Business intelligence (BI) tools, used to prepare, present and analyze data are now transforming the small businesses. The chief role of BI is to convert tons of raw data into useful information that the business executives can use. Small businesses will grow if and only if it can break down and analyze the information available on its server. BI tools help businesses make smarter decisions and also in Customer Relationship Management (CRM). Businesses must always find ways to understand their customers better. This is where BI solutions like YellowFin and Looker play a massive role.BI tools also help in boosting data governance and compliance. Thus BI helps small business owners avoid mistakes by keeping data accurate and up to date.Read more at: https://www.business2community.com/business-intelligence/how-business-intelligence-tools-are-transforming-small-businesses-02208030

The most powerful algorithms extensively used in the field of deep learning are neural networks. Here we focus on the artificial neural networks which are nothing but the computing system based on the structure of human brain. It is a type of machine learning which is used for detecting patterns in unstructured data. A lot of business applications today depend on neural networks to solve various complicated problems. However companies use neural networks in various ways. For instance, as explained by LinkedIn’s Deepak Agarwal, neural networks are used in LinkedIn along with text classifiers to detect spams. Neural networks are widely used in activities like marketing, finance, operations management, insurance etc. Thus the capabilities of artificial neural networks are inexhaustible. Read more at: https://www.cmswire.com/digital-experience/what-is-a-neural-network-and-how-are-businesses-using-it/

Implementing Big Data in Manufacturing innovation, the integral part of our economic success, allows for great industry gains. With respect to the objective of the manufacturers of improving their systems, big data reduces inefficiency by probing meticulously into the supply chain. Big data solves the trade-off between managing reduced cost supply and customization. It provides real-time insights to customer data which improves order-to-fulfilment times and delivers demand predictions looking at historical data sales trends. Predicaments like breakdown of manufacturing assets can be avoided using big data’s power of predictive maintenance. Big data replaces guesswork with connected supply line thereby improving strategic decision making. Before extracting the advantages of big data, it is essential that accurate data is gathered and stored, cleaned and analysed, mined and monitored besides ensuring that it is actionable.

Read more at: https://www.smartdatacollective.com/heres-how-to-implement-manufacturing-analytics-today/

Are you a frustrated blogger? Has your website traffic come to a standstill? Have your dreams of earning a six figured income been shattered? Worry not! Machine learning can come to your rescue with its releases of keyword research tools that help bloggers find profitable longtail keywords. Longtail keywords are a set of less competitive or less targeted keywords which have higher conversion rates and can maximize the visits to a website. ML driven keyword research tools provide keywords that are at least related to, even if they don’t include the exact phrase that bloggers use to run their reports. Drawbacks like omitting relevant keywords and including inapt ones exist. Nonetheless, implementation of ML is any day more effective for a successful blogging career.

Read more at: https://www.smartdatacollective.com/machine-learning-helps-bloggers-secure-more-traffic-with-long-tail-keywords/

Bad data generally refers to the faulty or flawed data. All the marketing strategies and tactics become futile if the business falls prey to bad data. Studies have found that bad data are expensive and can cost upto $3.1 trillion per year. One of the important contributors of bad data is the reliance on traditional lead generation tactics by the companies. Third party intent data is a savior to the bad data problem. It helps companies overcome the inaccuracies that might have existed in their Customer Relationship Management (CRM) for years. Companies must ensure that the data provider is a trusted one. To overcome bad data problem, the intent data must be accurate, actionable and targeted. Read more at: https://www.business2community.com/marketing/solving-the-bad-data-problem-in-marketing-02210818

Contrary to the common belief that Big Data and green business have little intersection, the former provides an array of solutions to sustainability issues faced by businesses. Benefits of big data can alleviate threats of climate change in future. According to Annie Qureshi, the author of the original blog, following the IPCC report on climate change in October 2018, businesses are expected to shift their investments to green tech companies from fossil fuel resources. Prospective investors can be attracted if local investors focus on big data and predict to yield high return for investors or by launching social media campaigns. As more and more companies are meeting UN sustainability goals, big data is making the world greener. Hence investing in big data should help green companies flourish.

Read more at: https://www.smartdatacollective.com/how-to-use-data-analytics-to-launch-sustainable-technology-business/

Big Data is facilitating the marketing profession by giving marketers the advantage of detailed analytics capabilities. Visual social media networks like Pinterest and Instagram collect enormous data on their users, optimizing which, marketers can reach potential customers. This is where big data’s role comes in. In order for tracking the performance of different posts to derive patterns, online tools use machine learning to help marketers develop more engrossing pictures. Big data helps in keyword analysis as well where marketers look at the monthly search volume of different keywords to reach more customers. Shrewd marketers use big data get more views and hence expand their audience. Marketers using social networks need to keep up with new trends which be forecasted and prepared for using predictive analytics.

