Grow data trust to avoid customer and corporate consequences

The consequences of poor data trust

Data trust refers to the confidence and belief that individuals and organizations have in the collection, storage, processing and sharing of data. It encompasses a range of factors, including data security, privacy, transparency and ethical considerations. Establishing data trust fosters collaboration, innovation and responsible data practices.

When employees and clients don't trust the data a company provides, the corporate impact can be significant. Data that requires trust includes inventories, supply chain deliveries, sales figures and forecasts, marketing data, and many other sources that drive decision-making. Bad data results in bad decisions. Building confidence in data accelerates its use, eliminates double-checking and positively affects the business.

Some examples of what happens when data is not trusted include the following:

• Missed business opportunities. Employees hesitate to use data if they don't feel it is accurate, properly governed and timely. This leads to missed opportunities for data-driven decision-making, market insights and competitive advantages. Businesses that fail to establish data trust risk falling behind their competitors in a rapidly evolving landscape.
• Damaged reputation and customer loyalty. A breach of data trust can have severe consequences for corporate reputation and customer loyalty. Trust takes time to build, but it can shatter in an instant. Organizations that fail to prioritize data trust may face a decline in customer retention, reduced brand value and negative public perception.
• Legal and regulatory consequences. Inadequate data trust can result in legal and regulatory repercussions, especially in regions with strict data-protection laws. Organizations may face fines, legal action or forced changes to their data practices. This can lead to financial strain and the disruption of operations.
• Ethical dilemmas. Lack of data trust can lead to ethical dilemmas surrounding data collection, usage and sharing. This can harm individuals' privacy and affect societal values and norms. Companies that proactively address these ethical considerations can maintain trust and avoid moral dilemmas.
• Limited data sharing and collaboration. Data trust enables data sharing and collaboration within and between organizations. When data trust is lacking, businesses may be unwilling to enter data partnerships or share insights, which hinders opportunities for innovation and limits the collective benefits from data collaborations.

How to build data trust

Building data trust is both a technical challenge and an operational one, which may require a shift in how organizations collect, store, manage, analyze and present data. Many steps in the data management process must be evaluated and considered across the data pipeline. Some high-level considerations include the following:

• Data processing. Organizations should have clear strategies and data governance about their data practices, including how to collect, store, use and share data. Stakeholders should easily understand these at each step of the chain of custody. The first critical step is understanding how and where they collect, protect and govern data.
• Security measures. Implement robust security measures -- such as encryption, access controls and regular security audits -- to protect data from unauthorized access and breaches. Demonstrating a commitment to data security builds trust with customers and stakeholders.
• Ethical data handling. Companies should adopt ethical frameworks for data handling, ensuring that data is used responsibly and in accordance with individuals' expectations. By prioritizing ethical considerations, businesses can mitigate risks and build trust.
• Compliance with regulations. Stay informed about evolving data protection regulations and proactively adapting policies and practices to ensure compliance is essential. Companies prioritizing regulatory compliance demonstrate their commitment to data trust and responsible data practices.

Data trust is an indispensable element in the digital era. It helps drive innovation, customer engagement and sustainable business growth. With data trust, organizations can avoid numerous challenges including privacy concerns, lack of consumer confidence, compliance issues and limited collaboration opportunities. Organizations can build stronger relationships, protect their reputation and navigate the evolving regulatory landscape by prioritizing data trust.

Embracing transparency, security measures, ethical data handling and compliance can enable companies to harness the transformative power of data and foster trust in the process. There are a variety of tools in the market from vendors such as Informatica, Quest, Egnyte, Splunk, Teradata and more.

Editor's note: Enterprise Strategy Group is a division of TechTarget. Its analysts have business relationships with technology vendors.