Once the businesses have captured a huge amount of data<\/strong>, the problem starts with how to sort through and analyze it. After all, sitting on a pile of data is of no use if you can\u2019t leverage its power.<\/p>\n As machine learning<\/strong> <\/em><\/a>algorithms together with other forms of AI improve and proliferate, it has become possible for businesses to use data<\/strong> analytics and mine deeper down that huge amount of data<\/strong> to derive actionable insights. And this is where the expertise of a master in data science<\/strong><\/em><\/a> exactly fits in.<\/p>\n The key challenge with all the generated data<\/strong> isn\u2019t how we gather it, or which tools we use to analyze it, or how we structure it, but what we can achieve with it. Here\u2019re eleven key advantages that a master in data science<\/strong> can help us attain by working with the data<\/strong>.<\/p>\n With<\/em> the help of a master in data science,<\/strong> we can leverage consumer data<\/strong> to create personalized recommendations and shoppers can be informed of special promotions and offers that are most relevant to them. We can completely maximize up-sell and cross-sell opportunities to add to the bottom-line.<\/p>\n Brick-and-mortar stores can also use this data<\/strong> to improve their consumers\u2019 in-store experience. For instance, by using consumer location data<\/strong> and heat maps, we can understand and enhance traffic flows better and recognize where there is an opportunity to adjust merchandizing displays or optimize a store\u2019s layout. In addition, customer data can help us identify preference differences based on region, thus enabling us to feature the right products across key locations in a store.<\/span><\/p>\n The<\/em> particular types of products that every consumer is likely to want and the price they\u2019re willing to pay can be identified by using customer data analytics.<\/strong><\/em><\/a> By leveraging the data<\/strong> captured from online reviews and customer feedbacks, products or services can be improved. Customer data<\/strong> can also be used to identify users based on relevance and then asking them for feedbacks on different features of a product.<\/p>\n There<\/em> isn\u2019t any business that can survive without having a robust customer base. However, even with a solid customer base, businesses sometimes cannot afford to overlook the high competition they experience. If a business fails to learn what consumers are actually looking for, it\u2019s quite justifiable to start offering inappropriate products that would eventually lead to the loss of a clientele.<\/p>\n With the use of big data<\/strong>, we can observe different consumer related trends and patterns in an effort to promote loyalty.<\/span> In today\u2019s technology-driven age, it has become quite easy to capture these trends. All we need to have is a master in data science,<\/strong> who can utilize data<\/strong> analytics strategy to maximize the potential of the data at our disposal. With an appropriate customer data analytics strategy and mechanism in place, we\u2019ll have the ability to obtain critical behavioral insights to act accordingly to win and retain customer base.<\/p>\n The<\/em> highly risky business environment and unprecedented times need better risk management processes to be in place for businesses to sustain. For any business, a risk management plan is a crucial investment regardless of its volume or industry. Being able to foresee a risk and mitigating it before it happens is of utmost importance if a business wants to remain profitable.<\/p>\n Big data analytics<\/strong> <\/em><\/a>has greatly contributed toward the development of perfect risk management solutions. Considering the increasing diversity and availability of statistics, we can use big data<\/strong> analytics to enhance the quality of different risk management models. However, to attain this, businesses need to implement a structured process to accommodate the huge scope of big data<\/strong>. A master in data science<\/strong> can help to ensure that areas of potential risks or weaknesses are identified.<\/p>\n With<\/em> the help of big data<\/strong>, supplier networks can be offered greater insights, accuracy, and clarity. Through the implementation of big data analytics<\/strong>, contextual intelligence across supply chains can be achieved. <\/span><\/p>\n Earlier, supply chain management systems weren\u2019t in a position to leverage big data<\/strong> analytics, and thus, suppliers sometimes incurred great losses, apart from being prone to making errors. Today\u2019s supply chain systems are based on big data<\/strong>, and thus, are capable of handling more complex supplier networks.