Data is the driving force behind media and entertainment companies. Every day, they create and consume more and more data as they develop new products and services, seek to understand audience preferences, personalize user experiences, and optimize business strategies.
While that data holds immense value, offering businesses in the media industry a significant competitive advantage, it also poses substantial risks. The more data these organizations hold, the more daunting these risks become. Whether it’s mining this data for valuable insights, consolidating data after a merger and acquisition, or maintaining control over intellectual property during an investigation, data-related concerns loom large.
Let’s take a closer look at these risks and what media and entertainment companies should do to mitigate them.
The risks of poor data management in the media and entertainment industry
The media and entertainment industry relies on public trust and reputation. During a time when digital technologies enable seamless access to content and personal information, safeguarding customer data is not just a legal obligation; it’s a fundamental necessity. A data breach can severely damage the trust that viewers, listeners, and consumers place in media and entertainment companies, leading to customer churn and heavy financial losses. What’s more, cyberattacks that target intellectual property are existential threats for companies in the media and entertainment business, where projects must be protected from public exposure until their scheduled release.
Needless to say, mismanaged data creates risks, which include:
- Loss of customer trust: If customer details such as addresses, phone numbers, or payment information are compromised, customers may lose confidence not only in the company’s reporting but also in its productions.
- Reputational damage: Negative publicity about data security incidents can tarnish the company’s brand. This could lead to a loss of customers and business partners.
- Legal and regulatory consequences: Mismanaged data may result in legal consequences, fines, or lawsuits, especially if the company is found to be in violation of data protection laws.
- Financial impact: Data breaches can be costly. In 2023, the average cost of a data breach reached 4.45 million USD, marking a 15% increase over a span of 3 years. This financial burden is not just limited to immediate expenses; companies might need to invest in additional cybersecurity measures, compensate affected customers, pay regulatory fines, and cover legal fees. This comes at a time when revenue is likely declining due to the loss of customers and business opportunities stemming from the breach.
- Intellectual property theft: Mismanaged data can expose valuable scripts, videos, music, or other creative assets to the risk of theft. This exposure can lead to financial losses and potential legal battles. For instance, in a 2020 lawsuit, Thomson Reuters alleged that the legal research company Ross Intelligence unlawfully copied content from its legal research platform, Westlaw, to train a competing artificial intelligence-based platform.
- Operational disruptions: Addressing a data breach or responding to an investigation can significantly disrupt normal business operations. Resources that could have been used for business development may need to be diverted to manage the breach or matter, impairing overall productivity and slowing growth.
The 7 elements of a data governance framework for media and entertainment companies
A data governance framework is a structured approach that defines how an organization collects, manages, stores, shares, and uses data. It encompasses a set of policies, procedures, and guidelines that ensure data quality, privacy, security, and compliance. A framework also establishes accountability for data management, ensuring that data is used effectively to support business goals and is treated as the valuable asset it is.
To avoid the risks associated with mismanaged data, media and entertainment companies should develop a data governance framework that includes these seven components.
1. Clear data governance policies
To ensure a consistent approach across the organization, draft clear, understandable policies that outline how employees should handle, store, and protect data. Include data usage policies that define how data can be used within the organization, such as who can access certain types of data and for what purposes. Establish standards for data accuracy, completeness, consistency, and reliability. Create records retention policies that outline how long data should be retained and set out the legal and business requirements for data storage and disposal.
( Click here to download a data retention policy template.)
2. A data governance council with decision-making authority
Assemble a governing body to make decisions related to data governance policies, standards, and practices. Include members from various departments, such as IT, legal, compliance, business operations, and security, to ensure that diverse perspectives are considered when making data governance decisions. Ensure that the council has the authority to make decisions related to data governance policies, resolve disputes, and enforce compliance.
3. Consistent data quality management protocols
To ensure data remains accurate, consistent, and reliable for decision-making, it’s essential to implement effective monitoring and improvement processes. Establish data validation and standardization protocols to maintain uniformity across various datasets, especially when integrating multiple sources. Perform regular data profiling and auditing to identify inconsistencies and anomalies, ensuring ongoing data integrity. Assign data stewards the responsibility of overseeing and maintaining data quality within their domains, and use clear metrics like accuracy, completeness, and timeliness for regular quality assessments.
It’s also crucial to adapt data quality practices to align with the organization’s evolving needs, including changes in data sources, business requirements, and technological advancements. Additionally, incorporating feedback effectively helps in refining these practices, ensuring that your organization’s data remains a reliable asset for informed decision-making.
4. Strong data security measures
Establish measures to protect sensitive data from unauthorized access, breaches, and cyber threats. For instance, implement role-based access controls to ensure only authorized individuals access specific data sets. Encrypt sensitive data both in transit and at rest to protect it from unauthorized access, even in the event of a breach. Apply data masking or anonymization techniques, especially during testing or analysis, for additional safety. Regularly conduct security audits and risk assessments to identify and mitigate potential vulnerabilities, and develop a comprehensive incident response plan for swift action in case of security breaches.
Educate employees regularly on data security best practices and the importance of vigilance against common threats like phishing. Incorporate multi-factor authentication for accessing sensitive data, and maintain rigorous backup and disaster recovery plans to safeguard data integrity under all circumstances.
5. An understanding of the company’s data architecture
It’s impossible to manage data you don’t know about. Make sure you understand what data you collect, where it resides, how it’s used, and who has access. Spend time studying your organization’s data architecture. This involves:
- Mapping data flows: Identify how data moves and is processed across various departments and systems, to pinpoint where governance is most needed.
- Assessing data storage and accessibility: Evaluate how and where data is stored (databases, data warehouses, cloud storage), and ensure that it is accessible securely and efficiently.
- Integrating diverse data sources: Ensure consistent and reliable integration of data from various sources, maintaining data interoperability across different systems.
- Effective metadata management: Implement robust metadata management for clarity on data origin, format, and quality, which is critical for data discovery and compliance.
- Adapting to technological changes: Regularly update data models and architecture to keep pace with evolving business needs and technological advancements.
This strategic approach to understanding and managing the data architecture ensures that data is not only secure and compliant but also optimized for business utility.
6. Practices that ensure data privacy compliance
You may need to comply with relevant laws and regulations related to data privacy, such as the General Data Protection Regulation (GDPR) in Europe, the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA). Ensure that your data governance practices align with these requirements. To aid compliance, conduct a thorough audit to identify any personal data your organization collects, processes, and stores. Be sure to categorize the risks associated with your data based on its sensitivity and relevance to privacy regulations. Regularly audit and assess your privacy practices to ensure ongoing compliance with applicable regulations, and address areas of noncompliance promptly.
7. Data management that spans the data lifecycle
Effective data management involves the entire data lifecycle, from creation to disposal. This includes clear data classification, well-defined retention policies, robust archiving, secure disposal, continuous auditing, governance, meticulous documentation, and proactive monitoring. Tools and technologies can help media and entertainment companies effectively manage their data, ensuring that it is secure, accurate, and accessible to support informed decision-making.
Enhance your data governance strategy with Onna
By adopting a comprehensive data governance framework, media and entertainment companies can maximize the value of their data while reducing its risk. That puts them in a position to maintain both their customers’ trust and their own foothold against the competition.
Onna supports your data governance strategy with a scalable and adaptable approach to managing and protecting unstructured data, all while providing quick access to authorized users. Use Onna’s secure Data Archive to control, access, and protect your proprietary data from key systems such as Slack, Google Workspace, Microsoft O365, Zendesk, Confluence, and Jira.