Read more at: https://www.smartdatacollective.com/data-optimization-facilitates-pinterest-and-instagram-marketing/

In order for fast procession of data, companies today increasingly demand for stream processing. Big data is an enormous collection of structured and unstructured data which are analysed by companies for making smart decisions, reducing cost and time, developing new products and optimizing offerings. Being a means of reviewing real time data while it’s still in motion, Stream Processing, which finds its application in financial institutions, is beneficial as it accelerates data delivery and enhances real time analytics, deepens data cognizance of companies by working together with machine learning, etc. With big data stream processing, emerging companies can quickly transform time into innovation, respond to issues faster besides realizing the game changing ability of real time data. In this era of continuous evolution of technology, emerging markets and businesses should equip streaming applications to resolve business problems. 

Read more at: https://www.smartdatacollective.com/big-data-sets-standards-in-stream-processing/

Overtime the demand for wearables, like Fitbit, are increasing. The owners of the product get the details about themselves. However, at a large scale this data is beneficial for various sectors like the insurance companies, the pharmaceutical companies, doctors, etc. But this aggregate data is available only with the brand owner. Considering the insurance company, it can come into an agreement with its insurers, wherein the insurers would use the Fitbit and report the data to the company, which in return would give special discounts on premium amount. This aggregate data, or the Big Data, can then be used for the cost-benefit analysis. Faced with the fear of being misreported, the insurance companies might also go for vertical integration with the Fitbit companies, ensuring a greater market for the same in return of the Big Data. This would ensure a substantial portion of the market to the firm. The insurance company on the other hand would be successful in maximizing its profits. Hence, vertical integration and Big Data can do wonders in these markets. Read more at http://bigdatatobigprofits.com/2015/12/04/lessons-on-big-data-risk-and-the-vertical-integration-of-wearables-and-startups-2/

Gmail has become a huge platform with almost 1.2 billion monthly users and holds a 20% share of the global email market. However, Gmail is not really secure as they require personal information such as name, interests, who is one talking to, and many more things. Hence, for trusting Gmail, several tips should be implemented for ensuring maximum security. • Big Data plays a big role in cybersecurity and malware protection. Machine Learning tools are capable of understanding newest thus it is advisable to invest in email specific antivirus software. • Enabling two-step verification, again made reliable by the Big Data. Along with this is the easy-to-use update which adds to the security. • Upgrading the browser whenever possible reduces changes of cybercrimes. • Using a sophisticated password. Read more at: https://www.smartdatacollective.com/4-brilliant-ways-to-use-big-data-to-boost-gmail-security/

The main motive of bloggers is getting traffic to their websites. They therefore focus on competitive high-volume keywords. But, unfortunately, it is not these, but longtail keywords that will serve the purpose. There are two reasons behind preferring such keywords are: • Keywords not targeted by other bloggers can be used. This uniqueness will automatically increase traffic. • Many a times people are specific with certain keywords. Acknowledging and using them will also be a smart move. After longtail keywords have been used, the machine learning helps further in increasing the traffic. With machine learning, keyword research tools are getting more efficient at finding better contextual relationship between the terms. This therefore enables the bloggers to have access to a much more exhaustive list of longtail keywords to choose from. Although limitations still exist, improvements are faster. Read more https://www.smartdatacollective.com/machine-learning-helps-bloggers-secure-more-traffic-with-long-tail-keywords/

A rare hereditary disease is difficult to diagnose. Sufferers have to go through innumerable tests,that take up valuable time of doctors and patients alike with still no definitive result. This additional time delays therapy that could actually be directed to forestall damage. Professors from the University Hospital Bonn and a team of researchers had data of 679 patients suffering from 105 different diseases .They trained a neural network model with 30,000 portrait pictures of affected people and demonstrated how artificial intelligence can be used to perform efficient and reliable facial analysis in cases where the facial structure does show mutations when afflicted with a rare disease. Together with other symptoms and genetic data,it was possible to get accurate diagnosis.

Read more at: https://www.sciencedaily.com/releases/2019/06/190606133805.htm

In 2014,NASA had lost an instrument located on the Solar Dynamics Observatory that measured UV rays coming from the sun,to forecast solar storms and to alleviate their affects. This is when scientists and engineers turned to artificial intelligence,with the thought that well-trained data can fill the data void. Four years of data captured by space instruments that included images of the sun were used to design processes.Using the best software tools to test these models,the scientists concluded that CNN is a good fit for the data giving 97.5% accuracy. Superior images of the sun generated were used to predict UV measurements.Our question stands at- if AI can be used to fill data gaps,can it forecast UV spectra as well and can it address a wider spectrum of problems?

Read more at: https://www.aitrends.com/neural-networks/how-ai-came-to-the-rescue-of-scientists-studying-the-sun/

Nowadays emotional intelligence is changing the way we view sales. It is an effective way of understanding customers’ emotions and analyzing their buying behavior. An emotionally intelligent sales person must possess qualities like managing client relationships, self-awareness and self-management. Convincing the customers to buy products is an important task. An emotionally intelligent sales person must be able to resolve any issue that might arise. Being aware of how one’s action affect others can help develop a sense of self awareness. A self aware salesperson should know the right time to talk and should be an active listener. To be able to control one’s emotion is an important trait that a salesperson must possess. Emotional intelligence training therefore gives an all new perspective to sales. Read more at: https://www.business2community.com/sales-management/why-emotional-intelligence-training-is-vital-for-sales-02208645

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