SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

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Cyber security tips

Cyber security is evolving, and it is not advisable not to follow the techniques that you have already used to mitigate risk. This article link gives you some suggestions to get your company well-equipped for 2020 and all the threats it could bring. They are: Implementing AI Into Your Strategy, Keep All IoT Devices Secured, Teach Employees About BEC Phishing Scams etc. Read more at: https://it.toolbox.com/guest-article/6-it-cybersecurity-tips-for-2020

 

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Predictive analytics and IoT Data

It is a fact that Internet of Things or IoT is becoming predominant in business as you can gather data to analyse at real time. But, if it is not used diligently, it will be a cost and not an asset to the company.  Traditional business intelligence shows only IoT data from the past but with predictive analysis tells what to do with that data now. This article tells us some common useful applications of predictive analytics on streaming IoT data. Read more at:

https://it.toolbox.com/articles/4-useful-applications-of-predictive-analytics-on-streaming-iot-data

 

  

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How to keep customers happy with the help of ERP system

It is a fact that ERP or resource planning platform can manage the key business activities like production and supply chain operations. But, nowadays, this ERP system when integrated with machine learning technology, also plays a vital role in keeping your customers happy. And this ERP system can have greater control over the products they eventually receive, thus cutting down on errors. Read more at:  

https://it.toolbox.com/article/how-erp-can-help-boost-your-customers-satisfaction

 

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How Artificial Intelligence is redefining industry

The Artificial Intelligence industry is growing at an exponential rate and the artificial intelligence market is projected to grow in value to $191 billion by 2024 with a CAGR of 37%. Artificial industry has the capability for machines to make decisions based on logic, data, and information from the past is impacting several industries. From self-driving vehicles to chatbots, thus transforming the way business is done on a global scale. Read more at: https://www.business2community.com/business-intelligence/artificial-intelligence-is-redefining-these-5-major-industries-02232853

 

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Trade-off between Opaque and Transparent AI

AI can be classified into Opaque and transparent Systems. Opaque AI is the black box where it is not evident why AI operates in a certain way. Though it is effective,it just means that there is higher risk associated with predictions and insights. Transparent AI is when technology does explain how it reaches its decisions using data at hand.But a company often prefers opaque AI, if the insights provided help in actually growth of the company. The need for transparency is a constraint on AI. And opaqueness might prove more effective. There is a trade-off between the two. When GDPR comes into effect,banks in The EU will be legally obliged to explain how they operate. Opaque AI will not work here ,although it might be more effective.Businesses should be able to control the kind of AI to be used in a given situation,its ethics and accuracy. Read more at: https://cognitiveworld.com/articles/choosing-between-opaque-ai-and-transparent-ai

 

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Leadership Strategies in Algorithms

As the phrase goes, “everything that can be digitized, will be digitized”, is fast replaced by “If something can be run by algorithms, it will be”. Algorithms are supposed to be performing the following tasks: • Reading resumes: With natural language processing, resumes can be read faster and with more careful eyes. • Using spreadsheets: Soon the analysis made by experts using spreadsheets would be taken over by AI. • Hiring consultants: Since the analysis will all be done by algorithms, hiring consultants is really not needed as before. Hence, for coping up with the changes, one needs to get acquainted with the programs, rent a machine learning expert to design algorithms or make it on your own and invest for the future by learning new software. Read more at:https://www.experfy.com/blog/algorithms-are-replacing-leadership-strategies

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AI application by NASA

NASA has used AI in human spaceflight, scientific analysis and autonomous systems. Multiple programs like CIMON, ExMC, ASE, Multi-temporal Anomaly Detection for SAR Earth Observations, FDL, robots and rovers are currently available at NASA. It is now working on overcoming the barriers that once blocked it from innovations in AI and Machine Learning. Although Machine Learning has been in existence for 60 years, benefits couldn’t be reaped by NASA because according to Brian Thomas, a NASA agency data scientist and program manager for Open Innovation, they are being held back. Read more at: https://www.aitrends.com/ai-world-government/how-nasa-wants-to-explore-ai/

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Big Data in Economic Prosperity

Big Data, if utilized properly, is believed to become the historic driver of progress. It plays an important role in the fields of public security, healthcare, poverty, to name a few. Video surveillance and facial recognition using big data is far more effective than reviewing the footages manually, which can be erroneous. It also helps in avoiding cybersecurity threats. Predictive models using big data can predict for future attacks even before their occurrence. With the application of big data in healthcare sector, there has been a shift from treating illnesses to proactively maintaining our health and taking certain measure for preventive care. It plays an immense role in the education sector as well. By understanding the needs of each district, it gives schools the opportunity to build innovative educational techniques. Big data solves urban transportation problem by enabling government agencies develop alternate routes to ease traffic. It helps in alleviating the dangers of food scarcity. It is time to embrace big data as it opens up opportunities to encourage economic prosperity. Read more at: https://datafloq.com/read/5-applications-big-data-in-government/65

