Business Intelligence in a business is to tackle with the questions as to what are its main benefits. Read more at: https://financesonline.com/purpose-business-intelligence-business/

In an organization it may seem that the processes are ordered and it runs smoothly, but the affects are not always in line with the expectations. Companies expect and get results from process improvement, Business process improvement (BPI), also known as functional process improvement is a strategic planning methodology which aims at identifying the operations or employee skills which would eventually lead to smoother procedures and more efficient workflow for the overall growth of the business. Its main purpose is to meet customer demands and business goals more effectively. Read more at: https://financesonline.com/purpose-business-intelligence-business/

The world of artificial intelligence is so much exciting. So basically Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. In the year 1956, John McCarthy coined the term “Artificial Intelligence” and defines it as "the science and engineering of making intelligent machines." Precisely AI is a machine with the ability to solve problems that is usually done by us humans with our natural intelligence. AI is a broad branch of computer science. The goal of AI is to create systems that can perform intelligently and independently. Read more at: https://www.sciencedaily.com/terms/artificial_intelligence.htm

The online commerce industry has been growing at an unprecedented rate which is attributable to big data. Alongside exploring the benefits of deep learning and predictive analytics, small businesses, working with smaller budgets, should consider using big data to reap the most bangs for their buck in terms of highest profitability. Over the past few years, e-commerce companies have invested titanic amount in big data and have earned record profits. For example, according to Statista, Amazon in 2017 netted a profit of nearly $178 billion as compared to $136 billion in 2016. Walmart, too, experienced nearly a 44% increase in revenue attributable to more resource allocation to e-commerce. Small businesses primed for growth are also capitalizing on the surge in online payments and big data e-commerce to get their slice of the profits as well. A research by eMarketer, estimated e-commerce sales to rise upto $2.8 trillion by this year. Surely, e-commerce will continue to smoothen its way to dominance with consumers on board. Small businesses can extract certain benefits like building brand awareness, improving customer confidence and greater net impulsive buys. The propagation of big data ecommerce options is one of the biggest which has paved the way for more valuable payment systems.

Read More at:  https://www.smartdatacollective.com/small-businesses-use-big-data-ecommerce-solutions-success/

With the development of data-related technology in recent years, objective data analysis has not been able to overcome the hurdle of managing human relationships. It is indispensable to focus on qualitative human characteristics to opine about an employee in addition to performance numbers. In recent times there has been a sheer rise in demand for this kind of an analysis, even from the business perspective which would assist managers to coordinate and understand employees better. The major challenge facing data is to tackle an issue that has historically been impossible to be reduced to numbers. With a large volume of data, collected across multiple dimensions, even a rudimentary algorithm can point out parameters that could predict human behaviour. At the intersection of multiple data related disciplines including data mining, statistics, machine learning etc, a sophisticated system could be produced to make sweeping predictions about a group’s future behaviour. In a nutshell, predicting human behaviour needs complete understanding of the complex personality traits and emotional states. Moreover we aren’t naturally acclimatised to study subjective factors  that is easy for predictive analytics algorithms to parse. The transition from quantitative to qualitative data analysis calls for a more intensive method of data collection. By analysing the human behaviour, analytics businesses can become better managers, negotiators etc.

Read more at: https://www.smartdatacollective.com/how-big-data-could-soon-help-manage-human-relationships/

Technological innovation occurring all across the countries has made it easier to crowd source it to provide with better and faster services, funding and navigation. Powered by social media, new tech and web 2.0, Crowd sourcing is a sourcing model by which it engages a ‘crowd’ or group for a common goal — often innovation, problem solving, or efficiency. Being a blend of two words- crowd and outsourcing, there is no doubt that crowd-sourcing is helping various organizations to tap new ideas and solutions in order to encourage deeper consumer engagements, co-creation opportunities and reduced costs. It cuts across various dimensions and enhances all social and business interactions. Not only it is used by private organizations the way they work, hire, research and market but also by Governments to empower people and to enable them to voice their opinions in crucial matters of the country. Crowdsourcing also has the potential to revolutionize the system by touching across various fields such as science and technology and education. 

Read more at https://www.entrepreneur.com/article/307438