5G, AI, and Post-It Passwords Front and Center

By executives of Cloudera

Cloudera's executives tap their crystal ball for what they hope to, or plan to, see in 2021 when it comes to 5G, AI, Machine Learning, and Cloud. Top 5 takeaways? Emerging countries could have 5G before you do, conversations around ethical AI are not only going to continue but also grow, companies will replace technology with strategy when thinking of cloud migration, and a renewed cycle of rethinking the design for machine learning models.

Cindy Maike, VP of Industry Solutions

Emerging Geographies Will Outpace Us In 5G Adoption

"5G" has been on smartphones for a while now, but most people don't realize that their phone isn't actually running 5G - that's because, while the smartphone may be able to support 5G capabilities, they are useless without a 5G network infrastructure. Telecommunications innovation by providers across the world has been excellent and we are just starting to set their sights on upgrading current network technology to support 5G but more is needed. However, before we can even talk about getting the 5G network up and running on smartphones, we need to get more of the infrastructures (a sufficient mix of low-, mid-, and high-band spectrum) to support the full potential of 5G. In order to have 5G-enabled devices, we first have to make the capital investment in the infrastructure. 

Telecommunications providers are starting to make this happen, but, depending on geography, upgrading it is rather expensive. Emerging geographies may outpace mainstream geographies in this adoption because they don't have any legacy systems, regulatory policy constraints or current infrastructure that needs to be updated. It's much simpler to simply put in the latest and greatest technology system rather than need to overhaul a current system. At Cloudera, we're already seeing phenomenal 5G infrastructure growth in our customers out in Africa and some Asian countries because they can streamline their 5G implementation. I wouldn't be surprised if we see these countries solely running on 5G by the end of 2021, where it will likely take the U.S. several years to get to that same level. 

Ethical AI & Data Governance

We will see ethical AI become front and center in the next 12 to 24 months. Today, ethical AI conversations revolve around the anonymization of data - we're already starting to see new legislation in Australia and Europe, and I believe the U.S. isn't far behind. We need to work on anonymizing data for the good of society, and, furthermore, ensuring we have strong data governance that monitors how this data is being used. A big conversation over the past year was about the enterprise data cloud can help companies simplify their governance and management of data and AI in the cloud, so we're now taking this one step further with ethical AI.

As we look to 2021, we will see the conversation of ethical AI and data governance be applied to multiple different areas, such as contact tracing (fighting COVID-19), connected vehicles and smart devices (who owns the data?), and personal cyber profiles (increased cyber footprint leading to privacy questions).

Anupam Singh, Chief Customer Officer

Public Cloud Is Still Post-Its And Passwords

Companies are still thinking technology first, not strategy first when it comes to the cloud. This can be seen in every company because the public cloud is still post-its and passwords with no clear-cut answer on who is responsible for cloud security privileges. There is so much great enabling technology in the cloud, but organizations haven't thought through how to properly use it to their advantage. I predict that next year we'll see security and governance take center stage. Everyone thinks of the cloud as a cost-effective and efficient solution, but the key that they're missing is the governance model. This is where the enterprise data cloud comes in and we'll see a realization and shift of focus to security and governance next year. It's simple, if you don't have a strong security and governance system in place, then anyone can access your data and you risk leaving yourself vulnerable to outside hackers or insider threats.

Santiago Giraldo, Senior Product Marketing Manager of Machine Learning

The Ability to Trust and Operationalize ML will be 2021's Litmus Test For Survival 

On top of a pandemic and a recession, we're continuing to grapple with the exponentially growing amounts of data and ever-increasing complexities of new technologies. If businesses want to be successful in making sense of their large data sums and technical complexities, they must leverage and operationalize machine learning models in explainable and easy to understand ways. It is no longer enough to focus on getting models into production, the focus must now be on getting models into the hands of the business users and decision-makers. But to operationalize, businesses must be able to trust in, derive understanding from, and communicate about, a model's ability to meaningful impact business potential. In 2021, a business' ability to trust its model -- to the extent that they are able to produce action from AI-derived insight - will be determinant of its ability to survive.

Teaching To Predict For Unpredictability

The unpredictability of 2020 will permanently rethink what we design for and ask of our machine learning models. The expectations of a model's ability to perfectly predict will be less important than a model's ability to automatically flex. In turn, we will increasingly look to models that perfect notably human attributes - proactivity, instinctive agility, the ability to expect the unexpected - to frame our build, expectations, and operalizations of machine learning. As a result, we will not only shift how we build, but what we build for humans. Additionally, this past year has been a proof point that there's no such thing as having ‘fully digital;' humans have to always be in the loop. With shifting landscapes, there must be synergy between people and digital, and keeping people a part of strategy and data science processes is critical.

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