Navigating the Complexity of Cross-Platform Data Standardization in a Global Economy
Explore the benefits, challenges, and solutions for data standardization at a global enterprise.
Vishnu Arunachalam
Director of Engineering, BaseCap Analytics
Featuring guest contributor, Mark Dangelo.
Mark Dangelo currently serves as an independent design and innovation advisor to banking, mortgage, insurance, healthcare, private equity, and miscellaneous VC funded firms developing data meshes/fabrics, stacked/ensemble AI, MAD data sciences, ethical data governance, and decentralized finance.
Visit Mark’s LinkedIn page and read his articles in Mortgage Bankers Association.
Data has become a critical asset in today’s global economy. As organizations expand across borders, the need for standardized data across platforms becomes increasingly apparent.
Cross-platform data standardization is essential for driving efficiency, improving decision-making, and enhancing collaboration on a global scale.
However, the path to standardization comes with complexities. In this article, we’ll explore the benefits, challenges, and solutions for data standardization at a global enterprise.
Companies engaged in or preparing for a merger or acquisition use data validation to improve their valuation.
What is data standardization?
Data standardization refers to the process of ensuring that data from different sources is consistent, structured, and interpretable by different systems and platforms. By using common standards and formats, data can be seamlessly shared and analyzed, ensuring that businesses and organizations can make informed decisions with reliable, high-quality data.
Data standardization refers to the process of ensuring that data from different sources is consistent, structured, and interpretable by different systems and platforms.
Standardizing data helps to overcome differences in format, quality, and structure, which can break down essential processes. For example, a mortgage servicer that boards new loan files—including payment letters, notes, and tax documents—may use a different structure in their system of record than the mortgage originator that sent the data. Often, teams of analysts must manually decode and restructure the data before they can board the new files.
"Today’s web of systems, infrastructure, and data sources are so vast that without growing automation of data governance, IT methods and practices become last-gen inhibitors to continued success."
Now, imagine that data comes in a different language! These simple issues can quickly escalate into operational crises.
How Data Standardization Can Benefit Today’s Global Economy
The global economy thrives on data. Standardized data helps organizations manage operations across multiple countries and industries, driving efficiency and innovation.
Here’s how data standardization benefits the global economy:
Enhanced Global Trade
Standardized data facilitates smoother trade between countries. By using universally accepted formats, customs agencies and businesses can process import and export documents faster, reducing delays and errors. In addition, standardized data helps companies comply with international trade regulations more easily, streamlining global transactions.
Improved Supply Chain Management
Supply chains today are complex, involving numerous stakeholders across different regions. Data standardization ensures that all participants—from suppliers to manufacturers to retailers—can communicate effectively. With standardized data, companies can track products in real-time, optimize inventory levels, and predict potential disruptions before they occur.
Increased Collaboration and Innovation
When data is standardized, it can be shared more freely between organizations. This opens the door for greater collaboration, leading to innovation in industries such as healthcare, finance, and technology. Standardized data also helps in benchmarking best practices across sectors, fostering a culture of continuous improvement.
Better Decision Making
Data standardization improves the accuracy and reliability of the information that organizations rely on to make decisions. Decision-makers are empowered with cleaner, more consistent data, allowing them to identify trends, optimize operations, and anticipate market shifts.
Strengthened Economic Resilience
A standardized approach to data helps economies better withstand disruptions. For example, during the COVID-19 pandemic, standardized health data allowed governments and businesses to react quickly and coordinate responses. This agility helps countries and businesses remain resilient in the face of future global challenges.
Challenges of Cross-Platform Data Standardization
Despite the benefits, achieving cross-platform data standardization isn’t without its challenges. Organizations must navigate technical, organizational, and quality-related issues to ensure the success of their data standardization efforts.
Data Format Incompatibility
Different systems and platforms often use unique data formats, making integration difficult. One CRM may export CSV files, while another system it connects to only processes XML. When data is shared between these systems, it can result in errors, delays, or misinterpretations. This lack of compatibility slows down operations and may cause costly disruptions.
Data Quality Issues
The value of standardized data depends heavily on data quality. Poor-quality data, such as incomplete, outdated, or incorrect information, can lead to misguided decisions. Organizations must address data quality issues before standardization can be fully effective.
Technical Limitations
Legacy systems often pose technical limitations to data standardization. Older platforms may not support modern data formats or integration technologies, complicating the process of ensuring data consistency across systems.
"With the explosion of data and the painful realities of many cross-purpose types (e.g., structured, unstructured, semi-structed, synthetic), the use and purpose of data standards has radically shifted in the use stories, data diagrams, and use cases that once drove enterprise initiatives."
The enterprise approach to data standardization is changing as technology stacks evolve and ever more data systems must plug into each other.
