Three Emerging Data Science Trends You Need to Know

Data science has become a necessity for companies looking for innovative and efficient ways of solving complex business problems. As the world becomes more data-centric, trends in data science technologies and practices continue to emerge and evolve. Continual education on cutting edge data science applications can help optimize an organization’s operations, internal processes, and bottom line.

Here are three emerging data science trends that you and your company should be exploring to maximize insights and profitability.

Natural Language Processing

Residing at the intersection of linguistics and AI, Natural Language Processing (NLP) makes computer programs capable of analyzing, predicting, and deriving meaning from human languages. The majority of AI algorithms in commercial use today are designed for modeling structured data, which is data that conforms to specific predefined formats. However, it is generally accepted that 80% to 90% of all data generated daily is unstructured. While there are many types of unstructured data, text and audio containing human language (email, webpages, text documents, social media posts, PDFs, audio recordings, etc.) account for a significant portion. For example:

NLP can also help companies to improve upon their existing data practices by automating data collection as well as augmenting their modeling datasets with additional information gleaned from unstructured data.

Parallel Processing with GPUs

To understand what the GPU (Graphical Processing Unit) does, it is worthwhile to distinguish it from the CPU (Central Processing Unit). The computer or mobile device you are using right now uses its CPU to operate, which handles much of the computer’s necessary work until it comes to rendering graphics; that is when the work is handed over to the GPU. While GPUs were originally used exclusively for rendering high-quality images and videos, particularly in gaming systems, they have been shown to provide fast parallel processing of data versus CPUs, which provide fast serial data processing. The GPUs’ ability to quickly process data, in parallel, makes it ideal for the kinds of processing required of AI workloads.

The still-burgeoning area of Artificial Intelligence, which includes Machine Learning and Natural Language Processing requires computer systems to “think” and “learn” while processing data as quickly and efficiently as possible. GPUs’ strength in parallel processing for NLP allows companies to create models for fast and repeatable image, sound, text, and data classification, perception, learning, and analysis. GPUs are a natural fit for companies that have image classification and recognition workloads.

Adoption of GPU technology on self-hosted infrastructure is tricky due to fast-evolving technology. Because new generation GPUs are released every year, and new models are introduced every few years, a cutting edge GPU does not stay top-of-the-line for long, and it is important to understand how different GPUs handle required workloads. This creates an obstacle for some organizations to make and maintain the investment in GPU technology, which is why its use is still not widespread.

Omnichannel Insight Automation

In order to blend web and non-web data, organizations often must revise their website design, website analytics tracking software, the structure of their campaigns, and organization of the non-web data that is to be joined with website data. Web analytics tools utilize cookies or a combination of computer and browser attributes to identify unique visitors. This can be combined with personally identifying information through form web submissions, logins, or passthrough identifiers in a personalized URL that brings the visitor to the site. The key is setting up website or campaign design to capture the visitors’ non-web identification online to allow the match of data sources. Importantly, opt-in mechanisms and disclosure prior to collecting and combining such data are of critical importance in order to maintain compliance with US and global privacy regulations.

Once organizations combine these data sources, they can dissect the channel-specific impacts of marketing campaigns on transactions, measure engagement of various customer segments across channels, and enable personalization of content across channels at the customer level for a better, and potentially more profitable, experience.

Keeping up with the Curve

As data science as a whole is continually evolving, it is critical that businesses not only stay up to date with the latest trends, but know which tools are right for their needs. Data science is embedded into the fabric of every business, both big and small.

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