Your Data Architecture Holds the Key to Unlocking AI’s Full Potential

In the words of J.R.R. Tolkien, “shortcuts make long delays.” I get it, we live in an age of instant gratification, with Doordash and Grubhub meals on-demand, fast-paced social media and same-day Amazon Prime deliveries. But I’ve learned that in some cases, shortcuts are just not possible.

Such is the case with comprehensive AI implementations; you cannot shortcut success. Operationalizing AI at scale mandates that your full suite of data–structured, unstructured and semi-structured get organized and architected in a way that makes it useable, readily accessible and secure. Fortunately, the journey to AI is one that is more than worth the time and effort.

AI Potential: Powering Our World and Your Business

That’s because AI promises to be one of the most transformational technologies of our time. Already, we see its impact across industries and applications. If you’ve experienced any of these, then you’re seeing AI in action:

• Automated assistants such as Amazon Alexa, Microsoft Cortana and Google Assistant.
• COVID vaccines and/or personalized medicine used to treat an illness or disease.
• Smart cars that alert drivers like you, help you park and ping you when it’s time for maintenance.
• Shopping preferences that are tailored to your specific tastes and proactively sent to you.
  
  

Despite these AI-powered examples, businesses have only just begun to embrace AI, with an estimated 12% fully using AI technology.¹ But this is changing rapidly. And that’s because AI holds massive potential. In one Forrester study and financial analysis, it was found that AI-enabled organizations can gain an ROI of 183% over three years. _²

That’s why AI is a key determinant of your future success. Businesses that lead in fully deploying AI will be able to optimize customer experiences and efficiencies that help maximize customer retention and customer acquisition and gain a distinct advantage over the competition. The growing divide between AI haves and have-nots is underway and at a certain point, that chasm will not be crossable.

For example, today airports can use AI to keep passengers and employees safer. AI working on top of a data lakehouse, can help to quickly correlate passenger and security data, enabling real-time threat analysis and advanced threat detection.

In order to move AI forward, we need to first build and fortify the foundational layer: data architecture. This architecture is important because, to reap the full benefits of AI, it must be built to scale across an enterprise versus individual AI applications. 

Constructing the right data architecture cannot be bypassed. That’s because several impeding factors are currently in play that must be resolved. All organizations need an optimized, future-proofed data architecture to move AI forward.

Complexity slows innovation

Data growth is skyrocketing. One estimate³ states that by 2024, 149 zettabytes will be created every day: that’s 1.7 MB every second. A zettabyte has 21 zeroes. What does that mean? According to the World Economic Forum⁴, “At the beginning of 2020, the number of bytes in the digital universe was 40 times bigger than the number of stars in the observable universe.” 

Data’s size alone creates inherent complexity. Layered on top of that are the different types of data stored in various siloes and locations throughout an organization. It all adds up to a “perfect storm” of complexity.

A complex data landscape prevents data scientists and data engineers from easily linking the right data together at the right time. Additionally, multiple systems of record create a confusing environment when those sources do not report the same answers.

Extracting value from data

Highly skilled data scientists, analysts and other users grapple with gaining ready access to data. This has become a bottleneck, hindering richer and real-time insights. For AI success, data scientists, analysts and other users need fast, concurrent access to data from all areas of the business.

Securing data as it grows

Securing mission-critical infrastructure, across all data in an enterprise, is a default task for every organization.  However, as data grows within an enterprise, more desire for access and use of that data produces an increasing amount of vulnerable security end points.   

Catalyzing AI at Scale with Data Lakehouse

The good news is that data architectures are evolving to solve these challenges and fully enable AI deployments at scale. Let’s look at the data architecture journey to understand why and how data lakehouses help to solve complexity, value and security.

Traditionally, data warehouses have stored curated, structured data to support analytics and business intelligence, with fast, easy access to data. Data warehouses, however, were not designed to support the demands of AI or semi-structured and unstructured data sources. Data lakes emerged to help solve complex data organizational challenges and store data in its natural format. Used in tandem with data warehouses, data lakes, while helpful, simultaneously create more data silos and increase cost.⁵

Today, the ideal solution is a data lakehouse, which combines the benefits of data warehouses and data lakes. A data lakehouse handles all types of data via a single repository, eliminating the need for separate systems. This unification of access through the lakehouse removes multiple areas of ingress/egress and simplifies security and management achieving both value extraction and security. Data lakehouses support AI and real-time data applications with streamlined, fast and effective access to data.

The benefits of a data lakehouse address complexity, value and security:

• Create more value quickly and efficiently from all data sources
• Simplify the data landscape via carefully engineered design features
• Secure data and ensure data availability at the right time for the right requirements

For example, pharmacies can use a data lakehouse to help patients. By quickly matching drug availability with patient demand, pharmacies can ensure the right medication is at the right pharmacy for the correct patient.

Moving AI Forward

AI deployments at scale will change the trajectory of success around the world and across industries, company types and sizes. But first things first mandate that the right data architecture be put in place to fully enable AI. While data lake solutions help accelerate this process, the right architecture cannot be bypassed. As J.R.R. Tolkien intimated, anything worth achieving takes time.

Want to learn more?  Read this ESG paper.

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^[1] https://www.zdnet.com/article/what-is-ai-maturity-and-why-does-it-matter/ 

^[2] https://www.delltechnologies.com/asset/en-us/products/ready-solutions/industry-market/forrester-tei-dell-ai-solutions.pdf

^[3] Finances Online, 53 Important Statistics About How Much Data Is Created Every Day, accessed April 2022

^[4] https://www3.weforum.org/docs/WEF_Paths_Towards_Free_and_Trusted_Data%20_Flows_2020.pdf

^[5] https://www.dell.com/en-us/blog/break-down-data-silos-with-a-data-lakehouse/