With more than 120 professionals joining every minute, LinkedIn is a continuously growing venue for business professionals and content. Beyond its personal use cases such as professional networking or applying for jobs, LinkedIn is also a rich and tailored data source for many business use cases. LinkedIn profiles are very structured and rich in content, enabling you to find individuals with certain backgrounds, posting about a certain content or looking for a certain job at a large scale. In this article, we will introduce the top 3 business use cases that LinkedIn data can transform. In 2019, US marketers ranked email as the tool with the highest ROI for B2B lead generation.

Whether you're faced with countless job applications or simply looking to improve the quality of your workforce with the best candidates, you will require some form of automation to ease your workload. In this article, I will share what automation there exists and how it helps you. Gone were and should be the days of traditional recruiting where one has to manually source and evaluate candidate profiles high up in the recruitment funnel. This is not only resource-intensive in effort but in time. Leveraging on the power of web-scraped data, recruiters can automatically source for candidate profiles from the web and evaluate them in an automated fashion based on the relevance of the applicants' skill sets, years of experience, etc and save tons of time and manpower.

Artificial Intelligence Specialist - Artificial Intelligence and Machine Learning have both become synonymous with innovation, and LinkedIn data shows that's more than just buzz. Hiring growth for this role has grown 74% annually in the past 4 years and encompasses a few different titles within the space that all have a very specific set of skills despite being spread across industries, including artificial intelligence and machine learning engineer. According to Indeed, Machine Learning Engineer job openings grew 344% between 2015 to 2018 and have an average base salary of $146,085 according their Best Jobs In The U.S. Study. Robotics Engineer – LinkedIn is seeing a 40% annual increase in demand for technical professionals with robotics expertise today. Robotics is an industry on fire according to LinkedIn's study.

By 1433, the Chinese admiral Zheng He had already sailed from China to India, Indonesia, and even Africa on caravels twice as large as those Christopher Columbus used 59 years later for his fateful journey. China could have been the country to discover America. Instead, its government surprisingly decided to put an end to its naval activities and burn its entire fleet of ships, indirectly allowing Spain to conquer America and bring prosperity to Europe. It took more than 5 centuries for China to recover from this political decision. What could make such an advanced country deliberately turn away from its future?

Few concepts in professional life stir more ideas and emotions than Artificial Intelligence (AI). There are passionate differences of opinion about what it is and what it's capable of, whether it will simply destroy jobs or create them as well, whether it can improve our human capabilities or make them redundant. Nowhere will you find a broader and more representative range of these opinions than in the AI conversation on LinkedIn. Analysing it reveals important clues about our understanding of AI. We can see the thought-leaders and themes dominating the conversation, the motives that business leaders and technology companies have, and the likely consequences for different industries and sectors.

A man dressed in white from head to toe was posted outside the Las Vegas Convention Center last week. His outfit included just a few colorful exceptions: four bubbles in red, yellow, blue, and green splashed in the center of his T-shirt, and a fluffy red poof atop his ski cap. Those multicolored spots should look familiar to anyone who's used Google's virtual assistant on their smartphone. After all, those dots form the same logo that appears in the Google Assistant app. That's because he, like others at CES in the same attire, was dressed as the Google Assistant. The costumed characters were part of Google's effort to give its digital helper a flashy presence at CES after Amazon's Alexa stole the spotlight at last year's show.

Machine learning is in; Flash is out. Data scientists are in great demand, specialized developers, not so much. These are just a few of the trends LinkedIn picked up in its 2017 Emerging Jobs Report. It's no surprise that jobs in tech are growing faster than any other industry. The fastest growing job over the last five years is machine learning engineer, as the number of open positions on LinkedIn has multiplied by nearly 10X.

LinkedIn data represents the world's largest online professional network, with relationships among more than 467M members, 290M jobs and 9M organizations through professional entities and attributes. This data provides the foundation of consumer products for our members and monetization products for premium members. Data value is usually measured by revenue and user engagement with the products, both of which depend on the accuracy and comprehensiveness of the data. For example, the successfulness of LinkedIn Sales Navigator is determined by how accurately it finds the right decision makers in a company for salespeople to contact, and how many such candidates are discovered. Knowledge derived from LinkedIn data needs to be represented without ambiguity in a machine-legible way.

Last week, completed its acquisition of for $26.2 billion. After winning a purported bidding war with, Microsoft again battled the Customer Relationship Management (CRM) leader in front of EU regulators. Salesforce argued that the acquisition would make Microsoft too powerful in the CRM market, an argument the regulators rejected. The stakes were much higher than that. In fact, the CRM card was a red herring that Microsoft played masterfully.

Hiring industry has changed very much with the advancement in technology. Today, most Recruiters go through the candidates social profiles(LinkedIn, Facebook, Twitter, Github) along with the Resumes to figure out if the candidate is a good fit for the company. All this sounds like one huge task which is generally spread over days depending on the number of candidates who have applied to the company. One third of new hires quit their job after about six (6) months. It is forecasted that the number of unemployed people will reach more than 212m by 2019. Thus these recruiters just shortlist the candidates based on some hard filters like GPA, Years of experience etc. to shorten the list.