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Visual Data in Decision Making

With every passing day, data and not instincts, are used for the expanding of business. Data is the new gold, as it helps in determining trend, offering better customer experience, responding better to market demands. However, given the data size is so big, Data Visualization is opted for, making the interpretations easier. The major reasons that data visualization is crucial are: • Data visualizations amplify a story with pictures and visuals. • Data visualizations makes difficult data comprehensible. • Data visualizations help in decision analysis. Read more at: https://www.experfy.com/blog/the-value-of-visual-data-in-decision-making

 

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Financial Analysis in Businesses

Financial analysis s beneficial for businesses in the following ways: • Cutting costs: Financial data relating to investments and cash flows are analysed. • Making investments: Financial analysis helps in predicting the returns from investments, thereby enabling the companies to go for profitable investments only. • Forecasting the future: The future of the company can also be forecasted. • Following business trends: Financial analysis relies upon the current business trends and success rates of businesses in the sector. Such analysis helps in recovering faster in case the market suddenly drops. • Management: Financial management is also tracked by the financial analysts which helps in increasing efficiency overtime. Read more at: https://bigdataanalyticsnews.com/big-data-improve-ecommerce-for-businesses-customers/

 

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Predictive Analysis

Predictive modelling software is known for training the model with the dataset with known results to predict outcomes for the new data. The two common types of predictive models are, classification model (example, predict outcome when a component fails) and regression models (predicts a number). The benefits of predictive analysis are: • Improved production efficiency: It allows for effective inventory forecasting, production rates for meeting demand, and the like. • Improved Decision making: It identifies patterns and trends for the data, enabling easy decision making. • Enhanced risk reduction: Predictive analysis, as the name suggests, enables prediction about the future. This is most helpful for a firm to save it from the upcoming risks. • Enhanced fraud detection: Being aware of the trend, a change becomes helpful in detection of fraud. • Targeted, personalized marketing campaigns: Predictive analysis helps in knowing the structure of the market and helps in closely targeting and personalizing marketing campaigns to attract customers. Read more at: https://blogs.opentext.com/predictive-analytics/

 

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Big Data’s contribution in eCommerce

Before the introduction of Big Data, only calculated guesses were made by the companies to optimize pricing and forecast demand. Big Data has contributed big time in facilitating eCommerce activities. Some of the ways are: • Predicting trends: This helps in determining the trend, and the type of customers they will face in near future, and keep the inventory accordingly. • Pricing optimization: It helps in calculating the competitors’ position and make decisions about the set of products. • Demand forecast: Studying the data, the expected time of high or low sales can be predicted. • Flexible pricing policy: Prices can be changed time to time depending upon the concerned factors. Big Data provides the data required by managers for expanding the business, taking into consideration every possible factor. Read more at: https://bigdataanalyticsnews.com/big-data-improve-ecommerce-for-businesses-customers/

 

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Machine Learning and Deep Learning

Machine Learning and Deep Learning both uses the algorithms fed into them. While in the first, the algorithm needs to be told how to make accurate prediction, in the latter, the algorithms are fed via neural networks, making the operation similar to a human brain and involving lower chances of mistakes as compared to Machine Learning. While Machine Learning gives result for a numerical and text field, Deep Learning also enables face, voice and handwriting recognition. Also, with new data fed into the system, the accuracy rates by Deep Learning are much more than by Machine Learning. Although Deep Learning is anyday better than Machine Learning, Machine Learning plays a vital role in the existing economy. Read more at https://www.analyticsindiamag.com/understanding-difference-deep-learning-machine-learning/

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Utilization of AI to refine the entertainment marketing strategies

Merging entertainment with data is a well-known concept. The marketers and content-creators have always focused on strategies that will resonate with the audiences and keep them engaged. Over the past few years, there has been an evolution of AI in the marketing strategies of content creators, brands, networks, etc. AI uses the deep learning algorithms that can digest, asses and contextualize unstructured data quickly to derive actionable insights. AI can analyze millions of pieces of content at a time, with the help of deep learning which is undoubtedly beneficial for the content creators and marketers. Deep learning helps in predicting whether a campaign will be successful even before it starts. Thus marketers are increasingly turning to deep learning algorithms to make better sense of the contents. Read more at: https://www.thedrum.com/industryinsights/2019/04/03/the-evolution-ai-entertainment-marketing

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Eliminating Gender Pay Gap Through Data Analysis

Gender pay gap in the workplace is one of the most relevant issues which can be solved by data driven decision making. On taking a closer look, it has been found that gaps in pay structure arise from unconscious biases and strategies that benefit one gender more than the other.  Research shows that there is no connection between the fairness of a raise and the effect of that raise on the gender pay gap, making it a complex issue. This can be solved with Data analysis and visualization. Rather than raising the salary of every female employee by the same percentage, building algorithms using companies’ data is more effective to eliminate the gap. This approach also brings the manifestations of unconscious bias in the pay structure to the forefront. Thus, data driven solutions can test salary decisions before making them thereby closing the gap.

