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7 Ways To Build a Sophisticated AI Data Pipeline

To help businesses build a sophisticated AI Data Pipeline, we asked tech professionals and business experts this question for their best advice. From establishing an effective data preparation structure to creating well-structured datasets, there are several options that may help you to build a sophisticated AI Data Pipeline.

Here are seven ways to build a sophisticated AI Data Pipeline: 


Establish an Efficient Data Preparation Structure

The ability of organizations to build sophisticated AI data pipelines depends on the way they handle their data preparation process. Data preparation, which involves processing raw data, is usually the first step towards successful machine learning in any system. To support ingestion and machine learning in an organization, there must be an efficient method of data gathering, data analysis, data harmonization, and data engineering which will help in creating smart algorithms for effective machine learning.

Nonyerem Ibiam, Law Truly

Web to Print Portals

I believe that AI can be integrated into any business to help streamline your processes and provide your customers with a simple and easy experience. In my company, we accomplish this through our seamless web to print portals. This option is perfect for my clients that need a simple and intuitive way to order and distribute consistent marketing materials across multiple locations.

Eric Blumenthal, The Print Authority

Manage Your Applicant Pool

63% of business technology decision-makers are implementing, have implemented, or are expanding the use of AI. At Charter Capital, utilizing AI is important to manage our applicant pipeline and support ingestion and organization into our applicant process. 

Carey Wilbur, Charter Capital 

Evaluate Your Business Processes

The best course of action when looking to build an AI data pipeline is to first evaluate your business. What do your processes look like? Are there steps that can be automated? Will the cost of implementing a tool like this generate a positive ROI for you down the road? Depending on the answers to these questions, you can determine whether implementing AI is the answer to your problems, and if so, what is the most cost-effective way to do so is.

Megan Chiamos, 365 Cannabis 

Well-Structured Datasets

AI data pipelines are complex and confusing. It’s not like a linear or fixed method, where datasets flow nicely into analytics. The solution for a data flowing AI pipeline is the same as you’d have for any pipeline system: datasets just need to be well-structured. When datasets have the structure needed to support rapidly accelerating AI, better decisions can be faster, and with improved information. 

Brett Farmiloe, Markitors

Make It Exist, Then Make It Better

My recommendation in approaching a data project is to first make it exist, then make it better. With data, you can invest hundreds or even thousands of hours designing what may seem like a perfect system. By the time you are complete, the market or other parameters may have shifted to make your data less relevant or even obsolete. Instead, get started with a very basic system. For example, you could use a tool like Zapier to stitch together data sources and run basic analyses. Then, once you have a data pipeline in place, build it out from there.

Michael Alexis, Teambuilding

Hire a Consultant and Focus on Your DataOps

Building an AI data pipeline is no easy task, and it works best if you hire a consultant and/or contract with a large cloud provider that can provide their third-party expertise. That being said, when it comes to AI, you get out what you put in. If you’re training the AI with bad quality data, you’re going to get bad decisions and predictions. You need to have a way to collect and cleanse the data before using it to train your AI. Focusing on your DataOps before building your pipeline is crucial to successfully implementing an AI data pipeline.

Jenn Fulmer, TechnologyAdvice

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