Is Your Structured Data Ready for Analytics & Gen AI?
In 2024, both analytics and generative AI (GenAI) adoption reached unprecedented levels. As businesses integrate these insight-generating technologies into daily operations, the spotlight shifts to what drives their success: data. But not just any data. Success depends on structured, analytical- and AI-ready data.
Despite the surge in data analytics and GenAI adoption, many companies struggle to create effective data strategies that truly leverage their capabilities. The result? A sea of data but a shortage of actionable insights. Structured data holds the key to unlocking business value, enabling precise insights and driving revenue. This article explores the pivotal role of structured data in the success of data analytics and GenAI initiatives and outlines actionable steps to prepare your data.
Why Structured Data is Crucial for Analytics & GenAI
Structured data, typically stored in organized formats like tables or databases, is often easier to search and analyze than unstructured data (e.g., text or audio files). However, structured data presents its own challenges. While unstructured data dominates discussions about data analytics and AI hurdles, structured data remains the backbone of decision-making. Here’s why:
Decision-Making Power: Unlike unstructured data, structured data is critical for generating precise insights. Without proper preparation, even structured data can lack the context needed by analytics and GenAI, leading to skewed analyses and user distrust.
Risk of Misinterpretation: Poorly prepared structured data can result in inaccurate predictions and missed opportunities. GenAI and analytic application models require structured data that is contextually rich and semantically aligned to provide actionable insights.
Unlocking Potential: Properly prepared structured data transforms analytics and GenAI into reliable decision-making tools, enabling organizations to generate trusted insights.
Challenges in Preparing Structured Data for AI
Structured data must be more than neatly arranged to be analytics and AI-ready—it must be contextualized, labeled, and consistently managed. Here are the primary challenges businesses face:
Semantic Gaps: Structured data often lacks semantic meaning, making it difficult for analytics and GenAI to understand relationships and context. Without enrichment, GenAI risks producing nonsensical or skewed outputs, a phenomenon known as AI hallucinations. And users of analytical tools are often stopped dead in their tracks because the data isn’t in terms they understand.
Data Silos: Structured data often exists in isolated systems, limiting access by analytics tools and GenAI to a holistic view of the business. This lack of centralized, unified data hampers the generation of accurate insights, reduces data quality, and undermines the consistency needed for reliable GenAI models.
Inconsistent Data Quality: Duplicate entries, inconsistent labels, and poorly maintained data undermine the reliability of data analysis and GenAI output. Clean, accurate data is a necessity to deliver meaningful insights.
Addressing these challenges is essential for leveraging structured data effectively and achieving both analytics and AI readiness.
Simplifying Structured Data for Analytics and Generative AI with a Data Hub
Accessing information from ERP systems and other business platforms is possible using modern reporting tools, but the complex data structures of these systems often create barriers. Users may struggle to locate or understand the data they need. Moreover, essential data for analysts often extends beyond transactional systems. This includes plans, budgets, forecasts, and third-party data like demographics and point-of-sale information contributed by employees, suppliers, and customers.
A modern data hub, such as the one in the Silvon Stratum analytics solution, addresses these challenges by organizing data with well-defined values and dimensions. This ensures consistent reporting and interpretation. For example, any report or dashboard referencing “total revenue by month” uses the same definition. This standardization supports accurate and consistent calculations of higher-level concepts like KPIs.
In addition, a data hub enriches data for better analytics and AI applications by integrating supplementary information not found in ERP or other core systems. This approach enhances data consistency and trustworthiness, which is critical for departments like sales, finance, and operations that often rely on disparate spreadsheets, business applications, and other data sources.
Before implementing a data hub as part of an analytics or GenAI initiative, follow these steps to prepare effectively:
Step 1: Ensure Comprehensive Data Collection
The data model underpinning an analytical or GenAI project provides a structured framework for reporting and analysis. To build a robust data model, ensure you account for the following components:
- Data Sources: Data Sources include transactional system databases, spreadsheets and web services, to create a unified view of your business information.
- Tables and Entities: Data should be organized into tables or entities, representing specific business aspects like customers, products, or transactions, with clearly defined attributes or fields.
- Relationships: Relationships should be defined between tables to enable data joins and aggregations for answering business questions.
- Attributes and Measures: Differentiations should be made between descriptive elements (e.g., customer names) and numerical data (e.g., sales revenue) that can be aggregated.
- Calculations: Calculated fields or metrics will be needed to derive insights using mathematical operations or custom expressions.
- Hierarchies: Hierarchical structures should be included to reflect relationships, such as product categories or organizational levels.
- Security: Security measures should be incorporated to control access and protect data integrity.
Step 2: Validate and Correct Data Against Source Systems
Data validation is a critical step in preparing data for analytics and GenAI. During this phase, your analytics solution provider or internal resource should assist with:
- Loading Data: Import data into the solution’s repository.
- Automating Updates: Set up a nightly data refresh to maintain accuracy without full reloads.
- Enabling Validation Access: Provide access to key team members for validating measures, dimensions, and master data.
- Offering Validation Resources: Use tools like customized validation views, instructional guides, and task plans to streamline the process. For example, Silvon provides tailored resources to help customers ensure accurate validation of data imported into the data hub of our analysis and reporting solution.
After the initial validation, establish a long-term strategy:
- Assign Ownership: Designate individuals responsible for ongoing data validation.
- Select Validation Resources: Use systems-generated reports or spreadsheets containing accurate, up-to-date data to simplify validation.
- Address Inaccuracies: Correct errors in the original data source to maintain consistency and trust.
By following these steps, a modern data hub can empower organizations to harness structured data effectively, enabling precise analytics and AI-driven insights that support informed decision-making across departments.
Obstacles To Avoid
Not Accounting for Evolving Trends
Data validation must also account for changing trends and evolving datasets. Processes that treat old and new data uniformly risk obscuring important differences. For example, new customer data might include metadata or features absent from older records. Failing to recognize these differences can result in models that overlook emerging patterns.
No Defined Iterative and Collaborative Cleaning Strategy
Rather than undertaking massive data validation efforts upfront, organizations should adopt an incremental approach. By starting with small, trusted datasets and expanding gradually, teams can identify and address issues as they arise. This iterative process allows for adjustments based on real-world performance and feedback.
Collaboration between data scientists, domain experts, and business stakeholders is essential to align cleaning efforts with organizational priorities. Documenting decisions about cleaning practices and their impact on models helps maintain transparency and accountability.
Conclusion
While data quality is fundamental to successful AI projects, excessive cleaning can undermine data’s utility and compromise model performance. Effective data preparation involves striking a balance between cleanliness and context, focusing on use-case-specific requirements, and adopting iterative, collaborative validation strategies. By carefully managing data preparation efforts, organizations can harness the full potential of their data to drive impactful analytic and GenAI outcomes.
To learn more about Silvon’s data hub, visit www.silvon.com/DataHub.php