Of late, Agile methodologies have been taking root in data science boosting complex collaborations between data scientists and other developers. Agile can be easily ported over to Machine Learning and Artificial Intelligence domains due to its feedback-heavy, iterative nature and given that incessant improvement is an innate part of AI. Such methodologies are characterized by fast feedback loops and short development sprints. Agile projects, in distinction to old-school waterfall approaches, involve error correction and cyclical stakeholder input and primarily focuses on short term goals rather than the long-term view. AI researchers should think of research as an iterative, evolving process to remain receptive and adaptive as per Agile’s basic tenets. To ensure that projects do not grind to a halt, maintaining a buffer of solutions for implementation is a priority as data scientists work on multiple projects, each taking months to complete. The iterative nature of Agile well captures experimentation as a core part of AI and ML projects. Agile maximizes value throughout the development process.

Read More at: http://www.dataversity.net/case-agile-machine-learning/

Allowing various components of technology to come together seamlessly, Artificial Intelligence (AI) has been taking over the technology arena for some time. The pressing budget of UK’s National Health Services (NHS) has led to clinics being short-staffed and overworked. In the healthcare industry, AI uses algorithms to be tantamount to human cognition to analyze composite medical data. Institutions such as Massachusetts General Hospital, The Mayo Clinic, NHS have been applying AI programs to processes such as drug development, personalized medicine, treatment protocol development and patient monitoring. Use of AI may introduce certain type of risks such as algorithm bias, DNR implications and machine morality issues, though, research on the use of AI aims to validate its efficacy in improving patient results prior to its broader adoption. AI enables evaluation of current health of patients, access to more up-to-date research through NLP and identifies warning signs earlier in the care process thereby reducing risks of medical issues having long-term implications. Requiring a vast amount of data is indispensable for AI solutions to provide powerful insights. However, the obstacles faced in the use of AI must be overcome before a true change can take place.

Read More at: https://www.smartdatacollective.com/artificial-intelligence-healthcare-changing-industry/

Organic traffic is used for visitors landing on the website as a result of organic search results. Recently, companies have been witnessing a drop in organic traffic. Prioritizing quantitative analysis over qualitative business assessment would lead a company on the road to success. Companies can manage to hit their KPIs by understanding current view generation trends with the help of close data-driven analysis of site traffic. A continually changing SEO rules have contributed to declining organic site traffic for many businesses transforming an advantage into a source of punishment for manipulative guest posting. Visibility and ranking are conflicting priorities with data being the determining factor of increasing or decreasing organic site activity. Social media data and visibility are among the other major factors contributing to website traffic. Despite searches not directing to the social pages, the best ranked sites are attributed with more social interactions. Target sites having the greatest degree of visibility are the matter of concern for ranking algorithms. Using big data in general and unique views coupled with first visits in particular, gives an organic traffic boost to businesses. Referral marketing boosts reputation and is effective at generating new businesses

Read more at: https://www.smartdatacollective.com/wheres-the-traffic-increasing-organic-leads-with-big-data/

Data awareness is a sensitive topic within the crypto community and fears regarding crypto will be disseminated by new efforts that educate people about data privacy and security.

The Information Commissioner’s Office (ICO) has started conducting a number of data awareness campaigns with the aim of building trust in cryptocurrencies. Data security breaches and privacy violations are the two most concerning issues underlying the faith in crypto market. Data privacy laws and EU’s Global Data Protection Requirement (GDPR) work to encourage people to the usage of cryptos and re-establish consumer confidence thus restoring faith in digitalization. Under the new data guidelines in the era of GDPR, ICOs are working to ensure compliance among the users of cryptocurrencies. Smaller businesses, despite having smaller budget, less brand equity and subject to draconian fines,  too garner trust and meet compliance targets. ICO Data Awareness efforts inevitably benefits the crypto industry.

