This article was first published on Forbes.com on January 3, 2024.

Many forward-thinking companies have been evaluating and/or deploying predictive analytics, generative AI applications and machine learning for some time now. Almost all of them will tell you that success requires well-defined data pipelines, highly enriched and interconnected datasets, and a scalable data platform that is robust enough to power the ever-evolving landscape of AI applications. "Tried and true" data management fundamentals are the difference between AI excellence and "artificial ignorance."

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Building a strong data foundation to accelerate AI

The foundation of any successful AI or large language model (LLM) initiative is a robust, well-integrated and meticulously managed data platform. Auditing, integrating and transforming your data is essential to an effective AI deployment, but this critical preparation step is often overlooked when teams are entranced by the "magic" of model building.

Identify and inventory your organization's data sources

What data sources do you have? Are they located in the cloud or on-premises? What dependencies exist between your data sources, how often are they updated, and how does data flow from system to system? Do you have the proper permissions to access and read these data sources? Here's a four-step guide:

1. Identify data sources. Sources should include databases, file systems, cloud storage, external data sources, APIs and even unstructured data like emails or documents. Ask each department like marketing, sales or engineering about their unique domain-specific data sources.

2. Catalog and classify data. For each data source, document the type of data it contains (e.g., customer information, transactional data or sensor data). Classify the data based on sensitivity, regulatory requirements and business priorities. AI-enabled metadata tools can help tag and organize your data.

3. Assess data quality. Evaluate the quality of the data in terms of accuracy, completeness, consistency, timeliness and reliability. This step is crucial for determining the usability of the data and will help determine the relative priority for each data stream.

4. Document data access and usage. Record how the data is accessed as well as who has access and for what purpose. This helps to understand dependencies and potential bottlenecks.

Integrate all of your data sources into a central repository

Next, you want to bring all of those disparate data sources into a single place so that AI and machine learning apps can use all of your data in context. Every additional data source feeding a central repository adds intelligence to your LLM or machine learning model.

Effective data integration ensures that data is not only centralized but also accurate and up to date. While building custom data movement tools is possible, it can be time-consuming and complex. Prebuilt data integration solutions can often offer advanced features and scalability that save time in deploying AI.

Keeping your data store in sync with incoming data sources is a significant yet critical challenge. Change data capture (CDC) ensures that data is current and accurate. This approach captures and integrates changes made to the data in real time to maintain relevance and accuracy. It's possible to build bespoke data pipelines with CDC capabilities. However, doing so is not trivial even for experienced data teams, and maintenance of custom solutions could become cumbersome over time.

Ensure your data is private, secure and compliant while in motion

Finally, don't neglect the importance of data security across the process. Data in motion is more vulnerable than data at rest. Encryption is paramount, and industries like healthcare with strong data privacy laws need to take extra precautions. Make sure your vendors offer the specific certifications you need (including SOC2, ISO 27001 and HIPAA compliance) along with end-to-end encryption, private networking and local data processing options to enhance your cybersecurity posture and ensure regulatory compliance.

Transform your data to build features and get ready for model training

Once you've safely moved your data into a central repository, transformation is the next crucial step. For LLMs, this might involve identifying relevant text fields and isolating them in a new dataset used for language processing. For machine learning models, this can involve merging complementary datasets, joining tables to produce flat datasets and using creative feature engineering to make model training more effective.

At this step, it's also important to independently validate data quality; when in doubt, leave it out. Throwing more data at your models only helps if that data is reliable. If not, you risk polluting your datasets and reducing the accuracy of the final model.

Be sure to use tools or platforms that enable you to create reproducible templates for data transformation to save time down the road. These templates will prove to be an invaluable part of your burgeoning machine learning operations (MLOps) strategy, as they allow for consistent and efficient processing of data. This reproducibility ensures that as new data is acquired or models are retrained, data flow is still streamlined and reliable.

Effective data access and integration: The linchpin in any successful AI strategy

Building a strong foundation for AI-ready data with data integration best practices can help guarantee that your AI models have the most accurate and timely data available to deliver relevant results. By quickly unifying your most valuable data sources, sidestepping the temptation to reinvent the wheel and focusing on the most important challenges, effective data integration is the fast track to AI that adds value and drives a long-term competitive advantage.

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