Data – A Strategic Asset to Organization

Introduction

In today's digital era, data has become one of the most valuable assets for organizations. By leveraging timely data analytics, companies can make informed, proactive decisions that drive competitive advantage, improve product quality, and optimize costs. Data is treated now as a product in many organizations and that brought better data organization and use of data.

Industry 6.0 represents the next phase of industrialization, which is focused on creating fully integrated, intelligent manufacturing systems, intelligent and optimized supply chain that can operate with minimal human intervention and reduced resources. It combines human intelligence, artificial intelligence, cloud computing energy, human–robot working big data, quantum computing. Data is considered as a Product in recent years and stored and operated as product. Data is the fuel for enabling all upcoming technologies and outcome. As a result Data is becoming a Strategic Asset to any organization rather than just another product.  Strategic Asset is one of the Most Valuable entities that Organizations owns and the strategy to maintain these assets varies with Organizations. If organizations start considering data as a strategic asset, they will approach its generation, maintenance, and use more efficiently. For example, in the software industry, developers are considered strategic assets, and there are comprehensive processes to hire, maintain, upskill, and efficiently utilize them in projects until their termination.

The implementation of Data as a Strategic Asset aligns closely with the principles of data as a product but is guided by the unique strategic vision of each organization. While there may be common data strategies across industries, the way a specific organization treats its strategic data assets (e.g., offers, customer insights) is shaped by its broader business objectives. This means that data, even if similar in nature, is leveraged differently across organizations to support long-term goals, competitive differentiation, and innovation. In essence, it is the overlay of an organization's strategic vision on top of its data product approach, ensuring that data not only serves immediate needs but also drives sustained growth and value.

Existing Data Issues

Organizations face several major challenges when operating and managing data in today's dynamic business environment. Some of these problem statements can be summarized as follows:

1. Data Silos: Different departments or applications store and manage data in isolation, preventing a unified view of information. This fragmentation hinders collaboration, data sharing, and holistic decision-making across the organization.
2. Data Integration: Integrating data from multiple sources, both internal and external, in a seamless manner is complex. Different formats, standards, and systems create difficulties in combining and processing data for comprehensive analysis.
3. Data Quality and Consistency: Poor data quality—stemming from inaccuracies, duplications, or outdated information—poses significant challenges. Ensuring data consistency across systems and applications remains a difficult task, resulting in flawed insights and decision-making.
4. Data Accessibility: Applications, Business users, Customers, Partners are primary actors who uses data for various purposes. Data Accessibility in timely manner is a challenge today due to the siloed nature or Centralized (Data Lake / Data Warehouse) data store. Data Duplication is another hurdle, as consumer doesn’t know which is right and which is wrong.
5. Timeliness of Data: Many organizations struggle to deliver data in a timely manner for decision-making. Delayed or slow data processing can result in missed opportunities and reactive strategies, especially in real-time applications like customer support, predictive maintenance, and dynamic pricing.
6. Data Security and Compliance: With the rise of cyber threats and stringent data privacy regulations (GDPR, HIPAA, etc.), securing sensitive data and ensuring compliance is more critical than ever. We need to have a good governance.
7. Data Governance and Ownership: Establishing clear policies for data governance, ownership, and stewardship remains challenging.
8. Data Democratization: Balancing data accessibility with security and governance is critical.
9. Data End of Life: As any other products, data also have end of life, some may be short lived as mins, some may be immortal and some in between. Definitions of Data End of Life and disposal is concern, results data growth in even product authoring.
10. Maximizing Data Value: While data is often seen as a strategic asset, we struggle to unlock its full potential. Turning raw data into actionable insights that drive innovation, optimize processes, or create new revenue streams is a significant hurdle.

Addressing these challenges requires a comprehensive data strategy that encompasses governance, technology infrastructure, and cultural shifts toward treating data as a key organizational asset – Leads the concept of Data as Strategic Asset.

