The Top 5 AI & Machine Learning Trends for 2021 And Beyond

2019: The Year of AI and Machine Learning

January 14, 2019

2018 will be remembered, in many ways, as the year that disruptive emerging technologies began to reshape business models and change the economics of organizations. According to Gartner, digital business reached a tipping point last year, with forty-nine percent of CIOs reporting that their enterprises have already changed their business models, or are in the process of doing so. When CIOs and IT leaders were asked which technologies they expect to be most disruptive, artificial intelligence (AI) was the top-mentioned technology, with data and analytics taking second place. Based on my work leading more than 100 data analysis projects across a variety of industries, the combination of these technologies is hardly surprising; it’s an accurate reflection of what my colleagues and I see in data science on a daily basis. Data science is now a major area of technology investment for organizations, driven by the impact it can have on customer experience, revenue, operation, supply chain, risk management and many other business functions. As we enter 2019, new advances in AI and machine learning are not only democratizing data science, they’re also enabling organizations to make data-driven decisions with unprecedented levels of transparency and accountability.

Additionally, these advances have enabled the complete automation of data science at speeds that empower organizations to accelerate their digital transformations while operationalizing ten times (10x) more projects with transparent outcomes. Here’s what we expect 2019 to bring for data science, AI and machine learning:

The pressure to achieve greater ROI from AI and ML initiatives will push more business leaders to seek innovative solutions.

While substantial investments are being  made into data science across many industries, the scarcity of data science skills and resources limit the advancement of AI and ML projects within organizations. In addition, one data science team is only able to execute several projects a year given the iterative nature of the process and the manual work that goes into data preparation and feature engineering. In 2019, data science automation platforms will capture much of the mind share. Data science automation will cover much wider areas than machine learning automation, including data preparation, feature engineering, machine learning and the production of data science pipelines. These platforms will accelerate data science, and execute more business initiatives whilst maintaining the current investments and resources.

Transparency and interpretability will become even more important than accuracy.

Traditional data science approaches are “black boxes” that result in less actionable business outcomes.  Given the current regulatory climate, as it relates to profiling (GDPR, etc.), businesses are demanding increased transparency along with actionability.  Current data science processes focus primarily on accuracy and not transparency. In 2019, we will see an emergence of new tools that will enable data scientists to have greater transparency, without sacrificing accuracy. This shift to a more “white box” approach to data science will deliver more transparent and accurate models.  It will thereby empower businesses to make data-centric decisions and accelerate their digital transformations.

The democratization of data science and the increasing importance of “citizen” data scientists.

Big data is on the rise, and with it has come a growing demand for skilled data scientists. The shortage of data scientists has created a big challenge for businesses implementing AI and ML initiatives.  It has created bottlenecks and slowed time to production. In 2019, we will begin to see the rise of new data science platforms that will significantly simplify tasks that could formerly only be completed by data scientists.  Now, data science process automation has unlocked data science accessibility for non- data scientists, such as BI engineers, data engineers, and business analysts.  It allows data scientists to focus on more complex and high-value projects. Democratization of data science through automation empowers enterprises to not only accelerate, but establish the culture of data-driven decision making across entire organizations.

Data science tasks will become a 5-minute operation and deliver business values in days.

Gone are the days of waiting several months for a data science project.  In 2019, we are going to see a transformation in how businesses implement and optimize their AI and machine learning initiatives. New data science automation platforms offer a single, seamless platform that enables companies to accelerate, democratize, and operationalize the entire data science process – from raw data through feature engineering to machine learning – eliminating the most time-consuming and labor- and skill-intensive tasks.  As a result, projects that once took months to complete will now take only days.  This will significantly accelerate the time to value for AI and machine learning initiatives.

Operationalizing data science will become the next challenge.

While businesses are realizing that AI and machine learning can help drive business transformation, AI and ML models can only generate business value when they are fully operationalized and put into production. The traditional approach to data science is time-consuming and resource-intensive, resulting in many data science projects either cut, or ending up as one-time reports.  In the next year, model operationalization, as well as model development, will be significantly simplified by automation.  New data science automation platforms will enable enterprises to deploy, operate and maintain these improved processes in production helping companies maximize their AI and ML investments.


dotData Inc.

dotData Automated Feature Engineering powers our full-cycle data science automation platform to help enterprise organizations accelerate ML and AI projects and deliver more business value by automating the hardest part of the data science and AI process – feature engineering and operationalization. Learn more at, and join us on Twitter and LinkedIn.