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2021 Trends: Where Enterprise AI Is Headed Next

Get the rundown of how the events of 2020 shaped the future of Enterprise AI and some of the budding trends that we’ll see in 2021 and beyond.

2020 Year in Review

While 2020 was markedly unique with regard to its impact on the data science, machine learning, and AI space because of the global health crisis, many organizations have taken it as a valuable lesson learned — any period of disruption (new technology, economic downturn, environmental disaster, new competition, to name a few) can cause irreversible and damaging effects if there’s no plan in place that allows for agility and survival.

As companies aim to recover and understand their new market dynamics, it remains critical to implement AI systems that are persistent and resilient during times of economic flux. 2020 reinforced the need for core concepts that have always been part of our DNA at Dataiku, such as collaboration, agility, and responsibility.

In 2020, organizations worldwide committed to Enterprise AI efforts from the top down, but struggled to democratize projects from the bottom up to give more individuals access to actionable data insights (and, in turn, embolden them to use data in their day-to-day decisions). Data science has the capacity to generate long-lasting impacts, but it’s important to bring those on the periphery into the fold, as it shouldn’t exclusively be data executives and practitioners that help drive those impacts on a daily basis.

This concept is one we’ve been talking about since our inception at Dataiku — in order to be a data-powered organization, everyone needs access to the data they need to do their jobs and, in 2020, we saw organizations start to wholly understand and implement a self-service analytics (SSA) vision. Taking that a step further, not only are organizations grasping and implementing SSA, but they are going beyond it to operationalize their data projects to drive meaningful change. Ultimately, 2020 enabled data science, machine learning, and AI to emerge as critical organizational assets for handling large-scale change with less friction. 

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Looking Ahead

Here are a few key trends we believe we’ll see manifest in 2021 and beyond:

1. MLOps isn’t going anywhere: And, in fact, it’s only growing in importance. In 2020, the significant data drift observed resulted from the health crisis. As a result, in 2021 we’re bound to see organizations use MLOps to implement more robust structure around drift monitoring so that models can be more agile and accurate. They’ll also move beyond strictly using MLOps for periods of volatility, and aim to implement MLOps practices for the long term in order to more effectively scale their machine learning efforts.

AI experimentation, a key part of MLOps, will become more strategic. Experimentation takes place throughout the entire model development process — usually every important decision or assumption comes with at least some experiment or previous research to justify those decisions. Experimentation can take many shapes, from building full-fledged predictive ML models to doing statistical tests or charting data. 

Trying all combinations of every possible hyperparameter, feature handling, etc., quickly becomes untraceable. Therefore, we’ll begin to see organizations define a time and/or computation budget for experiments as well as an acceptability threshold for usefulness of the model.”

Florian Douetteau, CEO of Dataiku

2. Agility, agility, agility: In 2021, the use of AI for sustained resilience will be underscored, particularly with regard to empowering every team and employee to work with data to improve their business output. Businesses will need to be able to pivot in future times of uncertainty, so they’ll need to be equipped with the skills and AI systems to adapt over time (think: a collaborative data science tool that allows people across job functions to access data and work together on projects in a central location, facilitating strong governance practices and breaks down silos).

From a people perspective, the year 2020 more or less normalized remote and hybrid working styles, making collaboration even more critical for agility and efficiency. Once this has been achieved within teams, organizations should expand their collaboration to other departments and business units so that, eventually, data and analytics are deeply ingrained in the company’s DNA. A big part of this is becoming familiar with reuse and capitalization so that no productivity is lost and work isn’t duplicated.

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3. Organizations have learned about Responsible AI, now they want to implement it: 2021 will be the year that organizations gain the expertise to implement the responsible use of AI across their existing and future use cases. The continued rise of MLOps will help bring Responsible AI the attention it deserves, as organizations will need strong MLOps principles to practice Responsible AI and vice versa. Organizations will move beyond awareness and understanding of the realities of Responsible AI and work to broaden the scope of people participating in the creation of AI tools and systems in a diverse and inclusive way.

4. Democratized data quality will play a key role: We anticipate that organizations will aim to give data quality the attention it deserves (and always has) by identifying data issues, alerting the proper teams, and putting processes in place to diagnose and manage data quality issues at scale. Without undercutting the importance of data quality, though, it is important to note that it is just one part of an overarching governance strategy and one part of the holistic data science and machine learning lifecycle. While AI tools and processes can certainly help usher in defining improvements to data quality, it needs to go beyond that and really be part of an organization’s end-to-end strategy.

By 2022, 60% of organizations will leverage machine learning-enabled data quality technology for suggestions to reduce manual tasks for data quality improvement.”

Gartner, Build a Data Quality Operating Model to Drive Quality Assurance, Melody Chien, January 2020

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