Hurry Up and Slow Down
In today’s fast-paced business environment, there’s often pressure to deliver insights quickly. This is very common in today’s business world. As a data professional, I have needed to handle these situations for almost all my assignments. There’s a bit of a dichotomy to this. I teach part time too. During my lectures, I will tell my students the importance of taking their time with data projects. Despite telling my students to take their time, I also let them know that they will not have this luxury in the real world. Because of this pressure being put on the data professional from the business, there can be a significant temptation to rush through the analytics process without proper validation. However, this approach can have serious consequences. Without thorough validation, a data professional could be giving business leaders data that leads to unreliable insights and misguided decisions.
To mitigate these risks, organizations must prioritize validation as an essential step in the analytics workflow. This involves systematically reviewing and verifying data sources, methodologies, and assumptions to ensure the accuracy, reliability, and relevance of analytical outputs. Despite the push to finish quickly, data teams must prioritize data validation before deploying a solution to production. Data teams can help minimize the likelihood of errors and enhance the credibility and trustworthiness of their analytical insights.
The Fallacy of Superficial Insights
We live in a report and dashboard world. It’s easy to create visually appealing charts and graphs that seem to convey meaningful insights at a glance. However, without a proper validation phase, these superficial insights may lack depth and substance, leading decision-makers to draw faulty conclusions or overlook critical nuances in the data.
In order to avoid superficial insights, data professionals must go beyond surface-level analysis and delve into the underlying data and methodologies. This includes conducting sensitivity analyses to assess the robustness of results to different assumptions, performing statistical tests to validate the findings, and engaging sub to provide context and interpretation. By doing their due diligence, data professionals can ensure that the data and insights they are providing business users are accurate and actionable.
Building Trust Through Validation Processes
Trust is a cornerstone of effective decision-making. As the saying goes, “Trust is not given, it’s earned”. Without reliable and accurate data, trust cannot be earned. Business users do not trust your data may hesitate to act on recommendations or may even disregard your data altogether. Validations play a critical role in building trust by providing assurance that analytical results are based on sound data and methodologies.
To build trust through validation processes, organizations must be transparent and rigorous in their approach. This includes documenting validation procedures, assumptions, and limitations, as well as soliciting feedback from stakeholders to ensure that their concerns and perspectives are addressed. In my experience, getting stakeholder feedback is by far the best way to gain buy-in for an analysis. By demonstrating a commitment to validation and accountability, data teams can build confidence in decision-makers and stakeholders. This, in turn, builds trust in the data and help ensure that the analytical insights will be embraced and acted upon by business users.
Mitigating Risks and Avoiding Costly Mistakes
At the beginning of this blog entry, we briefly mentioned that consequences can occur when relying on bad data. These consequences can be far-reaching, ranging from missed opportunities to significant financial losses. Inaccurate or misleading insights can lead organizations down the wrong path. This results in wasted resources, damaged reputations, and, potentially, legal liabilities. Some examples are Purdue Pharma, Thalidomide Drug Scandal in the 1950s-1960s, and Growth Hormone Contamination Scandal of 1985. To mitigate these risks, data teams must take proactive steps to validate their analytical outputs and ensure their accuracy and reliability. Business users and leadership should embrace this step to ensure the insights they receive are accurate.
One approach to mitigating risks is to establish robust validation frameworks and protocols that govern the entire analytics lifecycle. This may involve implementing quality control measures to monitor data quality, conducting validation tests to assess the accuracy of analytical models, and establishing governance structures to oversee the validation process. One example that I favor is to establish periodic checks of reports. Depending on the report, usage, and business impact, periodic checks could be as frequent as monthly, quarterly, bi-annually, or annually. Establishing strong validation processes into every stage of your data team’s workflow will help identify and address potential issues before they escalate into costly mistakes.
Conclusion: Embracing a Culture of Validation
To conclude, validations are essential for ensuring the reliability, accuracy, and credibility of data analysis in today’s data-driven world. Prioritizing validation as a foundational component of the analytics process will help your data team mitigate risks, build trust among stakeholders, and drive better decision-making. From addressing data quality issues and validating analytical models to establishing governance structures and embracing a culture of continuous improvement, validations play a crucial role in enabling organizations to use data effectively. Through embracing a culture of validation, organizations can navigate the complexities of data analysis with confidence and clarity, driving innovation, and achieving sustainable success.
