Why Does Successful AI Require The Right Data Architecture | 7wData

Artificial Intelligence promises cost savings, a competitive edge, and a foothold into the future for the Business. While AI adoption is on the rise the level of investment is often not in line with the monetary returns. The right Data Architecture is essential to AI success. This article will show you how.

Only 26% of AI projects are currently being implemented in widespread production within an organization. This means that many companies spend a lot on AI deployments and don’t see any tangible ROI.

In a world in which every company must be a tech company, there is increasing pressure on IT leaders and technical teams to harness data to drive commercial growth. Businesses are eager to maximize the ROI and improve efficiency from expensive data, especially as cloud storage costs rise. Unfortunately, they don’t have the luxury to store all their data.

This demand for quick results means that mapping Data Architecture cannot be extended for months without a defined goal. However, focusing only on standard data cleaning and Business Intelligence reporting (BI) is regressive.

They’ll have to retrofit it later if they do not. Data architecture in today’s business should be driven toward a specific outcome. This outcome should include AI applications that have clear benefits for end-users. This is crucial to setting up your business for success in the future, even if it’s not ready for AI.

Data Architecture requires knowledge. There are many tools available, but how to put them together depends on your business and the goals you have. It is a good idea to start by reading about similar businesses and then dive deeper into the tool you are considering as well as their uses.

Microsoft provides a great repository of data models and literature on data best practices. You can also find great books that will help you to develop a business-oriented approach to data architecture.

Prediction machines Joshua Gans, Avi Goldfarb, and Ajay Agarwal are a great way to understand AI at a deeper level. It provides functional insights into AI and data usage to make it run more efficiently. For more experienced engineers and technical professionals, I recommend Designing Data-Intensive Appsby Martin Kleppmann. This book provides the most current thinking and practical guidance on building data applications, strategy, and architecture.

A few core principles will guide you in designing a data architecture that can power AI applications that generate ROI. These principles can be used as a guideline to help you when building, formatting, or organizing data.

As you develop and implement your data architecture, it is important to keep your eyes on the business outcome. Particularly, I recommend that you look at the near-term goals of your company and align your data strategy accordingly.

If your goal is to reach $30 million in revenue by the end of the year, you should look at how data can be used to help you achieve this. You don’t need to make it difficult. Break down the most important goal into smaller goals and work towards them.

Although it is important to have a clear goal, the solution must be flexible enough to change with business needs. You should consider the possibility that small-scale projects could grow into multi-channel projects. Fixed rules and modeling will only lead to more work.