9 AI Trends That Will Revolutionize Data Science

Shane Barker is a digital marketing consultant who specializes in influencer marketing, content marketing, and SEO. He is also the co-founder and CEO of Content Solutions, a digital marketing agency. He has consulted Fortune 500 companies, influencers with digital products, and a number of A-list celebrities.

Data science is vital to business success. It’s our window into the likes and habits of our customers, creating opportunities to glean insights from the mountains of data we collect every day.

Data has always helped businesses with decision-making, but AI is taking it a step further. So much so that today it can even be applied to the practice of creating impressive email subject lines. Machine learning for information management is now a key ally for every organization worldwide.

Artificial intelligence and machine learning are making the data science process far more efficient, giving us unheard-of access to customer insights at a time when businesses are constantly trying to find new ways to market to an oversaturated population. 

That’s why the AI industry is booming, with the machine learning market size alone hitting $21.17 billion in 2022. That number is expected to grow exponentially in the next few years, hitting $209.91 billion by 2029. 

Simply put, the partnership between data science and AI is not going away anytime soon. That is why we put together this list of nine major AI trends that will revolutionize data science in the coming years. 

AI Trend 1: Data Tiering and Diversity

First up on the list of booming AI trends in 2023 is data tiering. It is the process of moving the endless mountains of cold (irrelevant or rarely used) data to lower-cost solid-state drives. The goal here is to optimize the storage and retrieval of data by placing it in the most appropriate location. This frees up more prime real estate for actionable data without a person having to sit through the mind-numbing process of moving it manually. 

Data tiering can be helpful for data scientists because it automatically removes unimportant noise and frees up space. That means your AI system will have fewer data to sift through while creating models and aiding the machine learning process.

Data diversity is another exciting development that’s improving machine learning algorithms. It creates more complete data sets that prevent automated algorithms from creating a bias that might skew your insights. A dataset with more variability is less biased because it includes a broader range of features, which gives the model more options to learn from. 

Having a diverse dataset can also prevent your AI models from “overfitting,” which occurs when the model only learns from data points that are similar to what it has already seen. Overfitting can lead to poor generalization and performance on new, unseen data. 

By training on a diverse dataset, your AI model is exposed to a broader range of data points, which can help prevent overfitting and improve its ability to generalize to new data.

AI Trend 2: Predictive Analytics

In the AI world, predictive analytics refers to using customer data to predict massive trends or consumer shifts that will occur in the future. As opposed to other BI technologies, predictive analytics is “forward-looking,” which means that it uses past and current events to predict the future.

Predictive analytics is driven by machine-learning algorithms that perform pattern matching to determine how closely new data matches a reference pattern. A predictive score can be generated for each individual based on the algorithms that are trained on real customer data.

Here’s an example. Suppose an airline company wants to increase its ROI. Predictive analytics can help the airline predict the number of passengers who will not show up for a flight. By doing this, the airline can reduce the number of overbooked flights that require finding new accommodations for passengers, as well as the number of empty seats on the plane. 

Predictive analytics is not about forecasting how many ice cream cones you’re going to sell next quarter; it is about which individual person is likely to be seen eating ice cream.

Eric Siegel, author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

This helps the data science process as companies can anticipate dramatic behavior shifts in their target audiences and adapt to those changes as they occur. Using model output from predictive analytics, marketing managers can develop new strategies to never fall behind again, and sales managers can make special offers to customers at a high risk of churn.  

AI Trend 3: Cloud-Native Analytics Might Become Essential

As the cloud becomes more common for SaaS solutions and other vital company systems, we’re going to start seeing a lot more importance placed on cloud-native analytics. 

When someone says “cloud-native,” they’re referring to an application or service developed specifically to reside on the cloud with no physical server. These solutions are typically much more affordable and convenient for businesses. That has become especially true as remote workplaces become more common. 

Cloud-native analytics uses historical event data to predict which events are likely to occur and when, as well as which events may happen together. This approach offers several benefits to businesses, including:

• Faster time to market
• Self-service automation
• Scalability for accelerated product development
• Consistent experiences across the development lifecycle through CI/CD
• Advanced security measures to protect data and ensure compliance with regulations

AI Trend 4: Augmented Data Management

Augmented data management will become essential as the field of data science continues to grow. It’s the process of using AI-automated platforms to perform menial data management tasks that bogs down human data scientists. 

