Leveling Up Organizational Data for AI Readiness

Brian Henn
Blog Post
May 6, 2024
Leveling Up Organizational Data for AI Readiness
AI & Data

Artificial intelligence is a transformative force across industries as it revolutionizes how businesses operate, make decisions and serve customers.  

But the success of any organizational AI project is hugely dependent on the quality and readiness of the data required to supplement the AI initiative. To ensure that the data is reliable and prepared to support AI projects, organizations should follow these crucial steps to ensure their AI-driven success.

1. Define Clear Objectives
Before organizing and preparing their data, it is essential to clearly define organizational objectives. Typically, this process begins by establishing a shared vision of what AI means to the organization, especially with many companies and industry experts having varying definitions of what AI means to them. A clear understanding of these objectives will help inform data transformation and ingestion patterns, as well as data collection and model development processes.

2. Establish Data Governance Frameworks
Implementing a robust data governance framework ensures that data is managed, stored and accessed in a compliant and secure manner. This often includes defining data ownership, access controls and regulatory compliance measures like GDPR and HIPAA. Data governance is branded in many ways but recognizing it is an ongoing and evolving program is imperative to driving an organization forward. Organizations with an existing data governance program are considered “ahead of the curve,” and are effectively more prepared to take on new AI initiatives.

3. Data Integration and Virtualization
Integrating data from disparate internal and external sources and harmonizing them into a unified format is crucial in developing a framework to gather insights and value from organizational data. Once data is clean and integrated, data virtualization tools like Denodo are leveraged to provide a holistic, stakeholder-friendly view of an organization's core business systems and data elements.

4. Feature Engineering
Feature engineering plays a critical role in extracting meaningful insights from raw data. Relevant features contributing to the predictive power of AI models should be identified and engineered accordingly. Techniques such as dimensionality reduction, feature scaling and transformation can enhance model performance. Typically, this type of feature engineering occurs within the bronze (raw) and silver (integrated) layers of a cloud-based medallion data architecture, with the audience primarily consisting of data engineers, data scientists and analysts.

5. Data Privacy and Security
Alongside data governance, safeguarding all sensitive organizational data through anonymization, masking, encryption and access controls is crucial to maintaining privacy and compliance with regulatory requirements. Establishing protocols for responsible data sharing and collaborating with stakeholders through policies and learning ensures that data security remains a top priority. Failure to do so could result in costly data breaches, brand and reputational damage or legal ramifications for the organization.  

6. Continuous Monitoring and Iteration
Data readiness should be an ongoing process, not a one-time effort. Prioritizing the implementation of mechanisms for continuous monitoring of data quality, model performance and evolving business requirements in all AI projects is essential to their success. Iterating on data preparation pipelines and model architectures to adapt to evolving conditions will foster a data culture within the organization where data is trusted to feed AI initiatives and inform confident organizational decision-making.

7. Documentation and Transparency
Comprehensively documenting all steps of the data preparation process to ensure transparency, reproducibility and lineage is another vital aspect of running a successful AI project. Maintaining clear documentation of data sources, preprocessing steps and model configurations facilitates collaboration and knowledge sharing within the organization, helping establish a foundation of trust and transparency.

8. Establishing a Positive Data Culture
Encouraging collaboration among data scientists, business-focused domain experts and IT professionals is vital to leveraging their diverse perspectives and domain knowledge. Cross-functional teams can facilitate smoother data preparation processes and ensure alignment with business objectives by offering varied and unique perspectives on the problems to be solved. When working as a collective, they can also uncover additional value-based AI use cases with clearly stated outcomes. This concept is commonly referred to as data democratization.

Ensuring organizational data readiness is a pivotal prerequisite to the success of any AI project. By following these impactful steps and adopting a holistic approach to data preparation, organizations can unlock the full potential of AI technologies to drive meaningful business outcomes. It is essential to always remember that the journey toward AI excellence begins with high-quality, well-prepared data.

At BUILT, to set organizations on the path to AI-preparedness, we employ a thorough and technical deep-dive assessment of existing organizational data quality that involves identifying any inconsistencies, errors, missing values or outliers in datasets. BUILT’s Data Level Up program identifies contextual inconsistencies across data assets, including database schemas, tables and legacy reporting environments. We then build a detailed, executable roadmap for maturing in key areas that will grow the data program to enable the effective use of AI.

If you are ready to level up your organizational data to ensure AI-preparedness, contact us today: www.builtglobal.com/contact.

About the author, Brian Henn, Partner at BUILT and Head of Solution Delivery: Brian is a proven IT leader with a broad depth of experience serving clients across many industry verticals. Focused most recently on banking and retail clients, helping them to think in new and innovative ways to allow data, specifically strategic data management initiatives and architecture, to become a trusted, secure, and monetized asset for their organization.

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