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The implementation of data management strategies, such as meta-data management, data governance, and master data management, has been touted since the dawn of the data warehousing era in the 1980s. Despite its perceived benefits, it has been challenging to enforce in many organizations. Failure to prioritize data management often results in data duplications, inefficiencies, and a lack of a definitive source of truth. These organizational silos have plagued several companies, especially those with vast amounts of data spread across multiple systems.
Recent years have seen a significant uptick in data privacy concerns, with several regulations being put in place to safeguard user data. This development has served as a wake-up call for organizations to prioritize disciplined data management. Not only does it apply to existing data assets, but it also ensures compliance as new data is collected, processed, used, and stored. In fact, a study conducted by the Boston Consulting Group found that companies that prioritize data governance had a 45% higher return on equity compared to those that did not. Another study by Forbes Insights found that companies that prioritized data management were more likely to achieve their revenue goals and were better equipped to manage risk and comply with regulations.
The regulations that were briefly mentioned in the previous paragraph, EU’s GDPR (General Data Protection Regulation) and the CCPA (California Consumer Privacy Act), aim to bolster consumer privacy rights by protecting the personal information that businesses collect, use, or sell. Despite some differences in the scope of regulated companies, the size of fines, and the methods for consumers to exercise their rights, both regulations enforce certain fundamental consumer rights:
By enforcing these rights, both regulations help safeguard consumers’ privacy and control over their personal information.
Ensuring compliance with one privacy regulation can be a daunting task for companies, let alone adhering to two or more.For example, the EU’s GDPR has already resulted in over €114 million in fines since its implementation in 2018. Additionally, the California Attorney General has imposed over $100 million in fines since the CCPA went into effect in 2020.
The privacy regulations directly impact data collection, use, and storage on a large scale for enterprises. This can be incredibly resource-intensive and time-consuming for organizations that lack the necessary tools and governance processes to identify precisely where personal information resides, who has access to it, and how it is being used. A study by IBM estimates that the average cost of a data breach in 2021 is $4.24 million, emphasizing the importance of effective data management and compliance.
On the other hand, companies with effective data management practices in place can streamline the implementation of privacy regulations. Focusing on areas such as metadata management, data governance, and master data management can help organizations locate and manage personal data more effectively. By prioritizing disciplined data management, companies can ensure compliance with privacy regulations while also improving their overall data management capabilities.
Effective data architecture is a critical component of successful data management. The two cannot be separated. Well-designed data architecture involves clear documentation starting from business requirements, business-to-data mapping, business glossaries, naming conventions, and standards. It should also prioritize data privacy and security in the design process.
Data architecture provides clarity, transparency, and standards for each data object, down to the data element level, including where personal information is collected, used, and stored. This leads to clear design, efficient implementation, and optimal results, as privacy regulations like CCPA and GDPR require compliance with consumer rights to access, be forgotten, and opt-out of the sale of their personal information.
According to a survey by Infosys, 59% of organizations cite lack of clarity in data ownership and management as a barrier to effective data management. Effective data architecture can address this challenge and provide a framework for clear and efficient data management.
In order to enable privacy compliance, it is essential to have a comprehensive data catalog that accurately classifies and tracks data objects and elements. A good analogy is a library catalog that systematically tracks book titles, authors, genres, and locations, making it easy for users to find books without having to read them all. Similarly, a company’s data catalog should be established to quickly locate personal information within data assets in various systems, including details such as when the data was created and how it is used.
According to a study by Gartner, only 10% of organizations have an established and maintained data catalog. Furthermore, data governance is necessary to ensure the data catalog has complete and accurate information, with data stewards from different departments responsible for maintaining standards and policies to sustain compliance for the organization. By implementing these measures, organizations can ensure effective data management and compliance with regulations such as CCPA and GDPR.
A clear and well-defined data retention policy is crucial for companies aiming to ensure privacy compliance. It is essential to delete data that no longer serves a purpose or has exceeded the required retention period set by regulations such as GDPR and CCPA. GDPR mandates that personal data should not be kept “no longer than is necessary for the purposes for which the personal data are processed” (Articles 5(1)). The exponential growth in data sciences and analytics has led to the creation of numerous data objects that may contain sensitive information without the users’ awareness. Holding onto such data beyond the required retention period can increase the risk of data breaches and non-compliance with data access and data privacy regulations. According to a study by IBM, the average cost of a data breach in the United States was 9.05 million U.S. dollars in 2021, highlighting the importance of implementing a robust data retention policy to mitigate the risks associated with data breaches and privacy violations.
In light of the increasing concerns over data privacy and security, it is vital for companies to store their customers’ information in as few places as possible and manage it with utmost care. One solution that companies can adopt is Master Data Management (MDM), which provides a centralized system for data storage and management. MDM ensures data quality and serves as a single source of truth, thus enabling the best design for privacy compliance.
By adopting MDM, companies can eliminate the need for storing personal information in multiple locations, reducing the risk of data breaches and ensuring compliance with privacy regulations. With the centralized master data service, there is no need for other systems within the organization to store customer information permanently. Instead, they can retrieve the data from the MDM system, which serves as the golden record.
Moreover, MDM can enforce data intake processes for privacy information and establish fundamental business rules for data processing. It also provides careful control of data access, ensuring that only authorized personnel can access sensitive data.
To ensure sustainable compliance with privacy regulations, companies must establish robust data control processes that can automatically detect and alert on non-compliance. This involves implementing business rules and procedures to identify any personal information that may be inappropriately stored or used in violation of privacy regulations. According to a survey by the International Association of Privacy Professionals (IAPP), privacy technology spending is on the rise, with 72% of companies investing in technology to improve their privacy compliance programs (source: IAPP-EY Annual Privacy Governance Report 2021). By leveraging technology to automate compliance monitoring and risk detection, companies can ensure their privacy controls remain effective and sustainable over time.
In summary, data management practices such as data governance, metadata management, and master data management have been challenging to enforce in many organizations, leading to inefficiencies, data duplications, and silos. However, recent data privacy regulations such as the CCPA and GDPR have brought attention to the importance of disciplined data management. Effective data management practices can help companies ensure compliance with regulations and improve their overall data management capabilities, ultimately leading to better risk management and revenue growth. Prioritizing well-designed IT infrastructure, meta-data management, data governance, and a clear data retention policy can help companies meet these challenges and thrive in the digital age.
If you have implemented more than three services into your system, chances are, data is scattered all across them and you need the right tools to monitor your whole IT architecture. AINSYS integration framework syncs data between every tool and platform your IT team employs, helping you get an accurate picture of your data. By implementing AINSYS tools, any IT consultant can ensure they make the right decisions for your organization and keep up with the ever-changing technology landscape.
