To ensure effective decision-making, data management should be guided by a robust data governance framework. In a structured data management system, any enterprise can maximize the value of data in a secure manner and stay ahead in the market. Data Governance is one of the most critical aspects of treating data as a product.

Every business is now a data business. Data stands as a key asset within business operations, and the capacity to effectively monetize this asset successfully can transform a business’s overall value. Businesses are indeed recognizing the importance of data collection, analysis, and utilization to drive insights, decisions, and innovation, leading to investments in robust data strategies and the necessary technologies and talent.

Companies being acquired or valued based on their data assets or their capability to effectively utilize data. Microsoft’s purchase of LinkedIn gives them access to the professional network’s user data, and this data has the potential to help Microsoft personalize its collaboration and productivity tools, making Microsoft more competitive in the enterprise market. The value of a company can increases not only by having access to data but also by effectively utilizing that data. Data becomes even more valuable when companies use advanced systems, applications, and algorithms to uncover meaningful insights from it.

The Multifaceted Importance of Data

Data holds significant importance for businesses in three core areas: enhancing decision-making, improving operations, and monetization of data. Data enables companies to collect better market and customer intelligence, gaining insights into customer preferences, purchasing behaviours, and market trends. This information facilitates better decisions across all business functions, from product design to sales and marketing strategies. Moreover, data helps companies gain efficiencies and improve operations by tracking various aspects such as employee movements, machine performance, and delivery routes. By utilizing data-driven insights, businesses can enhance internal efficiency and productivity across different departments.

Data provides opportunities for companies to monetize information assets directly by integrating data into their product offerings. This can create new revenue streams and enhance the overall value proposition for customers. However, viewing data as a product represents a paradigm shift in how organizations perceive and utilize data. Unlike physical resources like oil, data is immaterial and renewable, generating value through analysis and innovation.

Monetizing Data as a Strategic Shift

These Data Products, ranging from metrics and dashboards to applications and APIs, are designed to create value for the organization by enhancing productivity, efficiency, and the quality of business work. This innovative approach shifts the perspective of data from a cost to an asset, considering the needs of internal and external customers and their potential to generate measurable value for the company.

Overall, leveraging data as a product underscores its importance in driving strategic decision-making and fostering innovation in the digital economy. By treating data as product organizations take a proactive approach to managing data. They design, build, and maintain data products that meet the needs of stakeholders. These products offer insights for decision-making and innovation. Embracing data as a product helps companies create new revenue, improve customer experiences, and succeed in today’s data-driven economy.

Designing Data Products for Value Creation

One of the important business-oriented definition is the one proposed by Nicola Askham “Data Governance is all about proactively manage your data to support your business achieve its strategy and vision”. It can be understood as a strategic Data Product aimed at enhancing data usage within an organization. Like any Data Product, Data Governance aims to create measurable value by providing guidelines, procedures, and rules to ensure effective and efficient data management, use, and quality.

To quantify the benefits of Data Governance, it is essential to identify and monitor key metrics that reflect the value generated by data governance practices. Below are some examples of metrics that help quantify the benefits of Data Governance:

1. Increase in Revenue: By improving data understanding and leveraging information effectively, Data Governance can contribute to revenue growth and increase the overall value of company assets. Data Governance can help boost revenue by improving how companies understand and use data. This can lead to better ways to group customers for targeted marketing, create products based on data insights, or run operations more efficiently to save costs or increase sales. Essentially, this metric captures the direct impact of Data Governance on the financial performance and value creation within an organization.
2. Compliance Support: Data Governance directly impacts the financial health and reputation of an organization. By ensuring adherence to regulatory requirements and industry standards, Data Governance helps mitigate the risk of non-compliance penalties and associated costs. This includes expenses related to fines, legal fees, and potential damages to the company’s reputation due to compliance failures. This not only reduces the financial burden but also helps maintain trust and credibility with customers, partners, and regulatory authorities. Therefore, measuring the savings achieved through compliance is a valuable metric for assessing the effectiveness of Data Governance initiatives.
3. Cost Reduction: Cost reduction is another crucial metric for evaluating the effectiveness of Data Governance. By addressing issues such as data duplications, redundant management processes, and poor data quality, Data Governance can lead to significant savings in both financial resources (expenses related to data storage, maintenance, and processing) and human resources (time spent on manual data cleaning and reconciliation tasks). Moreover, by improving data quality, organizations can minimize errors and associated costs, such as those incurred from incorrect decision-making or customer dissatisfaction due to inaccurate information.
4. Impact Analysis: Effective Data Governance ensures that stakeholders have access to accurate and reliable information about data lineage, dependencies, and impacts across various systems and processes. This allows organizations to assess the potential consequences of data changes or decisions before implementation, thereby reducing the risk of unintended disruptions or negative outcomes. By providing useful information for impact analysis, Data Governance enables stakeholders to make informed decisions regarding data management, governance policies, and strategic initiatives. This can lead to more efficient resource allocation, improved risk management, and better alignment of data-related activities with organizational objectives.
5. Integrated Management Support: Data Governance plays a crucial role in breaking down silos and fostering cross-functional collaboration by establishing standardized processes, guidelines, and communication channels for data management. By facilitating integration and alignment of data-related activities across departments, Data Governance helps avoid inefficiencies, duplication of efforts, and conflicts arising from disparate data practices. This can be quantified through metrics such as the frequency of cross-departmental meetings, the adoption of shared data standards and tools, and feedback from stakeholders on the effectiveness of communication and collaboration facilitated by Data Governance.
6. Data Repository Improvement: Data Governance initiatives aim to establish standards, policies, and processes to ensure that data stored in repositories is accurate, reliable, and accessible to users. Organizations can improve data quality through measures such as data cleansing, standardization and validation thus enhancing the integrity and trustworthiness of information stored in repositories. Data Governance facilitates classification of data within repositories, making it easier for users to locate and access relevant information. Metrics for data repository improvement may include measures such as the reduction in data errors or inconsistencies, the increase in data completeness and accuracy, and user satisfaction with the usability and accessibility of data within repositories.

Data Governance should be regarded as a strategic Data Product since it offers guidelines and rules to ensure effective data management, use, and quality, thereby contributing to the creation of measurable value. Key metrics enable the quantification of the benefits of Data Governance, including operational cost reduction, increased sales through customer data analysis, improved operational efficiency, and regulatory compliance. From bolstering revenue growth and reducing operational costs to ensuring regulatory compliance and fostering collaboration, these metrics underscore the multifaceted impact of Data Governance across diverse facets of organizational operations.

Reference

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Bernard Marr (2016) Why investments in big data and analytics are not yet paying off, Forbes, 27
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Rita Sallam (Feb 2022): Toolkit: How to Optimize Business Value from Data and Analytics Investments … Finally

Also Read: How to Implement Data Governance