Its 833 Information Governance Chapter 7 By Dr. Geanie Asant

Its 833 Informationgovernancechapter 7dr Geanie Asantecopyrightgea

Understand the core concepts and challenges related to information governance (IG), particularly focusing on structured versus unstructured data, the cost implications of managing unstructured information, and strategies to effectively govern and leverage organizational data assets. Additionally, explore how an IG-enabled organization differs from one without such practices, and examine tools, models, and cost drivers essential for comprehensive information management.

Paper For Above instruction

Information governance (IG) is a strategic framework integral to managing an organization’s data assets, particularly as the volume of unstructured information continues to grow exponentially. This paper examines the fundamental distinctions between structured, semi-structured, and unstructured data, elucidates the challenges inherent in managing unstructured information, explores full cost accounting (FCA), and discusses how organizations can effectively implement IG practices to reduce costs, mitigate risks, and improve decision-making.

Understanding Data Types and Their Challenges

Structured data refers to information stored in fixed fields within a database schema, such as integers, dates, or predefined categories that facilitate easy retrieval and analysis. In contrast, unstructured data encompasses non-conformant formats like emails, documents, images, videos, and social media content, which lack a predefined data model. Semi-structured data, lying between the two, includes formats like XML and JSON, which possess organizational elements but do not conform entirely to traditional database schemas (Beyer et al., 2010).

The rapid growth of unstructured data presents significant challenges. Its horizontal nature means it is dispersed across various platforms, locations, and formats, complicating identification, classification, and management. Unlike structured data, unstructured information often lacks formal organization, making ownership and responsibilities unclear. This leads to increased difficulty in discovering relevant data during legal or compliance processes and complicates data security and privacy efforts (Dalle et al., 2017).

Furthermore, unstructured data expands storage costs and demands more labor-intensive management, especially for knowledge workers tasked with tagging, categorizing, and maintaining data integrity. As it proliferates, organizations face difficulties in deriving value from such data, underscoring the need for effective governance strategies.

Cost Implications of Unstructured Data

The management of unstructured information entails multiple costs categorized broadly within full cost accounting (FCA). Rising storage costs are compounded by the need for scalable infrastructure to handle ever-increasing data volumes. Labor costs, often the largest expense, involve skilled personnel required for content classification, data curation, and security management (Fornell et al., 2011). Overhead costs, such as maintaining compliance with regulations and e-discovery requirements, further inflate expenditures.

Opportunity costs also emerge when unstructured data is poorly managed, leading to missed insights, inefficient decision-making, or inappropriate legal disclosures. According to the FCA model, direct costs include storage and personnel expenses, whereas indirect costs encompass productivity losses and potential legal liabilities. These costs underscore the importance of strategic management to optimize data value and control expenses (Sarda et al., 2020).

The Models for Full Cost Accounting in Information Management

Organizations utilize various models to assess the total cost of ownership for their data assets. The Total Cost of Ownership (TCO) model incorporates immediate expenses such as infrastructure and personnel, as well as future costs related to data growth, obsolescence, and compliance obligations (Wang et al., 2012). Return on Investment (ROI) frameworks help justify governance initiatives by illustrating potential cost savings and risk reductions over time.

Full Cost Accounting (FCA) extends beyond monetary costs, integrating environmental and societal impacts, aligning with the triple bottom line approach (Elkington, 1997). FCA’s comprehensive perspective facilitates better strategic planning, highlighting the importance of legal, regulatory, and security costs, along with indirect impacts on organizational reputation and operational efficiency.

Implementing Effective Information Governance Practices

Successful IG implementation depends on formal, communicated policies, automated classification tools, and defensible deletion protocols. Data maps and proactive e-discovery processes enable organizations to locate and manage unstructured information efficiently. Clear governance structures and structured repositories prevent uncontrolled proliferation of data, minimizing risks and costs.

Technologies such as automated tagging and content management systems streamline classification and help enforce compliance with legal and regulatory requirements. Moreover, establishing ownership responsibilities ensures accountability and facilitates consistent data management across departments (Koh et al., 2019).

An IG-enabled organization is characterized by standardized processes, integrated policies, and a culture of accountability. Such organizations leverage analytics, dashboards, and metrics to continuously monitor data health, security, and compliance, resulting in more strategic use of information assets and reduced legal exposure.

The Business Case and Strategic Benefits of IG

Investing in IG is often justified through long-term benefits rather than short-term returns. Effective data governance reduces legal and e-discovery costs—currently significant drivers of data management expenses—by facilitating quick, defensible data retrieval during litigation (Blatt et al., 2018). It also enhances legal posture by ensuring data privacy and reducing risks associated with regulatory penalties.

Beyond legal advantages, IG contributes to better decision-making by ensuring data quality and integrity. Clean, accurate data enhances business intelligence initiatives, leading to more informed strategic choices. Additionally, IG fosters operational efficiencies, such as reducing duplicated or corrupted data, improving communication between units, and supporting knowledge management programs (Lehmann & Basili, 2010).

In essence, an organization embracing IG practices is better positioned to meet compliance requirements, harness data as a valuable asset, and achieve strategic objectives—all while managing costs and risks effectively.

Conclusion

As data volumes grow exponentially, particularly unstructured information, organizations must adopt comprehensive governance frameworks to manage this asset effectively. Recognizing the differences between data types, understanding the associated costs, and applying full cost accounting models are essential steps toward strategic data management. Implementing automated classification tools, establishing clear ownership, and fostering a governance culture enhance an organization’s ability to reduce costs, mitigate risks, and leverage data for competitive advantage. Ultimately, a well-implemented IG program transforms data management from a costly liability into a valuable strategic resource, ensuring long-term organizational resilience and growth.

References

• Beyer, M. A., Buchan, M., & Muller, H. (2010). Data Management and Data Governance: Strategies for Success. Data & Knowledge Engineering, 68(11), 1-22.
• Dalle, J., Leonard, A., & DiPietro, R. (2017). Managing Unstructured Data in Modern Enterprises. Information Systems Management, 34(2), 111-123.
• Fornell, D., Nelson, R., & Kauffman, R. (2011). The Hidden Costs of Data Management. Journal of Information Management, 51(3), 201-215.
• Koh, H. C., Tan, G. W., & Lim, K. H. (2019). Developing Data Governance Policies to Improve Organizational Efficiency. International Journal of Information Management, 46, 21-29.
• Lehmann, D., & Basili, V. R. (2010). Managing Data Quality and Integrity. Communications of the ACM, 53(8), 147-152.
• Sarda, S., Sharma, A., & Kukreja, P. (2020). Total Cost of Ownership in Data Management: Challenges and Strategies. Journal of Business Strategies, 41(4), 55-66.
• Wang, Y., Qiu, T., & Chen, X. (2012). Cost Models for Data Infrastructure Investment. Journal of Information Technology, 27(4), 318-330.
• Elkington, J. (1997). Cannibals with Forks: The Triple Bottom Line of 21st Century Business. Capstone Publishing.