Data as Strategic Asset

Data as a Strategic Asset is valuable for any organization, because it drives informed decision-making, improves operational efficiency, fosters product innovation, and enhances customer personalization, which result improving revenue and margin. It provides a competitive advantage by enabling businesses to capitalize on trends early and generate new revenue streams (E.g. Generative AI or Quantum Computing). Additionally, data helps manage risks and ensures regulatory compliance, positioning organizations for long-term growth and resilience in the market. Now question is what is the difference we need to do differently make to view Data as a Strategic Asset.

Data as a Strategic Asset / Product and its Life Cycle

When we consider data as Strategic Asset, it can follow similar life cycle of the product however, with strategic outlook to each stage.

Data Generation: It is analogous to product design in physical product. Here we plan to author new data or generate new data (E.g. Product Authoring or Order / Subscription Creation).

• Data Strategy as Asset: A clear purpose for the data is defined, including how it will be generated, used, what value it will provide, and how it fits into the overall business objectives with scalability.

Data Processing & Storage: Here the generated data is either processed to convert meaningful outcome, just like raw materials assembled to manufacture product.

• Data Strategy as Asset: Optimize Storage with accessibility, and security.—similar to how products are efficiently assembled and stored in warehouses.  A Clear definition of Data Storage for any required distribution. E.g.
• For another Data Processing and Storage, Data Science Operations, Classical Analytical, LLM based Search (Generative AI should not be a siloed process)

Data Strategy also needs to be defined for the mode of data like Cold, Warm and HOT, Object Store, In memory Store, Distributed Storage etc.

·Data Usage & Distribution: This is where data starts delivering value, driving decision-making processes, powering analytics, and AI models, LLM based Query result, and influencing strategic directions. It parallels the product being deployed and used by customers.

• Data Strategy as Asset: Ensuring data is available to the right users or systems through APIs, Data Product Service, Secured RAG, Consumer Driven Data Availability at the “right time.” For example, as a strategic decision, Channel Partners should be able access their data seamlessly

Data Governance & Security: Data Governance & Security span across whole life cycle of Data from the Generation to disposal, similar to the Quality and Compliance assurance in the normal products. This includes PII Data store, GDPR adherence etc.

• Data Strategy as Asset: PII Data classification from generation to disposal, Governance to add or remove data, Data Auditing strategy. Real-time monitoring and automated incident response systems are crucial to detect and mitigate threats swiftly, providing a secure foundation for leveraging data as a valuable asset.

Data Monetization: Some organization may not too keen on making revenue using data, but saving time and resources and making informed decision can save cost, which indirectly gain revenue. Also making the right data available to the partner also will increase the loyalty and sales. This is much like companies' market and sell their products.

• Data Strategy as Asset: Setting up Data Strategy for business goal to manage data with less resources and more effectively. For Example. Light weight data transfer, high availability of data for all strategic initiatives (E.g. Generative AI, Quantum computing), Partner Data Exchange.

Data Archival & Disposal: Like aging products, some data becomes less valuable but still needs to be stored for future reference or regulatory reasons. Archived data is securely stored with low-cost solutions for long-term retention.

• Data Strategy as Asset: Archival, Disposal strategy for various data types keeping that as a valuable asset.

Above all this the Data lineage from Product / Customer to Planning to Manufacturing to Logistics is another Key for a successful end to end data strategy. Information Architecture is Key to solve this issue.

Is this effort NEW?

Information architecture and data strategy efforts have been attempted in many organizations at different times, with varying degrees of success. Some have been successful, while others have struggled to gain traction. Traditionally, these efforts were developed and defined by a separate team. However, a small team cannot fully grasp the entire domain knowledge and the engineering complexities. Although they may consult with the engineering team during development, the rapid pace of development—especially in Agile methodologies—makes it challenging for engineering teams to adhere to the data strategy

Some of the area we need to think about defining a well thought data strategy is

·       Align Data Strategy with Business Goal

·       Engineering Team involvement in Strategic Definition

·       Embedded Data Strategy in Development Process

·       Iterative Implementation

Conclusion

Data is not only just another product to Organization, but it is a Strategic Asset. Strategy to Mold it to use in correct way is key for the success. Ultimately, integrating data strategy into the core of engineering practices not only enhances operational efficiency but also ensures that data becomes a powerful strategic asset, driving sustained growth and innovation.