Bridging the Data trust gap.

Have you ever had that feeling – the feeling that your efforts to achieve your goals were not effective because of data?

The data you used might have been stale, or misinterpreted, or even unavailable. You probably expected something different from the data – and it didn’t live up to your expectations. It must have left you questioning whether your organizational data is a helpful resource, or even a partner in solving the tasks you are accountable for.

To make it even more dramatic: what impact will you make if you leave your actions to agents and AI is destined to deliver an answer without the awareness of data quality? Of course, the human in the loop will still be filtering some of the sense out of the nonsense. But still, your raw materials in the form of data assets, half fabricates in the form of data products, should be fit for purpose.

I see it quite often – the actual use of untrustful data leads to significant negative outcomes for organizations. Consider some of the key consequences:

• Poor Decision-Making

When data is not trusted, decision-makers may rely on inaccurate or incomplete information, which can lead to poor strategic and operational decisions. Without reliable data, organizations may fail to identify market trends, customer preferences, and other critical insights. Then they find themselves looking at inefficiencies, financial losses, and missed opportunities for innovation, growth, and competitive advantage.

• Inefficiencies and increased costs

Lack of trust in data can cause inefficiencies in various business processes. Employees may spend excessive time verifying data accuracy or correcting errors, which increases operational costs and reduces productivity.

• Compliance risks, legal penalties and reputational risk

Inaccurate or unreliable data can lead to non-compliance with regulatory requirements. This exposes organizations to legal penalties and fines.

Trust in data is also crucial for maintaining a positive reputation with customers, partners, and stakeholders. Data breaches, inaccuracies, or misuse can erode trust and damage an organization’s reputation. 

• Data quality issues

Poor data quality is a common result of not having trust in data. This includes issues such as data inaccuracies, inconsistencies, and lack of completeness, which further undermine trust and reliability. 

• Ineffective Data governance

Without trust in data, data governance efforts may be ineffective. This will potentially lead to poor data management practices, lack of accountability, and inadequate oversight of data-related activities.

• Security risks

Lack of trust in data can also increase security risks. Organizations may struggle to implement effective data security measures, leading to vulnerabilities and potential data breaches.

• Challenges in Data integration and interoperability

Trust issues can complicate data integration and interoperability efforts. Inconsistent or unreliable data can hinder the seamless exchange and use of data across different systems and platforms.

• Reduced organizational effectiveness

Overall, the lack of trust in data can reduce organizational effectiveness. It impacts customer experience, employee morale, and the ability to achieve business objectives. 

• Ethical and compliance issues

Untrustful data can lead to ethical concerns and compliance issues, particularly in areas such as data privacy and protection. Organizations may face challenges in ensuring ethical data handling and meeting regulatory requirements 

Not having trust in data can have far-reaching and harmful effects on an organization’s decision-making, efficiency, compliance, reputation, and overall effectiveness.

Data governance vs Data management

To mitigate these consequences, the necessary efforts required for data quality are typically addressed as part of data management, but then organizations often struggle to define and maintain consistent data governance, which again becomes a limiting factor. So, where do you begin?

First of all, data governance and data management should go hand in hand. Your data management efforts toward the execution of better data quality should be overseen just enough to continuously enjoy the effect of a prioritized data management component. 

Second, data governance is not a goal in itself. It is the support act for data management, which is driven by the business goals. Data governance makes sure we manage data with policies, stewardship and ownership, cultural change, strategy, principles and ethics, data valuation, and classification. 

Third, the effect of data management efforts is only as strong as the set governance. How much governance you need depends on a lot of factors and the appetite for risk.

Keep in mind that data governance is mainly a business matter. Business should be in the driver’s seat, and should embrace governance to maximize the value that data management provides. 

A aragmatic approach

A data governance approach aimed to bridge the data trust gap should be practical and have actionable strategies that are aligned with business data needs. It should ensure data quality, security, and accountability. 

Establish clear accountability and responsibility 

Data governance should formally assign responsibility and accountability for the prioritized aspects of data management. This includes defining roles and responsibilities and ensuring clear accountability for stewardship of the company’s critical data assets. Building and maintaining a culture of accountability is crucial for building trust in data.

Implement a Data governance framework 

A robust data governance framework should be rolled out. It should focus primarily on improving data quality and protecting sensitive data through modifications to organizational behavior, policies, standards, principles, governance metrics, processes, related tools, and data architecture. 

Foster collaboration between business and IT 

Business and IT must form a collaborative partnership with assigned responsibility and accountability for ensuring data quality and control. This collaboration is essential for solving immediate data quality issues and evolving into an organization responsible for monitoring and maintaining agreed-upon levels of data quality

Develop and implement Data quality standards 

Facilitate the development and implementation of data quality standards, data protection standards, and adoption requirements across the organization. This includes understanding the status quo of your data with profiling, defining indicators of performance and quality metrics, and ensuring compliance with data-related policies, standards, roles, and responsibilities. 

Continuous monitoring and improvement 

Establish mechanisms (for example, data quality improvement lifecycle) to monitor and measure compliance with data governance policies, procedures, and tools such as Microsoft Purview Data Governance. Implement corrections or improvements as needed with root cause mediation. This holistic approach of Plan, Do, Check, Act ensures that data governance is an ongoing process that adapts to evolving business requirements with the joint efforts of the data quality team and the business operations teams. 

Remember that:

• Data management efforts on data quality without a plan can be costly. An approach that has a direct impact on business needs is the best way forward. It is even the first principle on data quality and stimulates scoping on critical data. And yes, even critical data can have a scale of criticality. each.
• What you ask from your data is highly dependent on the quality of data that is available. Poor data quality directly affects the performance of AI and other data-driven systems.
• High quality data is subjective and depends on the needs of the consuming party.
• Common causes of data quality issues include lack of oversight, the data entry process, data processing functions, system design, and even fixing previous issues.

As digital demands evolve, so must the solutions that power them. Together with Microsoft, Sogeti is committed to helping you stay ahead with future-ready capabilities. Connect with us to explore how this partnership can accelerate your journey toward digital excellence.