Why Does Successful AI Require The Right Data Architecture

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.

Every Company Should Perform Like A Technology Company

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.

Technology Leaders Must Put AI and Data Architecture at The Forefront of Their Goals.

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.

Start from Scratch? Start With The Best Data Practices

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.

Three Essentials for a Success Data 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.

• Building Working Towards an Objective:

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.

• Designing to Create Rapid Value:

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.

Any architecture that you design should be able to accommodate more data as it becomes available, and leverage that data towards your company’s current goals. Automating as much as possible is also a recommendation. This will allow you to make a significant business impact on your data strategy quickly and over time.

If you know that you will need to provide monthly reporting, automate the process. This will ensure that you only spend your time on it for the first month. The impact will continue to be positive and efficient after that.

• How to Test for Success:

It is important to assess the effectiveness of your data architecture in order to keep you on track. Data architecture is effective when it (1) supports AI and (2) delivers usable, relevant information to all employees in the company. These guidelines will ensure that your data strategy is both fit for purpose and ready for the future.

Future of Data Architecture: What Innovations Can You Learn About

These key principles can be a great starting point for technical leaders and teams. However, it is important to not get stuck in one way. Businesses risk losing out on opportunities that could provide even greater value over the long term if they do not. Tech leaders need to be aware of the latest technologies that are available and how they can improve their work and provide better results for their businesses.

• Cheaper Processing:

Already, we are seeing improvements that make processing more efficient and less costly. This is crucial because advanced technologies require high levels of computing power. One example is neural networks. As computer power increases, so will our ability to solve problems in more complex ways.

A data scientist, for example, must train each machine learning model. In the future, models will be able to train other models. This is a theoretical idea, but it’s likely that we will see more innovation as processing power becomes easier.

• Bundled tools:

We are currently in a phase where the majority of technology can only do one thing well when it comes to apps and software that can reduce time to value for AI. All the tools required to produce AI, such as storage, machine learning providers, and API deployment, are not bundled.

Businesses risk wasting valuable time trying to figure out which tools they require and how to combine them. Technology is slowly emerging that can solve multiple data architecture problems as well as databases that specialize in powering AI applications.

Businesses will be able to put AI into production quicker by combining more offerings. Similar to the fintech industry, it’s similar. In the beginning, companies were focused on being the best in a single core competency. Then they merged to create bundled solutions.

• Data Marts vs. Data Warehouses:

It seems that data lakes will be the most important AI/data stack investment for any organization in the future. Data lakes will allow organizations to understand how best to execute their predictions. Data marts will be more valuable in the future, according to me.

Marts provide the same data to all teams in a company in a format that they understand. Marketing and Finance, for example, see the same data in familiar metrics and can use them. Data marts of the future will be more than just dimensions, facts, and hierarchy. They will not only be used for slicing and dice information but also support decision-making within specific departments.

Conclusion

Businesses must keep up with technology developments. Otherwise, they will be left behind. This means that tech leaders must stay connected with their teams and allow them to bring new ideas to the table.

It’s important to take the time to learn, experiment, and invent, even as your company’s data architecture and AI apps become more robust.