This includes actions like data replication, transformation, and enrichment while moving data from various sources into data stores for reporting. These sources could be data warehouses or data lakes. It’s a process that fuels both business intelligence and machine learning, and it can all be handled using AI and ML algorithms. 

AI Trend 5: Data-as-a-Service (DaaS)

DaaS is a business model where entities buy and sell data as part of their applications that can be used in data science efforts. This data is valuable, and many companies out there will pay good money for a glimpse into the behaviors of consumers who aren’t yet buying from them. 

AI is used in DaaS to compile and segment the information, ensuring that the entities that purchase data stores only receive relevant and actionable insights that they can use to improve their organizations. AI can also automate complex data cleaning, standardization, and reporting processes, helping to accelerate the generation of insights and facilitate decision-making. 

AI Trend 6: Automated Machine Learning (AutoML)

Machine learning can be hugely beneficial to businesses, but automated machine learning takes the process even further. 

When using AutoML, building a machine-learning model is completely automated. That cuts down on a very time-consuming process, which means your system will start learning to anticipate and understand the needs of your audience even faster. 

AutoML allows data scientists to automate the process of building machine learning models, eliminating the need for manual techniques such as feature engineering and hyperparameter optimization. This frees up data scientists to focus on more strategic tasks in the data science workflow while also improving the speed, consistency, and performance of the model-building process. 

By reducing the barriers to entry for machine learning and enabling expedited experimentation, AutoML can help organizations more easily adopt and benefit from machine learning technologies.

AI Trend 7: Low-Code/No-Code AI and Their Advantages

You no longer have to be a tech wizard to set up an AI program and reap the benefits. Low-code/No-code AI systems allow less tech-savvy business owners to set up their AI systems without hiring a full-time AI technician.

Many start-up businesses might have once missed out on the advantages of AI and data science, falling woefully behind larger competitors who could afford to bring in a tech expert to oversee their systems. But thanks to low-code/no-code AI platforms, the playing field has been leveled, opening up data science to all. 

Low code/No code systems are designed to make app development and model building more accessible and efficient by providing simplified frameworks and pre-built templates and building blocks. These systems allow organizations to quickly and easily get started with tasks such as optimizing workflows, predicting churn, and suggesting recommendations. 

By providing ready-made datasets and other resources, low code/no code systems significantly reduce the time it takes to bring a product or service to market or put it into use. 

AI Trend 8: Role of Data Privacy in Determining Customer Loyalty

All businesses must implement a strong data security system and data protection structure to prevent potential attacks and breaches. 

In the telematics industry, there is a need for telematics data privacy and compliance as information is constantly shared and transmitted through electronic logging devices. For example, telematics systems record sensitive data when mobile devices are connected to automobiles and used to make calls or open applications. That’s why data governance and management must be implemented in environments you might not think of at first.

By year-end ’23, 40% of technology used for privacy compliance will rely on AI capabilities, up from less than 5% in 2019. 

Gartner 2019 Predictions

If you want to generate repeat business (and who doesn’t?), you’ll need to use automated AI systems to keep this valuable data secure, according to the guidelines set out in your privacy policy. In this matter, your employees will be more ready to participate in your customer referral program and recommend your business to others.

AI Trend 9: Automated Data Cleaning

The data cleaning process can also be automated using AI, wherein corrupted, irrelevant, inaccurate, or duplicate files are removed. Once, this process fell to human workers who had to sift through endless piles of data, missing various files and creating an incomplete system. 

Now, thanks to AI, the process is foolproof and can be done in an instant. 

Additionally, you’ll be able to use data transformation tools like dbt Labs, which can speed up analysis efforts for your data team by cleaning raw data and adapting it into a usable format.

Wrapping Up 

Data science is an invaluable piece of our business puzzle, which provides a previously unheard of view into the habits and trends that surround our audiences and businesses. 

As we wrap up our exploration of the nine prospering AI trends in 2023, it’s clear that AI has the potential to revolutionize data science and drive significant improvements in a variety of industries. From predictive analytics and augmented data management to auto ML and cloud-native analytics, the advances in AI and machine learning are truly staggering. 

By staying up-to-date on these trends and investing in them, organizations can leverage the power of AI and machine learning to improve their operations, make more informed decisions, and drive business growth. The future of data science is bright and full of endless possibilities, and by embracing these trends, businesses can position themselves to take advantage of the opportunities that lie ahead.