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Building the Foundation for an AI-Ready Organization

According to recent Gartner reports, an estimated 85% of artificial intelligence (AI) projects will fail through the year 2022. This estimation is likely to cast some doubt on the realm of artificial intelligence and its ability to deliver value. However, the reason that so many AI projects fail is not due to the AI processes themselves, but rather the lack of strong data governance, collaboration, and problem definition. Organizations that successfully leverage AI typically start their design with the goal of AI in mind. This approach allows organizations to work backwards, guiding them through the foundational components that help contribute to the end goal.

Jumping into an AI project without a strong foundation can be a bit like buying a powerful car engine without any way to use it. While we may look at the engine and dream of the excitement it will someday produce, it isn’t providing any real value without the car frame, wheels, body, transmission, etc. To continue with the car analogy, we can (subjectively) map some aspects to the world of data:


The wheels are like a data governance strategy that provides processes, security, accessibility, and accountability. Without strong data governance, the data itself may become unusable, inaccurate, or even unsecured. This is a critical step to becoming a successful data-driven organization, but is often incomplete or overlooked altogether.


If the wheels are the governance strategy, then the frame is the foundational infrastructure, networking, and security that enable the data initiatives. This includes how you plan to access your data resources (such as in Azure, AWS, etc.), which service types (Infrastructure as a Service or Platform as a Service) are the best for your goals, methods to minimize sprawl, and how a data initiative aligns with the rest of the organization’s initiatives.


The body, like data platform services, can then be bolted on to the frame. These resources, such as a Data Lake or Data Warehouse, are critical to supporting organizational analytics initiatives. Resources that are responsible for governance-related activities are also included here, such as metadata management services. These resources also need the ability to scale and flex as the organization’s initiatives shift.


Like a transmission, core Extract, Transform, Load (“ETL”) services are responsible for receiving/extracting, transforming, and copying data from sources to destinations. This process is the glue for connecting data from the organization into the necessary systems that will be analyzed by consumers.


If everything up to this point can be considered the foundational components to a modern data platform, then the engine would be the analytical processes and tools that help convert the stored data into valuable insights (e.g., visualization and reporting, machine learning, AI). The engine is what makes the entire car function as intended, or what is providing the greatest value to the organization. Without the engine, the organization would only be collecting data with no intended purpose.

This is not an exhaustive list, as any car guru reading this may already be thinking. Other ancillary aspects surround these core components, such as the enablement of an experimental culture, executive support of data as an organizational asset, development and hiring of the necessary skillsets, and the trust and use of data in the decision-making process. These other aspects could be seen as the processes and building blocks of the facilities that help support the manufacturing of cars.

Every aspect of the business has a role to play in developing an AI and data-driven organization. Some organizations have every solution and process defined with minimal errors or faults, while others are struggling to design and build the foundational aspects. This does not mean that only the most advanced organizations will benefit from AI. We believe that every organization can benefit from using AI and data to develop a true competitive edge in their market—we just need to start with the end in mind and build a strong foundation.

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About the author:
Ben Prescott has spent the last 15+ years working in various technology roles, with 10 years specific to cloud consulting. Serving as ConvergeOne’s Analytics Practice Manager, he is responsible for growing and leading a team of senior data professionals who help organizations uncover value in their data. Ben holds a master’s degree in data science from Northwestern University, multiple cloud certifications, and is a Microsoft Certified Trainer.