<\/p>\n Though<\/em> log analysis and management tools have been around long before the emergence of big data<\/strong>, with the exceptional growth of business transactions and activities, it sometimes becomes a massive headache to be processed, stored, and presented in the most cost-effective and efficient manner.<\/p>\n With the help of big data<\/strong> log analytics applications, it has become possible to capture, process, and analyze a huge amount of log data<\/strong> without having to dump it into relational databases and then retrieving it. The synergy between big data<\/strong> analytics and log search capabilities has enabled us to discover valuable insights for more agile operations.<\/p>\n Businesses<\/em> that deal with a huge amount of financial transactions keep on searching for more effective, innovative approaches for fraud detection and insurance agencies are no exception. When working with traditional fraud detection models, the investigation process sometimes makes huge delays, making the business suffer huge losses.<\/p>\n With the help of the expertise of a master in data science<\/strong>, billions of insurance related information can be processed today, thus enabling the investigators to analyze individual records easily.<\/span> Machine learning<\/strong><\/em><\/a> capabilities and predictive analysis allow a big data<\/strong> platform for fraud detection to raise automatic red flags as soon as it identifies a pattern similar to a previously known fraud pattern.<\/p>\n Healthcare<\/em> is an industry that generates a huge amount of data<\/strong> on a regular basis. Using the expertise of a master in data science<\/strong><\/em><\/a>, costs of treatments can be reduced by not performing unnecessary diagnosis. <\/span><\/p>\n It also helps us avoid preventable diseases by detecting them during the early stages, and preventing them from getting any worse, which in turn makes the treatment effective and easy. For example, wearable sensors and devices can provide us with real-time data of our electronic health records.<\/p>\n Customer<\/em> data<\/strong> can even be sometimes used as a means of safeguarding more secure information. For example, financial institutions can use voice recognition data<\/strong> in order to protect users from fraudulent activities or authorize them to access their financial information. These systems work by combining data<\/strong> from the interaction of a customer with a call center with machine learning algorithms that can identify and flag potentially malicious attempts to access that customer\u2019s account.<\/p>\n1- Create a more enjoyable and personalized shopping experience<\/em><\/strong><\/h3>\n
![\"1.Create](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/1.Create-a-more-enjoyable-and-personalized-shopping-experience-data-science-300x226.jpg\")
<\/p>\n2- Improve products or services based on customer feedback<\/em><\/strong><\/h3>\n
![\"2.Improve](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/2.Improve-products-or-services-based-on-customer-feedback-data-science-300x190.jpg\")
<\/p>\n3- Customer acquisition and retention<\/em><\/strong><\/h3>\n
![\"3.Customer](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/3.Customer-acquisition-and-retention-data-science-300x131.jpg\")
<\/p>\n4- Risk management<\/em><\/strong><\/h3>\n
![\"4.Risk](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/4.Risk-management-data-science-300x174.jpg\")
<\/p>\n5- Improved supply chain management<\/em><\/strong><\/h3>\n
![\"5.Improved](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/5.Improved-supply-chain-management-data-science-300x134.jpg\")
<\/p>\n6- Improved management of log data<\/em><\/strong><\/h3>\n
![\"6.Improved](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/6.Improved-management-of-log-data-science-300x68.jpg\")
<\/p>\n7- Improved insurance fraud detection<\/em><\/strong><\/h3>\n
![\"7.Improved](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/7.Improved-insurance-fraud-detection-data-science-300x177.jpg\")
<\/p>\n8- Improved healthcare industry<\/em><\/strong><\/h3>\n
![\"8.Improved](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/8.Improved-healthcare-industry-data-science-1-300x228.jpg\")
<\/p>\n9- Improved data security<\/em><\/strong><\/h3>\n
![\"9.Improved](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/9.Improved-data-security-data-science-300x213.jpg\")
<\/p>\n10- Greater marketing insights<\/em><\/strong><\/h3>\n
![\"10.Greater](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/10.Greater-marketing-insights-data-science-300x168.png\")
<\/p>\n
![\"11.Improved](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/11.Improved-transportation-data-science-300x200.jpg\")
<\/p>\n![\"\"](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/final-300x225.jpg\")
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