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Interviewing a Prospective IT Staff Augmentation Partner

If your business is ready to engage an IT Staff Augmentation Partner, you have already decided you need and want help. Whether your enterprise wants support for a one-time software application development project, ongoing support for enterprise systems, or training expertise in a particular technological environment, you want to be sure you get the right resources, the right knowledge, and the right support! In this article, we outline three areas to discuss when hiring a prospective IT staff augmentation partner. 

Delivery Models

Every business need is different and even your business needs may change from month to month. What delivery models does this partner offer? Ideally, you want the most flexibility to respond to changes. You plan for one thing and then discover that you need more resources or a different type of resource. How does the partner respond to that kind of change? You may want onsite resources that are embedded with your team, or offshore resources working from the partner offices to deliver services remotely, or you may want a hybrid delivery model with some resources onsite and some offsite. Can your partner accommodate those needs and do they have experience in delivering services in this way?

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Big Data Trends to Consider in 2021

Intro

Big data is growing so fast it's almost hard to imagine. According to some studies there are 40 times more bytes in the world than there are stars in the observable universe. There is simply an unimaginable amount of data being produced by billions of people every single day. The global market size predictions prove it beyond any doubt.

It’s not a question of if you will use big data in your daily business routine, it’s when you’re going to start using it (if somehow you haven’t yet). Big data is here and it’s here to stay for the foreseeable future.

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Augmented Analytics With PySpark and Sentiment Analysis

In this tutorial, you will learn how to enrich COVID19 tweets data with a positive sentiment score.You will leverage PySpark and Cognitive Services and learn about Augmented Analytics.

What Is Augmented Analytics?

According to Gartner's report, augmented analytics is the use of technologies such as machine learning and AI to assist with data preparation, insight generation. Its main goal is to help more people to get value out of data and generate insights in an easy, conversational manner. For our example, we extract the positive sentiment score out of a tweet to help in understanding the overall sentiment towards COVID-19.

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Augmented Analytics: The Future of Data and Analytics

With the rising need and importance for data, many next generation technologies and data processing tools are coming into the spotlight. Today, becoming data-driven is a key priority for many advanced organizations. In order to sustain a good position in the industry, organizations need to adopt an advanced data processing tool such as augmented analytics.

Augmented analytics uses Artificial Intelligence (AI) and machine learning to augment human efforts to evaluate data. It beats the traditional analysis tools by automating data insights and providing clearer information. According to Forbes, 89% of industry leaders believe that Big Data will transform business operations in the same way the Internet did. Also, enterprises that don’t implement a business intelligence (BI) strategy to gather, evaluate, and apply that information in a meaningful way will be left in the dust. Here’s where an advanced data analytical tool like augmented analytics comes into the picture. According to a report by Allied Analytics, due to the growing adoption of next-generation technologies, such as augmented analytics, the global augmented analytics market size is expected to reach $29 million by 2025.

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Big Data Trends to Look Out for in 2019

Data is what moves forward digital innovation in innumerable and diverse areas, and it is small wonder that advancements and developments in big data are among the most influential in business and beyond. Organizations that are the first to find solutions to the most important data challenges gain significant advantages over their competition. In this article, we will take a look at some of the most prominent trends in big data that are worth watching this year.

1. Data Management Remains Challenging

The principle behind big data analytics has always been and remains rather straightforward: you collect a lot of data, find meaningful patterns in it, train machine learning algorithms to notice them, and create models that automatically detect such patterns. However, the practical implementation of this approach remains problematic. You have to use data coming from a number of silos, clean it up, and label it for the purposes of machine learning and implement such a system so that it works securely and stably. There are still no easy solutions to all these problems, which means that experienced data engineers are still going to be among the most high-demand IT specialists.