Orbweaver Blog

Data Storage and Format Considerations for Future-Proofing Your Digital Transformation

Chris Ciesielka
Current situation: You are working within the electronics industry. You have data management responsibilities and are looking to embark upon a digital transformation strategy that will 1) provide insights for your organization, 2) integrate your organization more seamlessly with your trading partners, and 3) create opportunities for automation to capture new efficiencies.

In this article, I discuss a couple of topics you might consider as you begin this initiative:

  • Do you need to change the methods and means by which data is stored within your organization? and
  • What data exchange protocols and formats align with your business and that of your trading partners?

This article also considers the relevant value and need for new data exchange methods that are being advanced within the electronics industry.

A review of key terms

Let’s begin by getting aligned on the definitions of relevant terminology:

Data Element. A data element is the smallest, indivisible unit of data that carries meaning. It represents a specific piece of information and can be as simple as a single character or as complex as a structured record. Data elements are the building blocks of data and are typically given a name or label for easier identification and reference.

Example: In a database of customer information, a data element could be the “Customer Name,” where each customer’s name (e.g., “John Smith” or “Mary Johnson”) represents a distinct data element. Within a system, this data element may be represented in code as customer_name.

Data Type. A data type defines the nature and characteristics of a data element, including its format, range of values, and operations that can be performed on it. Data types are crucial for ensuring data integrity and consistency, as they specify how data should be stored, manipulated, and interpreted.

Example: In programming, common data types include integers (int), floating-point numbers (float), strings (str), and Boolean values (bool). For instance, an integer data type may represent whole numbers like 42 or -17.

Data File Format. A data file format refers to the structure and organization of data within a file. It defines how data elements are arranged, delimited, and stored, making it possible to read and interpret the information. Data file formats are essential for data storage, retrieval, and sharing.

Example: The CSV (comma-separated values) format is widely used for tabular data. In a CSV file, data elements are separated by commas, and each row represents a record, making it suitable for spreadsheets and databases.

Data Exchange Format. A data exchange format is a standardized way of representing and transmitting data between different systems, applications, or organizations. These formats ensure interoperability and enable data to be seamlessly shared and understood by diverse entities.

Example: JSON (JavaScript Object Notation) is a prevalent data exchange format. It uses a text-based format to represent structured data as key-value pairs, making it easy to exchange data between web services, applications, and platforms.

Considerations for revising data storage methods

Revising the method and format of data storage is a critical aspect of any digital transformation initiative. Modernizing data storage practices can improve the efficiency, accessibility, scalability, and security of data, enabling organizations to harness the full potential of their data assets. Consider the following six elements and key considerations for revising data storage methods and formats as part of a digital transformation initiative:

  1. Strategic alignment and planning
    • Data strategy alignment: Ensure your revised data storage strategy aligns with your overall digital transformation goals and business objectives. Define how the changes will support your organization’s vision and strategic initiatives.
    • Data assessment: Conduct a thorough assessment of your existing data assets, including their volume, variety, velocity, and quality. Understand what data is valuable and what can be archived or deleted.
    • Change management: Communicate changes effectively to stakeholders and involve them in the decision-making process. Address concerns and gather feedback to make adjustments as needed.
  1. Data organization and classification
    • Data classification: Classify data based on sensitivity, regulatory requirements, and access permissions. Implement appropriate security measures for sensitive data to ensure compliance with data protection regulations.
    • Metadata management: Develop a robust metadata management strategy to catalog and index data, making it easier to discover and use. Metadata enhances data governance and data lineage tracking.
  1. Storage infrastructure and format
    • Cloud storage considerations: Evaluate the potential benefits of cloud storage solutions, such as scalability, cost-efficiency, and accessibility. Determine which data should be migrated to the cloud and which should remain on-premises.
    • Data storage formats: Choose data storage formats suitable for your data types and use cases. Consider using modern, open standards like JSON or XML for structured data and binary or object storage for unstructured data.
    • Scalability and performance: Ensure that the revised storage infrastructure can scale to accommodate growing data volumes and provide adequate data retrieval and processing performance.
  1. Data management and lifecycle
    • Data lifecycle management: Implement policies to automate the movement, archiving, and deletion of data as it ages. This ensures that data is stored efficiently and cost-effectively.
    • Data versioning and backup: Establish data versioning and backup mechanisms to protect against data loss and corruption. Regularly back up critical data and maintain version history for auditing and recovery.
    • Cost management: Monitor and optimize data storage costs to ensure that the revised storage infrastructure remains cost-effective over time.
  1. Data security and compliance
    • Data security and privacy: Prioritize data security and privacy by implementing access controls, encryption, and auditing mechanisms. Stay compliant with relevant data protection regulations.
    • Data compression and encryption: Implement data compression to reduce storage costs and optimize data transfer. Encrypt sensitive data both in transit and at rest to enhance security.
    • Data governance and compliance: Implement data governance practices and compliance measures to maintain data quality, ensure regulatory adherence, and establish data ownership and stewardship.
  1. Access, integration, and usability
    • Data access and integration: Facilitate seamless data access and integration using APIs and data integration platforms. Enable data to flow smoothly across systems to support analytics and reporting. More about this later.
    • User training and adoption: Provide training and support to ensure users understand the new data storage methods and formats. Encourage adoption by showcasing the benefits of improved data access and analysis.
    • Monitoring and analytics: Implement monitoring and analytics tools to gain insights into data usage, performance, and storage costs. Use this data to optimize your storage strategy continuously.

The most important consideration when revising data storage methods and formats is feasibility. What is possible, and potentially more importantly, what is not? Like many organizations in the industry, your business may rely on home-grown systems with tools designed by personnel who have long since retired. Such systems may pose a challenge to the organization, particularly as they may no longer be fit for purpose, lack the necessary security features, or be incompatible with newer technologies. 

The portability of that data or transfer to an alternative technology infrastructure may require a larger overhaul of the base platforms and resources used within your organization, and a project of that scale may not be a possibility at this time or any time soon. There is an alternative to this scenario that addresses not only the feasibility question but also many of the other considerations as well, including future-proofing.

Partnering with a capable third party can provide an opportunity to scale up and mobilize data without impacting your current data storage methods at all. This approach can yield various benefits, including enhanced efficiency, cost-effectiveness, and improved data-driven decision-making. Providers like Orbweaver act like a wrapper around your existing systems, such as MRP, CRM, PIM, DAM, etc. Integration to external trading partners and systems, as well as scalability, security, and alerts, are managed in the wrapper layer. If your challenges can be resolved with this approach, you may find the result to be more cost-effective and with a faster time to value (TTV) than building solutions yourself.

By carefully considering these factors and aligning your data storage and management processes with your digital transformation goals, you can create a robust and efficient data strategy that supports your organization’s growth and innovation efforts.

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