Local vs. Onshore vs. Nearshore vs. Offshore: Which Outsourcing Employee is Right for You?

Once upon a time, if you needed someone to answer phones, respond to mail or schedule appointments, you’d put an ad in the local newspaper or contact an employment agency. Now those options are about as appealing as only using your phone to make calls or foregoing the option to telecommute.

Times have changed drastically and so have the ways we add employees to our businesses.

However, while we may not be as limited as we once were for hiring additional help, it can still be overwhelming to think about outsourcing vital tasks. You may be wondering where to look to find a reputable call center that will treat your customers with respect.

Or maybe you need a small team to guide customers through more difficult inquiries. And what about budget constraints? Is there a way that you can get the help you need without it costing an arm and a leg?

Local vs. Onshore vs. Nearshore vs. Offshore

These are all common questions, and many companies are finding the answers through outsourcing employees from around the world.

How to Become a Mobile App Developer

If there's one evergreen job title for software developers that just keeps getting greener, it's mobile app developer. This IT role usually refers to somebody who can design, build and/or maintain mobile applications for either Apple's iOS or the Android platform. There are other mobile OS choices – including Mobile Windows and BlackBerry – but they already became dubious and irrelevant.

Essential mobile app developer background training and skills

To be a mobile developer, one must possess software development skills and knowledge. You could develop these skills by earning an associate's or bachelor's degree in computer science or a similar discipline (management information systems, for example). 

You can further your efforts to master mobile app development by attending one or more of the many coding boot-camp programs springing up to train aspiring developers.

How to Hire Experienced DevOps Consultants

Hiring experienced DevOps consultants is difficult as the demand for this concept has outnumbered the supply. In this article, Ankit Gupta explains the prerequisites for hiring the best DevOps consultant for your team.

There was a time when myopic approaches encroached every nook and cranny of the software development life cycle. Certainly, the development practices delivered capricious results making the clients more apprehensive about the fruition of any application. This inspired IT expertsPatrick Debois and Andrew Clay Shafer to embark on a quest to develop the reactionary response called DevOps in 2009.

DevOps emerged as a counter theory to defy conventional development practices and deliver high performing applications. It spearheaded several pragmatic and utilitarian concepts to deliver scalable infrastructures. The DevOps practice has emerged as a rising cultural movement that has been quickly embraced by the technical community. Increasing number of companies are looking for experienced DevOps consultants. But to set up a team of high-end DevOps talent requires the know-how.

Knowing the practice before headhunting

DevOps has radicalized the normal methods of software product development of separated roles. Where the production logs when developers develop the codes and QA team keeps rejecting them back. By removing these silos DevOps identified a new way of working. These newly evolved practices provided competitive advantages to the organizations.

How AI And Predictive Analytics Drive Marketing Success

When it comes to marketing, historical data should always be a driver for strategy and planning. Predictive analytics is the next level of using that data for marketing success.

Predictive analytics is the use of data, statistical algorithms and AI techniques to identify possible future outcomes. This can help you stay ahead of the curve and assess the future of your marketing. Here are a few ways that you can use AI and predictive analytics in your marketing.

Optimizing marketing campaigns

With predictive analytics, it works just like the Scientific Method by having a hypothesis and then proving it either right or wrong. You can use the data to determine what customer segments and audiences will be the most effective to reach and create actionable insights. With accurate reporting, you can accurately tell whether a campaign was successful and optimize where it may fall short. This lays the groundwork for best practices of strategies to follow, not just in marketing, but sales and business decisions as well.

Machine Learning: Emerging use cases of NLP

AI Solutions Development: Emerging use cases of NLP

First thing to know: This isn’t futuristic stuff. Natural language processing is happening right under your nose. Sports broadcasters do it all the time, using machines to generate narrative based on scores and statistics. Many universities routinely turn to NLP to screen for plagiarism in student work.

The healthcare industry is eager to leverage this emerging capability, with NLP playing a prominent role in the “10 Promising AI Applications Development in Health Care” recently identified by Accenture. For example, virtual nurse assistants could save $20 billion annually by taking on some of the first line responsibilities for interviewing and assessing patients.

Furthermore, a 2016 poll by analytics firm HealthMine found that while 60 percent of patients could access their electronic medical data, 15 percent had trouble understanding it and just 22 percent used it to make medical decisions. Some see NLP as a means to bridge the language gap between doctors and patients.

Researchers from Yale University, the University of Massachusetts and Bedford VA Medical Center have addressed this point. In a recent study they applied an NLP algorithm to clinical documents, tracking medical terms to their lay-language equivalents and making it easier for patients to understand their doctors’ instructions.

Text Mining in Banking – A Brief Overview of Capabilities

Text Mining in Banking – A Brief Overview of Capabilities

Banks are starting to explore AI and machine learning solutions development by deploying natural language processing (NLP) to make use of enterprise and customer data in text mining applications ranging from gauging customer sentiments to enterprise search.

Historically, banks have collected large volumes of data about customers and their own internal operations. A significant portion of these documents is textual information recorded mostly for compliance reasons.

This data might be siloed for different business functions inside a bank, and effective information search and discovery is challenging in its traditional form. AI systems could help navigate through the vast data stores to gain valuable business insights.

The banking industry was one of the early adopters of text mining technology with traditional systems like the Automatic Processing of Money Transfer Messages (ATRANS).

This was possible because the information that these early systems had to “read” and “understand” was relatively simple and highly structured. With NLP, the scope of what information can be made searchable has grown to include both semi-structured and unstructured free form text.