Editorial
The Gist
- Risk management crucial. Data integrity issues risk flawed business strategies, regulatory penalties and reliance on unreliable instincts.
- Quality assurance vital. Continuous quality checks and data source tracking are essential for maintaining high data trust and avoiding negative outcomes.
- Collaborative efforts key. Aligning business processes, improving data governance and educating staff are pivotal for achieving reliable data trust.
Data trust aims to address the challenges of managing data within today's businesses. It should address data being trapped within closed systems, often duplicated and suffering from inaccuracy and poor definitions.
To counter these issues, data trust should ensure data accuracy, trustworthiness, fairness and regulatory compliance. Data trust should make data easily findable, current and properly governed. Implementing these principles liberates organizations to innovate and transform their businesses' customer experience and marketplace value propositions.
According to the authors of "Data is Everybody’s Business," data must be "converted into data assets that people can find, trust, and use to address unmet business needs without having to create manual, bespoke processes and controls."
Criticality of Building Data Trust
In today's data-centric landscape, trust in data is non-negotiable. Quality, compliance and reliability of data form the bedrock of decision-making, strategic planning and actionable results. When data is compromised, businesses risk everything from flawed strategies to regulatory penalties and may revert to unreliable instincts over data-driven insights.
Industry experts underscore the critical nature of data trust, emphasizing its role in ensuring a common foundation for collective outcomes. Manhattanville University CIO Jim Russell says, “Data serves as the foundational element of business decisions, analysis and projections. If the organization cannot trust this foundation, each of those elements becomes insubstantial and far more likely to lead to the wrong results and planning.”
Data Trust Issues Threaten Business Success
Challenges to data trust are multifaceted, stemming from complexity, malicious actors and inadequate literacy in data science. Nonetheless, businesses that verify and trust their data have a competitive edge. Without this, the consequences are severe, extending to reputational damage, financial loss and beyond.
In-transition CIO Martin Davis suggests that “If the data is wrong the decisions, actions and results will not follow. So, it is critical the data is trusted, as without trust the business will resort to gut instinct to make decisions not data.”
Constellation Research Vice President Dion Hinchcliffe adds, “Trust in data is a fundamental element of business. Without it, there is no common foundation or ability to dependably create shared outcomes. However, in the ephemeral digital era, data trust is growing hard to come by. The cause is data complexity plus bad actors. For me, trust issues occur in the data gathering, wrangling, analytics/AI tech or poor data science literacy. But those who are unwilling to trust or use data that can be verified will end up at the competitive mercy of those that do.”
Related Article: The Role of Data Privacy in Customer Trust and Brand Loyalty
Impacts of Low Data Trust
Diminished data trust hinders the journey to becoming data-driven, notably affecting marketing and finance departments. Marketing teams may end up making poor strategic decisions and misallocating funds due to low-quality marketing forecasts. Finance teams, who regard data as the bedrock of their operations, face even more severe repercussions, as their trust in the logical order and reliability of data is fundamental.
A breakdown in data integrity can lead to widespread dysfunction, forcing continual verification and adjustment. Notable consequences, says Smart Manufacturing CIO Joanne Friedman, “Include damage to brand value, as evidenced by Air Canada's experience, and disruptions to crucial financial measures such as treasury, cash flow, and customer metrics.”
There is clear difference for the impact of data trust between marketing and finance. Finance requires near-absolute data reliability, while marketing's operations, though adversely affected by low trust, can withstand some discrepancies. Ultimately, low data trust can lead to teams wasting time on managing error-prone spreadsheets and may result in serious negative outcomes, from reputational damage to financial loss.
Hinchcliffe argues, “The tolerance for low trust in data between marketing and finance basically defines the spectrum.” Put simply, UC Santa Barbara Deputy CIO Joe Sabado says, “The integrity of data has consequences. For one, trust is a foundation of a business’ success. Also, there can be negative results (reputational, financial, political …) from erroneous data.”
Related Article: The Role of Data Privacy in Customer Trust and Brand Loyalty
Learning Opportunities
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Fixes Needed for High Trust
CIOs believe that high business trust is achieved through a predictable and disciplined data management. The path to high trust in business, say CIOs, involves several key actions:
First, accountability for data quality and governance. Producers, ideally domain experts, must be able to rapidly identify and fix any data that fails to meet set standards. Adopting a product mindset fosters a culture of continuous management and improvement.
Second, a collaborative approach is essential. It's critical to ensure that business processes are aligned, data governance is mature and staff are well-educated about data practices. Systems must not only be current but continually evaluated and improved. Key practical fixes, from Hinchcliffe, for establishing trust in digital data include:
- Accurate data gathering.
- Consistent quality checking.
- Data source tracking/filtering.
- Balancing between secure and accessible data.
- Rigorous controls + regular verification.
- Proactive elimination of unintended data consequences.
Data Immutability Key to Business Trust
Here a focus on data immutability, packet security and data provenance underlines the importance of creating an unalterable version of the truth. This involves ensuring that data lineage is clear, and that all data handling is secure, reinforcing the overall governance framework. Achieving high business trust is challenging yet entirely attainable with concerted efforts in accuracy, security, ownership, control and governance.
The starting point for this says Russell involves “Listening and integrated work with each business unit. Are the processes aligned and optimized? How mature are data governance structures? How educated are the people? Are the systems current?” Hinchcliffe claims, “All of this is difficult but doable.”
Related Article: Using First-Party Data to Build Trust With Your Customers
Steps to Assuring Data Trust
CIOs aiming to foster business trust must balance data accessibility and security. Establishing a robust data strategy with a centrally managed catalog, clearly defined roles for data producers and consumers, and shared responsibilities is critical.
In a conversation recently with a significant industry analyst, they noted that even with catalogs, 50% of users struggle to access the data they find. This issue must be addressed, and organizations need to encourage the reuse of datasets and ensure data-driven outcomes are built on a reliable foundation. With the increasing complexity of data protection and governance, it's essential to illuminate and manage the areas beyond audit scopes, securing data before it ventures into the wild for innovative exploration.
Moreover, the choice of architecture and compliance with regulations such as HIPAA shape data trust, but the human factor remains pivotal. Continuous training to elevate employee awareness about data protection is indispensable. While transparency fosters trust and allows for verification, CIOs must treat data as a premier asset and implement a new catalog and access model. This model should be based on a "rule of use" that allows freedom of access while enforcing usage restrictions, ensuring the strategic and safe exploitation of data assets.
Without question, Russell is right when he says, “The architecture and systems you select drive some of this (cloud vs on prem or regulatory like HIPAA). But also of note is the human factor. Data protections throughout your infrastructure help but it keeps going back to train and elevate employee awareness." Friedman concludes, “CIOs should consider data one of the most valuable assets the firm has, as the strategic premise. A new catalog and access model is required based on rule of use which can apply to both data prosumer and consumers, so access is freer, but use is restricted.”
Parting Words
Navigating the complex landscape of data trust, businesses must strike a delicate balance between ensuring data quality and governance while enabling innovation and access. Experts and CIOs emphasize accountability, consistent quality checks, secure yet accessible data and ongoing education as cornerstones of high data trust. CIOs and CDOs are tasked with creating a data strategy that privileges a centrally managed catalog and defines clear roles, ensuring data is both protected and poised to generate value.
The end goal is a resilient data ecosystem where trust is synonymous with both transparency and strategic restriction, allowing businesses to not only safeguard data but also leverage it to maintain a competitive edge.
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