Data Center

NVIDIA GTC 2024: 5 Ways to Prepare Your Data Before Adding AI Tools

At this year’s conference, Jill Klein, of CDW, says that unlocking the full potential of artificial intelligence means preparing data first.

Lily Lopate

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Lily is a Senior Editor at BizTech Magazine. She follows tech trends, thought leadership and data analytics.

Recently, conversations about artificial intelligence have stirred a sense of urgency, curiosity and excitement. And though AI is not a silver bullet to solve all problems, graphics processing units and high-performance computing are pushing businesses toward the future, ready or not. The technology is encouraging them to move faster, make data-driven decisions and innovate all at once.

At this year’s NVIDIA GTC 2024, hosted in San Jose, Calif., thousands of experts, software developers and industry leaders gathered to discuss the state of AI. For Jill Klein, head of Internet of Things (IoT) and emerging technology at CDW, preparing your data and managing it well is the best way to ensure AI success. In fact, this is a key step that many organizations overlook in their rush to use AI.

She explains that without visibility into your data ecosystem, AI’s impact is limited. One of the reasons it’s crucial to “establish a solid foundation in data management,” Klein says, is that the better your inputs are, the better your outputs will be.

On the expo floor of NVIDIA GTC, surrounded by a swirl of caffeinated attendees and one robotic dog, Klein told BizTech Senior Editor Lily Lopate about five ways that IT leaders can prepare their data ahead of deploying AI and machine learning tools.

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BIZTECH: You mention the importance of building a solid foundation in data management before layering on AI/ML tools. Could you describe this foundation?

Klein: This foundation is composed of modern data platforms and robust data governance practices. In addition, fostering a culture of data literacy and accountability across the organization is vital. Employees should be trained to understand the importance of data management and their role in maintaining data integrity.

BIZTECH: What steps should IT leaders take to manage their data effectively?

Klein: There are five steps that IT leaders should take.

Step 1: Run a Data Quality Assessment

Before diving into AI/ML or IoT initiatives, organizations must conduct a thorough assessment of their data quality. This involves identifying and rectifying any inconsistencies, inaccuracies or incompleteness in the data. Implementing data quality frameworks ensures that the data used for AI/ML models is reliable and trustworthy. This is essential to future success.

By investing in these foundational elements, organizations can unlock the full potential of their data assets and drive meaningful insights.”

Jill Klein head of Internet of Things and emerging technology, CDW

Step 2: Establish Data Governance Frameworks

Establishing a robust data governance framework is essential for ensuring compliance, security and accountability in data management. This framework should outline policies, procedures and responsibilities related to data collection, storage, usage and access. Effective data governance mitigates risks associated with data misuse and ensures ethical and legal compliance. Regular audits and assessments can help identify areas for improvement and ensure adherence to data governance policies.

Step 3: Invest in Modern Data Platforms

Organizations must invest in modern data platforms, such as data lakes or data warehouses, that are capable of handling large volumes of data efficiently. These platforms should support various data types, including structured, semi-structured and unstructured data, and enable seamless integration with other systems and tools. Organizations often have data dispersed across various systems and departments. Before deploying AI/ML tools, it’s crucial to integrate and centralize data from disparate sources into a unified repository.

Jill Klein, head of Internet of Things and emerging technology at CDW shares 5 steps to preparing your data at NVIDIA GTC 2024.

Step 4: Build Scalable Infrastructure and Architecture

AI/ML applications require scalable infrastructure capable of handling large volumes of data and complex computational tasks. Organizations must evaluate their existing IT infrastructure and ensure scalability to accommodate future growth in data volume and computational demands. For example, cloud-based solutions offer scalability, flexibility and cost-effectiveness for deploying AI/ML workloads.

Step 5: Data Privacy and Security Measures

Safeguarding sensitive data from unauthorized users is of the utmost importance. Before integrating AI/ML and IoT tools, organizations should establish stringent data privacy and security protocols. This entails employing encryption, access controls and anonymization techniques and adhering to regulations like the General Data Protection Regulation, the California Consumer Privacy Act and HIPAA. By prioritizing data privacy and security, organizations not only foster trust among customers and stakeholders but also mitigate potential liabilities.

BIZTECH: What do organizations gain by following these steps?

Klein: Ultimately, these data management and governance practices lay the groundwork for successful implementation of AI/ML and IoT tools. By investing in these foundational elements, organizations can unlock the full potential of their data assets and drive meaningful insights to support strategic decision-making.

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