Solutions and Strategies
To overcome these challenges, businesses and organizations can adopt several strategies for successful cross-platform data standardization.
Adoption of Industry Standards
Industry-specific standards are key to ensuring data uniformity. By adopting globally recognized standards such as ISO, businesses can ensure that their data is compatible with international norms. This helps streamline communication, compliance, and reporting across borders.
Data Integration Tools and Platforms
Modern data integration tools allow organizations to bridge gaps between incompatible systems. These platforms enable seamless data sharing, ensuring that information remains consistent and accurate across multiple platforms.
Data Governance Frameworks
A strong data governance framework ensures that data is managed effectively and standardized across an organization. By implementing clear policies, roles, and processes, businesses can maintain data quality and consistency over time.
Cloud-Based Solutions
Cloud computing provides a scalable and flexible solution for data standardization. With cloud-based platforms, organizations can store, process, and share data in real time, ensuring that all stakeholders have access to up-to-date, standardized information.
Artificial Intelligence and Machine Learning
AI and machine learning algorithms can automatically detect discrepancies in data and recommend ways to standardize it. These technologies enhance the speed and accuracy of data processing, making it easier for organizations to manage complex data sets.
"Machine learning and AI are data-driven solutions that are directly impacted by the quality and quantity of multi-platform data repositories (e.g., system, data warehouse, lake, mart). These repositories link together to offer new insights and predictions that continue to captative the imagination of enterprise leadership."
Regular Audits and Continuous Improvement
Organizations should regularly audit their data and data management processes to identify areas for improvement. Continuous monitoring ensures that data remains standardized and aligned with industry best practices. This process helps companies adapt to new data challenges as they arise.
Steps to Standardize Data
Data standardization is a multi-step process that involves the careful preparation, cleaning, and transformation of data to ensure it meets industry standards. While these steps are typically performed manually, with professionals staring and comparing data sets to correct issue, new technology deploys automation to speed up the standardization process.
Data Collection and Preparation
The first step in data standardization is gathering all relevant data from various sources. Data should be collected in a structured and organized manner, ensuring it is ready for the standardization process.
Data Cleaning
Data cleaning involves identifying and correcting errors, inconsistencies, and duplications in the collected data. Clean data is essential for accurate analysis and decision-making.
Data Transformation
Data transformation converts data into a standardized format that can be easily shared and interpreted. This may involve converting file formats, standardizing units of measurement, or normalizing data structures.
For example, BaseCap, an automated data validation platform, features a “code break” feature. This is helpful for when a third-party data provider uses unique terminology, short-hand, or acronyms. The code-break function helps analysts easily map “coded” words to their own nomenclature for rapid reconciliation.
Data Validation
Before data can be fully standardized, it must be validated to ensure accuracy and completeness. This step ensures that the data is fit for its intended purpose and meets the required quality standards.
"What leaders fail to understand and embrace is that the expectations of standards in 2025-2028 success factors are not as adherence or conformance, but as a building block of a comprehensive data design."
Embracing the Future of Data Standardization as an Economic Tool
As the global economy becomes more interconnected, data standardization will play an increasingly important role. By addressing the challenges and implementing effective strategies, organizations can unlock the full potential of their data and drive innovation, efficiency, and resilience in the global market.
Cross-platform data standardization isn’t just a technical challenge—it’s a vital economic tool for the future.
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Ask Vishnu!
Director of Engineering at BaseCap Analytics
Data can be standardized in any number of ways, from standardizing units to min-max scaling. At BaseCap, we talk about standardization through the lens of files and spreadsheets. Are all the column names mapped correctly? Does one system know how to read the data in another system? These are the questions our platform answers by automating the typical process of data standardization.
Let’s say your planning a trip to the Bahamas, and you’ve created a detailed spreadsheet about all of the locations you want to visit, what to do there, how much it costs, and other important data. But you’re friend is also a planner! They’ve created a similar document but it’s in a table in Microsoft word, and they have some of the same columns, but some different columns, and they’ve used different words like “pricing” instead of “cost,” and “activities” instead of “things to do.” A data standardization tool would help you transform your friend’s data table to make it compatible with yours. Now, you can plan the perfect trip with all the information required to make the best possible decisions.
In the BaseCap platform, users standardize data across a number of vectors: transforming data to match other file types (PDF vs CSV), code-breaking unique terminology and short-hand, and automapping columns between spreadsheets to name a few.
About BaseCap
BaseCap is the intuitive data validation platform that operations teams use to feed quality data to AI algorithms.
"I think the tool is great because it's an out of the box solution where you can give a business admin, or someone that's knowledgeable enough from a tech perspective and a business perspective, to really drive and make the changes and really own the administration of the tool."
