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The Coming Divide in Defense: Federation or Fallout Under DoDI 5000.97

Share: The defense industry is entering a pivotal phase of transformation. With the release of DoDI 5000.97, the U.S. Department of Defense (DoD) has made

Concurrent data collection and normalization for AI applications

Efficient data normalization is the backbone of successful AI applications, but traditional, linear approaches often introduce delays. By leveraging concurrent data collection and real-time normalization through integration, businesses can ensure AI models operate with clean, standardized data from day one.

September 26, 2024
Prakash Tiwary

Introduction

In the world of AI and machine learning, the importance of clean, normalized data cannot be overstated. Data normalization ensures consistency across datasets, enabling AI models to learn effectively. However, as organizations grow, and data sources become more diverse, normalizing data efficiently and at scale becomes increasingly complex. For businesses looking to optimize their AI models, solving the challenges of data normalization is a critical step toward unlocking valuable insights.

At least 30% of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value, according to Gartner, Inc.

Current Challenges of Data Normalization in AI

AI systems rely on data from multiple sources, often formatted differently, which makes normalization difficult. Some common challenges include:

These issues can lead to:

The complexity increases with real-time data, where processing speed and data consistency need to be balanced.
Traditional normalization often follows a linear process, occurring only after data collection. This causes delays, as data must be collected and stored before being normalized, leading to slower AI model deployments and inefficiencies.

Addressing Data Normalization Challenges Through Data Integration Solutions

Data integration solutions offer a powerful way to overcome the challenges associated with data normalization in AI. By automating and streamlining the process of consolidating data from multiple systems, these solutions address a variety of data-related issues.

Below are key challenges that data integration platforms help resolve, with practical examples from organization using ALM (Application Lifecycle Management), CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), and ITSM (IT Service Management) systems.

Data Standardization

In a CRM system, as customer data is being entered from various sources, a data integration platform can automatically standardize formats (e.g., phone numbers, addresses) in real-time, ensuring clean, normalized data is immediately ready for use.

Diverse Data Formats

A data integration platform can standardize data from multiple formats—such as date formats from CRM systems or financial records from ERP systems—automatically, ensuring consistency across all datasets.

Handling Inconsistent or Missing Data

Integration solutions can intelligently fill missing data and resolve inconsistencies, allowing smoother AI operations. In an ALM system, development timelines may have inconsistent date formats or missing values. A data integration solution can flag missing data and apply intelligent imputation methods (such as filling in with averages) to ensure consistency.

Resolving Duplicate Records

Data integration helps match and merge duplicate entries across systems like CRM , ALM and ERP, consolidating redundant records into a single source of truth.

Example: A CRM system may contain duplicate customer records from different departments (sales, support). Data integration can match and merge duplicates into a single record, eliminating redundancy before AI model training.

Security and Privacy Compliance

Automation in data integration enables the seamless application of security standards, masking sensitive data (e.g., PII, employee salaries) as the data is collected.

Real-Time Normalization

Data integration platforms ensure that data is normalized as it flows from the source to the system in real-time, minimizing latency and improving system responsiveness for AI tasks.

Handling Evolving Schemas and versions

Systems often update or change schemas (e.g., adding new fields, renaming columns), which can disrupt the normalization process.

Example: A CRM system might introduce a new version with updated customer engagement metrics. A data integration platform automatically adapts to schema changes, ensuring that new fields are properly integrated into the normalized dataset.

How OpsHub’s Enterprise-Grade Data Integration Solution Can Help

OpsHub offers a comprehensive data integration platform that simplifies data normalization at scale. By automating the flow of data across various systems, OpsHub ensures that data is consistent, normalized, and accessible in real-time for AI applications. Its ability to integrate diverse data sources and apply normalization during the data collection process eliminates linear bottlenecks and ensures efficiency.
Additionally, with OpsHub’s history fetching capabilities, customers can immediately leverage their historical data alongside real-time inputs. This means AI models can start delivering insights right from day one, maximizing the ROI from AI investments as data normalization and AI training begin simultaneously. By streamlining the integration and normalization of historical and real-time data, OpsHub accelerates the value derived from AI-driven decision-making

Conclusion

Data normalization is critical for successful AI deployment, but traditional methods often introduce delays and inefficiencies. By leveraging OpsHub’s data integration solutions, organizations can simultaneously collect and normalize data, addressing key challenges and ensuring that AI models operate with high-quality, standardized data.

Experience seamless integration & eliminate data silos with OIM

Prakash Tiwary

Prakash Tiwary is the Senior Director of Engineering at OpsHub, bringing over two decades of expertise in Product Development, Engineering Management, and Data Analytics. He has led innovation teams to create cutting-edge, market-leading products, and his passion for solving complex software challenges is central to accelerating OpsHub’s capabilities and solutions.

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Concurrent data collection and normalization for AI applications

Introduction

Current Challenges of Data Normalization in AI

Addressing Data Normalization Challenges Through Data Integration Solutions

Data Standardization

Diverse Data Formats

Handling Inconsistent or Missing Data

Resolving Duplicate Records

Security and Privacy Compliance

Real-Time Normalization

Handling Evolving Schemas and versions

How OpsHub’s Enterprise-Grade Data Integration Solution Can Help

Conclusion

Table of Contents

Prakash Tiwary

Curious to learn how OpsHub’s data integration solution can accelerate your AI adoption?