Read More at: https://www.smartdatacollective.com/ico-data-awareness-campaigns-create-more-trust-cryptocurrency/

Insurance companies, over the decades, have been overwhelmingly dependent on credit scores for judging customers’ credit worthiness. Analysis of credit scores are in practice long before big data acquired a firm foot in the consumer analytics industry. However, they have often been criticized of being biased against the most credit-worthy individual. Not neglecting the obvious imperfection of these credit score based actuarial algorithm models, to make nuanced decisions regarding the credit risks of their customers, insurers have resorted to using big data. There may be certain variables incorporated in credit scoring algorithms that overstate customer dependability. A person with a good credit score, even if he faces a couple of repayment defaults due to sudden financial breakdown, would have his current credit score unaltered. Several other reasons have made insurers skeptical of using credit rating in the era of big data. With the help of big data Insurers now recognize that credit based insurance policies have increased the risk of unjust racial profiling. Limitations and fallacies of credit-scoring are being continually exposed by analytics modeling compelling insurance actuaries to upend existing policies and have greater reliance on data-intensive approach.

Read More at: https://smartdatacollective.com/big-data-causing-insurance-actuaries-move-away-using-credit-scores/

The sprouting digital age has created both opportunities and challenges when it comes to businesses with customers. On one hand, the outburst of new channels has opened up amazing new ways to involve the audiences. On the other hand, it challenges the business enterprises to cut the noise out and stand apart to build a more sophisticated, intuitive and personalized relationship with their customers. In this regard they have gone one step further to personalize information such as name, title, organization, purchase history etc. and utilizes interactive and real-time data to create highly appropriate communication that is relevant to the user. This is what is called the hyper-personalization. Such an action of creating messages that target and connect with a specific subset of the overall consumer audience leads to companies’ willfully abandoning broad reaching marketing messages and creating different campaigns for different groups of people. This matter revolves around the question “what people want” and it’s predicted that the recent decade will see e-commerce companies connecting their brand through hyper personalization. Some of the renowned brands like Starbucks, Amazon and Spotify have adopted hyper-personalization, where AI and machine learning analyze numerous factors to power their recommendation engine. 

Read more at: https://monk.webengage.com/hyper-personalization-marketing-future/

Smart Robotics is becoming an evolving field in the area of artificial intelligence. Modern robots have become far more intelligent and adaptable in a continually evolving environment than their predecessors which is attributable to machine perception plays an indispensable role in their development when used in conjunction with more sophisticated machine learning steps.

Data scientists and AI engineers must overcome certain challenges to improve the future of robotics. According to Rewired, there are ways to improve machine perception to fortify smart robots. Treating machine perception algorithms as a passive system coupled with poorly thought on assumptions were the grave mistakes made by programmers. As a remedial procedure, learning as a proactive, multi-sensory process in being borne in mind.  Dependence on antiquated sensory systems have been replaced by new sensory systems to process inputs. According to Russell graves of CoSMoS Laboratory, improved positioning and quality of data forms the building blocks of modern machine perception. 

Read More at: https://www.smartdatacollective.com/vital-role-machine-perception-modern-smart-robotics/

Although data science, artificial intelligence, or Web 2.0. are the new buzzword, Jupyter Notebooks are revolutionizing in the sense that it is an open-source web project, a piece of software that allows data scientists to create and share documents that contain live code, equations, visualizations and explanatory text. Originally called IPython Notebook, it’s built to write and share code and text, within the domain of a web page. Even though Jupyter has its roots in Python (it evolved from IPython Notebooks), it is now multi-lingual. In fact the name itself comes from 3 languages: Ju(lia)+Py(thon)+(e)R and  Jupyter kernels now extends to 80 more languages. They are very convenient in the senses that have become a standard for data analysis and scientific research, allows us to publish our narratives in many formats, from live online notebooks to slide decks; and it simplifies the problem of distributing employed software to coworkers and associates.

Read more at: https://www.oreilly.com/ideas/what-is-jupyter

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

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