Data and analytics are helping today's enterprises in many ways, from decision-making to developing data-centric product offerings. Several themes are quickly gaining pace in the data analytics business. The most significant breakthroughs in 2022 introduce new methods to tackle complex problems, while others remove previous barriers to applying machine learning and data science to solve business problems.
Existing and emerging data science technologies solve problems in working with unstructured data, minimize the amount of training data required to create models, and reduce the manual labor required to label that data.
Most intriguing of all, many of these algorithms combine supervised and unsupervised learning, the two main approaches in machine learning. Data science is now more accessible and simpler than ever before, thanks to the approaching breakdown of the digital divide.
Increased focus on combining and improving Supervised and Unsupervised Learning
Unaided supervised learning necessitates massive data sets and effort costly annotations of business outcomes or causes. Unsupervised learning uses large amounts of training data to find patterns or features without annotations. There are many strategies that combine these two approaches to reduce the quantity of training data or labels used. Some of them are:
- Self-Supervised Learning, which enables machine learning without any labeled data pioneered by researchers at Facebook.
- Semi-Supervised Learning, where data scientists need to provide a small amount of labeled data into the unsupervised system.
- Generative Adversarial Networks are networks that are able to generate novel data based on examples from a training dataset. This is the technology behind deep-fakes, but also numerous other business applications.
- Reinforcement Learning allows learning from synthetic or generated datasets simulating environments, which is often used in robotics.
- Transfer Learning is a time-honored approach that conveys the learning of one generalized model into another that’s oftentimes specific to an organization’s individual use case for things like computer vision or text analytics.
Democratizing data with Automated machine learning
AutoML is a hot movement that's democratizing data science. AutoML solution providers strive to build tools and platforms that anybody may use to create ML apps. It's aimed at subject matter experts who have the expertise and insights to find answers to the most pressing challenges in their fields but lack the coding skills to apply AI to those problems.
Data purification and preparation tasks often take up a large percentage of a data scientist's work and need data skills but are repetitive and tedious. Creating models, algorithms, and neural networks is increasingly part of AutoML. The goal is for anyone with a problem or an idea to test to be able to apply machine learning through simple, user-friendly interfaces that hide the inner workings of ML so they can focus on solutions. In 2022, we'll be a lot closer to this becoming a daily occurrence.
Currently, all cloud-services providers like Azure and AWS offer such services and although these solutions are rarely a cure to every problem, they are quite useful in building automated ML pipelines and the MLOps practice within an organization.
Managing and training data complexities
The amount of training data required to construct viable machine learning models for corporate applications is prohibitive. Some domains simply lack sufficient data, potentially hindering data science initiatives. Transfer learning and GANs reduce the quantity of training data necessary or generate enough data to educate models. Transfer learning can be useful in both NLP and computer vision tasks, as large pre-trained model architectures like Google’s BERT are available to be fine-tuned using smaller datasets and towards a specific task. On the other hand, GANs can be quite useful in generating “synthetic” datasets in areas where data is scarce. These models have proven to do well in multiple areas like time-series, computer vision, and image processing.
Also, while working with supervised learning (which is the majority of data science projects), consider how much time and money it takes to classify data. Aside from transfer learning, GANs, and reinforcement learning, other methods for speeding up data labeling include:
- Unsupervised Learning – models find characteristics in the data themselves about a specific business problem with techniques like clustering. Coupling unsupervised learning with supervised learning can diminish the amount of labeling needed.
- Neuro-Symbolic AI utilizes its statistical and knowledge foundations in tandem to greatly reduce the reliance on labeling data. It generates knowledge representation from the data and then transfers it to different tasks, not requiring to label data anymore. This is still an area heavily in academic development.
- Encoding and Embedding - embedding and encoding can map multidimensional data into a lower-dimensional space to glean relationships between attributes in data. This is essential for tackling NLP tasks.
Composable Data & Analytics
While the cloud has taken the tech landscape by storm, many businesses still find on-premise infrastructure more efficient, reliable, and accessible for fine-tuning by administrators. While the cloud provides agility, on-premise services provide more granular control. Companies that prefer this traditional approach, however, don’t have to sacrifice the benefits of modern trends - thanks to composable data.
Composable data is collected from various sources across the enterprise and can be easily disseminated to remote machines and devices. The architecture behind it stands up and down virtual servers for specific tasks and workloads, using reusable, swappable models, and making this a very efficient and agile solution for companies with traditional on-premise infrastructure.
The result is more holistic data that enables objective, intelligent, and balanced analytics and decision-making. According to a report by Gartner, 60% of companies will employ composable data & analytics by 2023.
Big data necessitates complex and elaborate systems and huge bandwidth, which is why it has propelled developers to break new ground with highly intelligent and sophisticated solutions. But the next big step seems to be small data.
Specifically, businesses will find it efficient to automate using small data sets using techniques that identify insights within small or even microdata tables. Gartner predicts that 70% of companies will move to small and wide (composable) data by 2025.
A trend that has already been climbing rapidly but will continue its ascent in 2022 and beyond is predictive analytics. The global predictive analytics market is expected to grow at a CAGR of 24.5% by 2025, with businesses being determined to forecast market trends and consumer demand with ever-increasing accuracy.
The foundation of predictive analytics is solid data science that diligently processes and analyzes historical data. Whichever industry a business is in - from retail to human resource management to streaming - predictive analytics is a game-changer in identifying new value streams and getting a significant competitive advantage.
Consider how these trends affect your organization’s data science approach
Businesses generate vast amounts of data. Having professionals and digital technologies that gather, structure, analyze and interpret this data is vital to any company’s continued success, particularly in competitive markets.
These top trends in data science will be defining for establishing the data science practice in business organizations in the next few years. Some of them like the AutoML movement remove barriers to building ML processes and pipelines, while others like self-supervised learning are still considered avantgarde, but are expected to enter the business domain rapidly. Therefore, it is important to watch for these trends if you want to keep your data science practice and teams up-to-date with the newest innovations.