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/

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/

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/

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.

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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

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/