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2020: Starting a New Decade in the Age of AI

A look at the upcoming trends and opportunities of Enterprise AI that await data professionals and organizations in the new decade.

As we say goodbye to 2019 and enter into a new decade, we tried to not just look back at what was and wasn’t achieved in terms of AI progress in the past year, but also to look forward at what’s next for data-driven enterprises in 2020 and beyond. In this story, we offer you our take on the upcoming trends in AI, machine learning, and data science that will impact data scientists, analysts, executives and organizations as a whole in the year to come.

2019 Year in Review: Enterprise AI is Moving Fast, But We Still Have a Long Way to Go

There is little doubt that 2019 has been an exciting year for AI: advancements on the research side in machine learning and deep learning is moving incredibly quickly. Every day, there are breakthroughs (e.g., the quick dominance of DeepMind’s StarCraft II AI), new research (like on curiosity-driven learning), and new technologies, making computation cheaper and faster than ever.

In the context of all these major innovations, Enterprise AI is undoubtedly moving forward, though perhaps not at the pace the media coverage on AI makes it seem. While the use cases for AI in the enterprise are very promising, progress is slower than the AI fantasy the media sells. 

This is not necessarily bad news: after all, organizations are complex and need time to achieve truly impactful, sustainable transformation. There are engrained existing processes, revenue targets to hit, and – perhaps most importantly – there are people to consider. 

AI initiatives take time and are based in organizational change, both from the top down and from the bottom up. In many ways, investing in the right technology and hiring the right key people are the easy parts; it’s the business-wide transformation (including upskilling existing staff and incorporating data processes at all levels of the organization) that is challenging.” 

Florian Douetteau, CEO of Dataiku

AI Trends for the New Decade

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What Will the Second Generation of Data Scientists Look Like?

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The role of data scientists is inevitably impacted by changes such as the growing focus on data democratization, the push to move from just a few to hundreds – or potentially thousands – of models in production, as well as the increasing investment in AI systems to facilitate AutoML and leverage automation. 

This is not to say that data scientists are no longer valuable; on the contrary, in this landscape, they are probably more important than ever before. But in the year and decade to come, data scientists will have to redefine their role in the face of these changes, positioning themselves where they add the most value for the company. 

Here are some of the top things to know about the second generation of data scientists in 2020 and beyond:

  • Their skills will be the differentiating factor in the race to AI: The next generation of data scientists will be valuable because they are able to apply conventional approaches to unconventional problems – not to mention experimenting with and applying cutting-edge machine learning techniques – in ways that no citizen data scientist could do. 
  • Part of their role will be adding structure around data democratization initiatives. Citizen data science has been a hot topic for years, but many companies haven’t able to actually bring business value through citizen data science-led projects. The second generation of data scientists can address this by building processes around data democratization, incorporating ways to add expertise, approval, and operationalization to grassroots data initiatives.
  • They will still rely on business analysts. Though in an ideal world data scientists are acutely aware of business needs and interests, the reality is that they probably will never have as strong of a grasp on the business as analysts. The second generation of data scientists will understand this weakness and more tightly collaborate with business analysts to increase their effectiveness.

2020 Data Science Trend Report

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2020 – The Year of the Data Analyst?

New types of data, tools, and technologies are shaping the jobs of analysts, taking them in exciting new directions. In fact, things are moving so fast in the data analytics space, that some analysts are beginning to worry about what this could mean for the future of their jobs. Smart machine learning algorithms can now analyze and interpret data with an ever-growing speed and accuracy, and even produce content for dashboards and printable reports.

As with any other job out there, the role of data and business analysts might evolve in order to keep up with the most recent innovations, but it is here to stay in 2020 and beyond. In fact, the continuous developments in AI, machine learning, and automation, when implemented the right way, are less of a threat than an opportunity for data analysts. Experts predict that in the new decade, data analytics will be less about hoarding data and more about acting intelligently on data-driven insights, a task for which no one is better suited than analysts.

There’s even growing evidence to suggest that the most important role data technologists will play in the near future is that of a data analyst. The reason behind this growing trend is the increasing automation of many data-related tasks, and the growing importance of data analysis by humans.

The 2020 Analyst Playbook

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What’s Next for the Data-Driven Enterprise?

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In looking forward to 2020 and beyond, starting a new decade in the age of AI, there are several key overarching themes. If there are any areas for organizations to focus on in the years to come to succeed in the path to Enterprise AI, they are:

  • Elasticity. From architecture and scaling up and down resources to teams, businesses have to be ready for whatever the future of AI holds. That means not investing in (and getting locked into) one technology, one way of doing things, etc., but being able to pivot at any turn. Whether it’s a move to the cloud, embracing the next-generation data lake, new types of data scientists, or new mindsets, the industry will never stop moving.
  • Trust. Employees – whether in a data role or not – need to trust data from the bottom up, from its raw format to the AI systems it powers.Similarly, customers or users need to be able to trust the business, and that means deploying AI that is explainable and responsible. Businesses that ignore trust in the coming decade are sure to lose the AI race.
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