The Basic Attributes of Data and AI-driven Companies

Here are the top habits of successful data and AI-driven organizations

We're currently in a "data renaissance" where enterprises realize that to execute on novel data and AI use cases, the legacy model of siloed technology stacks needs to give way to a unified approach. In other words, it's not about just data analytics or just ML – it's about building a full enterprise-wide data, analytics, and AI platform. They also recognize that they need to empower their data teams with more turnkey solutions in order to focus on creating business value and not building tech stacks. Organizations also realize that the strategy can't be some top-down authoritarian initiative but needs to be supported with training to improve data literacy and capabilities that make data ubiquitous and part of everyday life. Ultimately, every organization trying to figure out how to achieve all this while making things simple. Here are the top habits of successful data and AI-driven organizations.

AI is in Our Future and We Better Grasp It Already

The world organizations like Rolls-Royce, ABN AMRO, Shell, Regeneron, Comcast, and HSBC are using data for advanced analytics and AI to deliver new capabilities or drastically enhance existing ones. And we see this across every vertical. In fact, Duan Peng, SVP of Data and AI at WarnerMedia, believes "The impact of AI has only started. In the coming years, we'll see a massive increase in how AI is used to reimagine customer experiences."

Open Standards for the Future

The challenge to this approach is that many data practitioners and leaders associate "open" strictly with open source, but oftentimes, you've got an open-source engine, and it was just about how you get services and support around it. Every organization is under increasing pressure to fly the plane while it's being upgraded, but as we get to the point where there are multiple options on how to fly and upgrade, that open nature allows optionality for the future. The optionality enabled by an embrace of open standards and formats is becoming a critical component organizations are increasingly prioritizing in their strategies.

The Multiverse of Multi-cloud

Some of the organizations are already multi-cloud and some are on their way to entering that multiverse. The major agents of driving multi-cloud are the ability to deliver new capabilities with cloud-specific best-of-breed tools, mergers and acquisitions, and requirements of doing business like regulations, customer cloud-specific demands, etc. But one of the biggest drivers is economic leverage. As cloud adoption grows and data grows, for many, spending on cloud infrastructure will be one of the largest line items. As organizations think about a multi-cloud architecture, two things roll up to the top as requirements. First, the end-user experience needs to be the same.

Make The Building Blocks Simple

As companies are focusing more in-depth on modernized data handling systems, the complexity of data architecture is growing. It's time for organizations to upgrade their ability to simplify the new insights of building data products as it will help them drive innovation at a faster pace. Experts explain that the data teams of the organizations spend a lot more time in research than in data curation, management, or pipeline. However, it is impossible for the engineers to do all the data management by themselves no matter how much they want it. What they will have to focus on is to automating the other process as much as possible but putting manual effort into making the data presentable and valuable enough for the clients as well as the organizations. Well, it is not as simple as it sounds. Because there is apparently no end to data as it keeps coming in new forms every day.