Read more at: https://insidebigdata.com/2019/06/26/addressing-demographic-pay-gaps-with-data-driven-solutions/

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Chatbots: Key To Customer Service Optimization

With the advancement of data science, Chatbots are on the rise. Chatbots optimize customer service as they prod intelligent conversations between a human and an automaton. Chatbots, driven by machine learning, constantly collect new data from their interactions with customers to deliver improved experiences. It has been estimated that in 2020 over 85% of all customer service interactions will deploy Chatbots. They can act as virtual advisors and look into customer issues, can decipher typos made by interlocutor, provide speedy responses with 24/7 assistance to customers, deliver help in the banking industry, etc. Chatbots being the employees that can work without taking any rest, are useful in the insurance industry, Facebook Messenger and health service as well. Developments of Machine Learning algorithms and technology behind Big Data are the pillars of Chatbots.

Read more at: https://www.smartdatacollective.com/big-data-leads-to-impressive-array-of-chatbots-in-customer-service/

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Big Data And Gmail Security

   With the idea of improving Gmail security, Google has adopted new big data security standards. Nonetheless, users should incorporate big data to secure their Gmail login. So far, Gmail is not the most secure email servers in the market as it wants our emails and details which act as data. Big data enhances Gmail security by prioritizing advancements in cybersecurity and malware protection and making two step verification more reliable. As Gmail users, we should upgrade our browser every time we are notified about it and use a sophisticated password which should be a unique combination of characters, letters and numbers. Hence it’s a blessing for us that Big data has started looking into Gmail security concerns.

Read more at: https://www.smartdatacollective.com/4-brilliant-ways-to-use-big-data-to-boost-gmail-security/

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Big Data: Solution for Shipping Industries

Having entered the era of Data Analytics, lately it has been realized that even the shipping industry is driven by Big Data. Companies track KPIs (Key Performance Indicators) to measure its performance and spot areas that need improvement. KPIs can be applied to shipping logistics as well and this monitoring can be done easily by Big Data. Shipping Damage is a vital metric which Big Data tracks during transit as it’s important to identify and curb damages of shipments. Big Data helps monitor Shipping Time and identifies the causes of delayed deliveries. It also ensures that customers’ expectations are met by tracking Inventory Accumulation. Owing to its versatile nature, Big Data can also monitor Shipping Costs. Thus given the profound impact that Big Data has on shipping, it’s important that shipping companies incorporate data analytics to save themselves from higher costs and other damages.   

Read more at: https://www.smartdatacollective.com/data-analytics-optimizes-shipping-through-kpi-tracking/

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Predictive Analytics helping retailers in customer retention

Predictive Analytics make predictions about the future events based on current data. These days retailers use it to determine the future needs of the customers. The biggest challenge faced by retailers today is customer retention. Predictive Analytics is bringing about a huge change in the retail experience altogether. Eminent retailers like Amazon is using predictive analytics to make customer recommendations based on purchasing history. Furthermore, development in Artificial Intelligence and machine learning is boosting its use. These developments in turn are helping the retailers gain a competitive edge. However tech giants like Google, Microsoft tend to keep the cutting-edge innovations for them. The new and innovative predictive analytics must level the playing field for small medium industries. Read more at: https://channels.theinnovationenterprise.com/articles/retail-reaping-rewards-predictive-analysis

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Big Data: Key to Creating Powerful Instagram Stories

With benefits like tracking the response of Instagram users to different Instagram stories, delivering better content, deeper understanding of the ROI of various time slots, etc. Instagram marketers can use Big Data to attract more followers and drive more sales. The feature of Instagram stories help increase these numbers backed by big data. Appealing content for Instagram stories can be created by first determining the goal of the story. Next, a great concept story needs to be spun to convey the benefits of the brand’s products to Instagram users. After building the concept, the story outline needs to be created which should then be sketched visually. Application of these steps and proper incorporation of Big Data should keep Instagram marketers from buying followers without causing any hindrance in the achievement of their goals.

Read more at: https://www.smartdatacollective.com/big-data-for-instagram-using-data-to-perfect-instagram-storyboard/

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