Australian businesses recognise the significance of data in making decisions, but first and foremost you need to trust what the data is telling you. However, according to research the majority of Australian business leaders suffer from Decision Distress and still don’t believe they have trustworthy data and the right policies in place to support decision making. To build trust in data and analytics, data quality, data integrity, transparency, data governance and clear communication must be prioritised. Given this, we thought we’d share some of our learnings and best practices in how to build trust in business data and analytics.
Does Your Business Data Have Trust Issues?
Given the power that data holds, trust in data and analytics should be a non-negotiable business priority, unfortunately Data Scientist & best-selling author Seth Stephens-Davidowitz, has found that Australian business leaders more so than any other in the world, are suffering from Decision Distress. In fact, 82% admit the sheer volume of data and their lack of trust in data has stopped them from making any decision at all, while a large majority regret, feel guilty or question a decision they made in the past year.
Now the underlying cause of this can be due to a number of reasons, such as siloed, ungoverned data, struggling with too few resources to effectively deploy analytics (that’s 1/3 of businesses according to most research) or other factors such as data literacy issues, cultural challenges, lack of a single data platform or single version of the truth or inefficient processes and practices.
Whatever the cause, when we talk about having trust in your business data this is about having confidence that your organisation’s data is clean, reliable consistent and up-to-date to deliver insights and inform business decisions. It’s important as it drives informed decision making, forms the basis of strategic planning, is essential to get right for compliance and risk management and to maintain a strong corporate reputation. It also obviously can drive operational efficiency, innovation and competitive edge for businesses. The problem however, is that it’s hard to deliver confidence and trust in your business data. It has lots of moving parts and requires a company-wide effort of business and technical teams working together on an ongoing basis.
6 Ways To Build Trust In Your Business Data
The key to delivering trust in your business data is to ensure best practice is followed at each stage of the data and analytics lifecycle. To build trust in data and analytics, prioritise data quality, transparency, and clear communication. Establish robust governance frameworks, ensure data security, and actively engage stakeholders to foster a culture of data integrity. Here are a six key best practice tips based on our experiences that can help businesses build trust in their business data:
1. Assess Your Trust Gaps
- Building trust in your data should start with assessing your data and analytics readiness and maturity level so you know where to focus attention. An assessment should be made of Leadership and Commitment, Business Engagement, Skills and Competencies, Data Readiness, Technology and Operations and Structures.
2. Build Data Quality & Integrity
- Data cleaning & validation: Regularly clean and validate data to remove errors and inconsistencies.
- Data lineage: Track the origin and transformations of data throughout its lifecycle to ensure transparency. This starts with sourcing data from verified and reliable systems, ensuring consistency across all reports.
- Data governance: Develop a clear, structured data governance framework outlining data ownership, standards and procedures for data collection, storage, access and usage.
- Metadata & business logic: Incorporating data provenance features, such as tooltips, metadata and business logic not only add context to data to improve understanding and usability, but also explain where data comes from (data sources), its limitations and update frequencies.
- Data security: Implement strong data security and privacy measures. Additionally, implementing role-based access controls ensures that sensitive data is only seen by authorised users, while automated alerts for data anomalies help maintain confidence in the reports. Use techniques like data masking or anonymisation to protect sensitive information.
3. Be Transparent & Communicate
- Document data sources: Documentation should be readily available to clearly explain where data comes from and its characteristics.
- Share methodologies: Explain the analytical techniques used to generate insights.
- Communicate limitations: Be open about potential biases and uncertainties in the data.
- Data visualisation: Present findings in clear and understandable visuals and interactive dashboards. Clear labelling, intuitive visualisations, and avoiding information overload help users interpret data accurately. If using self-service dashboards ensure a governed library of measures for users to access with a governed glossary of terms and metrics. Provide business testing/UAT for both data and the User Interface (UI).
4. Engage Stakeholders Effectively
- Strengthen leadership commitment: Is your data and analytics program supported wholeheartedly by senior leaders – critical for the success of any initiative. If not, demonstrate the value data can deliver to the organisation through a Proof Of Concept exercise in a small area of the business.
- Involve users early: Collaborate with decision-makers from the project scoping phase to understand requirements and incorporate feedback. Identify and assign clear data and analytics roles and responsibilities to team members. If you need guidance on how and what these should be check out our ebook. Involve stakeholders in the data governance process to build trust and ensure alignment.
- Data literacy training: Educate stakeholders on how to interpret and use data effectively. Underscore the value of data and analytics by linking data driven decision making to compensation, rewards and recognition.
- Feedback loops: Establish mechanisms for users to provide feedback on data quality and analysis results.
5. Ensure Business Alignment
- Connect to business goals: Does the organisation have widespread consensus on the value of data as a strategic asset and does the program support short-term and long-term strategic directives? Ensure data analysis is directly linked to strategic objectives and measurable outcomes.
- Prioritise impactful insights: Focus on delivering actionable insights that drive business decisions. Integrate data, analytics and insights into daily workflows at the point of decision making eg. weekly sales meetings.
- Measure success metrics: Track the impact of data-driven initiatives and demonstrate value within the organisation.
6. Review For Continuous Improvement
- Regularly review data quality, governance and security processes and update methodologies as needed.
How A Cloud Data Platform Builds Trust In Your Data
A modern cloud data platform is essential for building trust in business data by ensuring accuracy, consistency, and integrity across the organisation. By centralising data from multiple sources into a unified, secure environment, it eliminates silos and discrepancies, enabling a single version of the truth. Advanced governance features, such as data lineage tracking, automated validation, and access controls, enhance transparency, compliance, and security. Real-time updates ensure data remains fresh and reliable, supporting informed decision-making. Additionally, as AI adoption grows, a trusted cloud data foundation is critical to providing high-quality, unbiased data for accurate insights and predictions, preventing errors that could undermine confidence in AI-driven decisions.
Make Trust In Data & Analytics A Core Value
Trust underpins everything we do as companies, as people and as a society. Organisations need to start by creating a solid foundation of trust within their data and analytics so that when the time comes to accelerate, they can do so with confidence. Strengthening trust in business data is a continuous endeavour at each stage in the data and analytics lifecycle and should span the entire business. From the sourcing and preparation of data through to the outcomes and measurement of value. There are no perfect answers to driving trust however there are best practices and practical examples that all organisations can consider and adopt as we highlighted. So, start with the basics, assess your trust gaps, clarify and align goals, increase internal engagement, build your expertise to best practices, encourage transparency and be innovative. Ultimately, make trust in data and analytics a core company value.
