data engineering apis

Leverage Interzoid's Standard AI Models to Solve an Important Data Quality Problem: Normalizing and Matching of Company and Organization Names

by Interzoid Team

Normalizing data with AI models

The Challenge

Having inconsistent company or organization name data present within your important data assets can lead to several problems in data-driven applications, processes and initiatives. Here are some examples.

Duplicate data and inaccurate business intelligence:

When the same company or organization is collected and stored under multiple variations of its name, it leads to duplicate records of the same entity within organizational data assets. Inconsistent company names will skew analytics, reports, and dashboards, leading to incorrect insights and potentially flawed decision-making. For example, if a company's sales are split across multiple name variations, the true total sales figure may be underreported, causing lost opportunities for targeted marketing or resource allocation.

Difficulty in data integration and analysis:

Inconsistent naming conventions make it challenging to integrate data from different sources or systems. This can lead to time-consuming manual data cleansing and reconciliation efforts, increasing labor costs and delaying analysis and decision-making processes.

Missed opportunities for Customer Relationship Management (CRM):

When customer data is fragmented due to inconsistent corporate name data, it becomes difficult to gain a comprehensive view of a customer's interactions and history with your organization. This can result in missed opportunities for cross-selling, upselling, or providing personalized services, ultimately impacting customer satisfaction and revenue growth.

Compliance and regulatory issues:

In some industries, inconsistent company name data can lead to compliance and regulatory problems. For example, in financial services, failing to accurately identify and aggregate data related to a single entity may result in non-compliance with anti-money laundering (AML) or know-your-customer (KYC) regulations, leading to potential fines and reputational damage.

Operational inefficiencies:

Inconsistent company names can cause operational inefficiencies in various business processes, such as invoicing, contract management, and vendor relations. These issues can lead to increased manual work, errors, and delays, resulting in higher operational costs, vendor overpayments, and potential missed opportunities for early payment discounts or favorable contract terms.

How Interzoid Can Help

To mitigate these problems, Interzoid has built and refined specialized AI models to identify and cluster instances of inconsistently-represented data. These models have been built over the past several years through several methods. In addition to incorporating Generative AI (to build a problem set-specific language model) and Machine Learning, there are also specialized algorithms and extenstive knowledge bases used in the analysis.

Here is an example of inconsistent data clustering available out-of-the-box using Interzoid's specialized AI models:

Matching company names and organization names examples

These AI models and capabilities can be accessed and leveraged from Interzoid in multiple ways, including via a per-data value API call, an API that analyzes entire datasets (including within database tables), A UI-based wizard on top of these APIs that runs from the Cloud, or alternatively, these capabilities can be installed within your own Cloud infrastructure using AWS and deployed within your Virtual Private Cloud (VPC) infrastructure on EC2 virtual machines, deployable anywhere in the world.

Having clean, standardized, and normalized company/organization name data within your strategic data assets, without any duplication of corporate entities, offers several major benefits:

Enhanced data integrity and reliability:

Standardized and normalized company name data ensures that the information in your database is accurate, consistent, and reliable. This improves the overall quality of your data assets, making them more trustworthy for analysis, decision-making, and reporting purposes.

Improved data integration and interoperability:

Clean and standardized company names facilitate smoother data integration from various sources, both internal and external. This enables better data interoperability across different systems and departments, allowing for more efficient data sharing and collaboration.

Accurate business intelligence and analytics:

With standardized company names, you can perform more accurate data aggregation, analysis, and reporting. This leads to better business intelligence insights, enabling data-driven decision-making and strategic planning based on a clear understanding of your customers, suppliers, and partners.

Effective customer relationship management:

Normalized company name data allows you to create a single, comprehensive view of each customer, regardless of the various touchpoints or systems they interact with. This 360-degree view enables targeted marketing efforts, personalized services, and improved customer experience, ultimately leading to increased customer satisfaction and loyalty.

Operational efficiency and cost savings:

Standardized company names streamline various business processes, such as invoicing, contract management, and vendor relations. This reduces manual effort, minimizes errors, and improves overall operational efficiency. By eliminating duplicate records and entity name inconsistencies, you can also save on storage costs and reduce the time and resources spent on data cleansing and reconciliation.

Better compliance and risk management:

Standardized company name data helps ensure compliance with various regulations, such as AML and KYC requirements in the financial industry. It allows for more accurate identification and monitoring of business entities, reducing the risk of non-compliance and potential legal and financial consequences.

Enhanced data governance and security:

Standardized company names contribute to better data governance practices by ensuring data consistency, accuracy, and completeness. This makes it easier to implement and maintain data security measures, access controls, and data privacy policies across the organization.

Improved supplier and partner management:

Normalized company name data provides a clear view of your suppliers and partners, enabling better relationship management. You can easily identify key suppliers, monitor performance, efficiently review financial transactions, and optimize procurement processes, leading to improved supply chain efficiency and cost savings.

Increased agility and competitiveness:

With clean and standardized company name data, your organization can respond more quickly to market changes, identify new opportunities, and make informed decisions. This agility and data-driven approach can give you a competitive edge in your industry.

Using Interzoid's Cloud-native, AI-powered data quality and data matching capabilities, you can maintain accurate, standardized, and normalized company and organization name data, unlocking data-accelerated benefits and driving significant business value from each of your high quality strategic data assets.

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