For companies that can master it, Artificial Intelligence (AI) promises to deliver cost savings, a competitive edge, and a foothold in the future of Business. But while the rate of AI adoption continues to rise, the level of investment is often out of kilter with monetary returns. To be successful with AI you’ll want the right data architecture. This article tells you how.
Currently, only 26% of AI initiatives are being put into widespread production with an organization. Unfortunately, this means many companies spend a lot of time on AI deployments without seeing tangible ROI.
Meanwhile, in a world where every company must perform like a tech company to stay ahead, there’s increasing pressure on technical teams and Engineering and IT leaders to harness data for commercial growth. Especially as spending on cloud storage increases, businesses are keen to improve efficiency and maximize ROI from data that are costly to store. But unfortunately, they don’t have the luxury of time.
To meet this demand for rapid results, mapping Data Architecture can no longer stretch on for months with no defined goal. At the same time, focusing on standard data cleaning or Business Intelligence (BI) reporting is regressive.
To do otherwise — they’ll find themselves retrofitting it later. In today’s businesses, data architecture should drive toward a defined outcome—and that outcome should include AI applications with clear benefits for end-users. This is key to setting your Business up for future success, even if you’re not (yet) ready for AI.
Data Architecture requires knowledge. There are a lot of tools out there, and how you stitch them together is governed by your business and what you need to achieve. The starting point is always a literature review to understand what has worked for similar enterprises, as well as a deep dive into the tools you’re considering and their use cases.
Microsoft has a good repository for data models, plus a lot of literature on best data practices. There are also some great books out there that can help you develop a more strategic, business-minded approach to data architecture.
Prediction Machines by Ajay Agarwal, Joshua Gans, and Avi Goldfarb is ideal for understanding AI at a more foundational level, with functional insights into how to use AI and data to run efficiently. Finally, for more seasoned engineers and technical experts, I recommend Designing Data-Intensive Applications by Martin Kleppmann. This book will give you the very latest thinking in the field, with actionable guidance on how to build data applications, architecture, and strategy.
Several core principles will help you design a data architecture capable of powering AI applications that deliver ROI. Think of the following as compass points to check yourself against whenever you’re building, formatting, and organizing data:
Always have your eye on the business outcome you’re working toward as you build and develop your data architecture is the cardinal rule. In particular, I recommend looking at your company’s near-term goals and aligning your data strategy accordingly. For example, if your business strategy is to achieve $30M in revenues by year-end, figure out how you can use data to drive this.
