Creating Intelligent Solutions in Data Science: 3 Trends to Watch

New technology is forcing the data science industry to rapidly evolve. This year, data science is going to be all about increasing efficiency and finding innovative ways to turn data into insights. Some of the biggest trends you will see this year are the digitization of the financial-services industry, data empowerment, and the growth of Generative Adversarial Networks. What is common is that they create intelligent solutions to some of the biggest issues facing digital science.

Financial Services Goes Digital

The first major trend that you will see is the move to digitize the financial services industry. Bitcoin was one of the most talked about topics last year and continues into 2018. Bitcoin’s recent popularity has sparked interest in using the technology for additional applications. The financial industry has often been slow to evolve with the changes in technology, particularly as it relates to security and regulatory issues. But if the popularity of cryptocurrency continues its upward trajectory, banks and other financial institutions won’t have any other choice but to adapt to the changing ecosystem. One of the first things that digitization will affect is the customer experience, but you will likely see banks adapt digital automation this year as well.

Putting Data in the Hands of the Right People

Data empowerment is another important movement taking place in Big Data right now. Data empowerment is all about collaboration and access. The goal is to give the same access to tools, resources, and data stores to all of the parties connected to a system. Often, raw data accumulates but remains untapped until it is organized in a way to understand the implications. If more people have access to the data, you increase the chance that someone can actually make use of the data. Another aspect of data empowerment is how it can be a resource to employers. Analytics, for example, can reduce the need for repetitive administrative tasks, which, in turn, allows employees to focus on more challenging and creative tasks.

Generative Adversarial Networks

General Adversarial networks (GANs) are a type of artificial intelligence (AI) model composed of two neural networks in competition. They’re used in machine learning and have numerous real-life applications, including text-to-image synthesis and image and video generation. You will start to see more and more companies switch to this architecture because GANs have the ability to generate essential data samples and then distinguish between real and fake data. Moreover, when fake data tricks the system, the system learns from the error. This allows GANs to train with much less data input than other AI models. The biggest problem with GANs is that some of the networks cease learning when the system gains too much strength. Developers have already started to address this issue. Once this snag is resolved, you will likely see GAN architecture become even more predominant.

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