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/

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/

   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/

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/

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/

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/

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/

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

    Considering how stubborn established companies are with their business operations, introducing Machine Learning which will bring a difference in business environments and in how people work, is extremely difficult. Data Science, AI and Machine Learning are different concepts where Machine Learning helps the other two prosper. It is a means of programming an algorithm wherein businesses feed in examples of real data. According to Maxim Scherbak, the author of this article, the new task for ML-driven organizations is preparing data for Machine Learning models. This implies that it will make Business Analysts different rather than making them redundant. For successful implementation of Machine Learning algorithms (which are mostly viewed as black boxes) they need to be backed by thorough testing. Leaving rule based approaches behind, businesses need to learn to trust these algorithms to shine in the era of AI. 

Read more at: https://towardsdatascience.com/when-business-analytics-meets-machine-learning-10ecaada9d8

A/B testing (also known as Split testing or bucket testing) is mostly used by websites and mobile apps as an effective data based method to test and compare alternatives wherein one variant’s significantly better performance proves its efficacy and suggests its implementation. To run A/B test, after deciding the objective, the variants of the landing page are to be determined, where the variations should differ only in the element that is to be tested. The test, then needs to be performed to compare how the users respond to the variations followed by reviewing the results and making changes based on the results. Some of the benefits of A/B testing to mobile developers are higher conversion rates, analysis of engagement metrics, decision making based on data and statistics, ability to view app store experience from user’s point of view, better use of resources, insights into customer behavior, etc. Ultimately A/B testing makes businesses improve if they run multiple experiments and understand their users’ behavior.  

Read More at: https://splitmetrics.com/resources/what-is-ab-testing-and-why-it-matters-for-mobile-developers/