How to build trust in data-driven decision-making

Data is every organization’s goldmine. Whether it’s to monitor the performance of strategic initiatives or find solutions to business problems, good data can open up a plethora of possibilities. Utilizing data to support decision-making can provide organizations with a competitive edge, enhance operational efficiency, and reveal new pathways for growth. This method, which involves employing facts, metrics, and measurable objectives to guide business choices, is known as data-driven decision-making.

There are countless examples of how organizations have benefited from leveraging data to guide their decisions. Netflix used play data and subscriber ratings to produce a hit series, House of Cards. Coca-Cola analyzed customer data to create hyper-targeted ads and improve marketing efficiency. However, the picture is not all sunshine and roses.

Every organization must also be cognizant of the risks associated with data-driven decision-making. Poor data quality may result in incorrect recommendations, while biased data could produce discriminatory or unfair policies. More broadly, technological errors might generate flawed decisions. Therefore, building trust in the processes used for data-driven decisions is crucial. This blog post delves into methods of cultivating trust and mitigating risks when utilizing data for decision-making. 

In decision-making involving data, technology plays a pivotal role. Specifically, organizations process their internal data through various models to derive outputs that underpin their decisions. Thus, the challenge of building trust in data-driven decision-making fundamentally involves ensuring these models consistently yield accurate and reliable results. Let’s delve deeper to explore the diverse methods by which we can accomplish this.

Good data is a prerequisite for any model to work effectively and produce reliable outputs. However, the quality of data in a system can be compromised when it is formatted incorrectly, mislabeled, or duplicated. This risk is particularly high in large organizations that deal with multiple sources and substantial volumes of data.

Organizations can employ various strategies to ensure that the quality of their data stays high, like standardizing data entry and creating data quality dashboards. Most importantly, they need to clean their data periodically, a process that entails correcting errors and eliminating corrupted records within a dataset to enhance its quality. This process includes removing duplicate entries, correcting structural errors, addressing missing data, and conducting quality assurance (QA).

When Machine Learning (ML) models are used to make decisions, it is crucial that these models don’t turn into ‘black boxes’—they need to be explainable. That’s exactly where model transparency and model interpretability come into play. 

Model transparency refers to the ability to explain the inner workings of a model, including its structure, equations, parameters, and assumptions. This clarity enables one to grasp the general functionality of the model while also accessing more detailed technical information when necessary. Documenting every phase of a model’s lifecycle is crucial to maintaining its transparency. As your model continues to evolve, it is essential to monitor its performance and behavior, checking for any anomalies. Additionally, soliciting feedback on your model from a diverse group of stakeholders, including data scientists, developers, domain experts, and others, is imperative.

Model interpretability, on the other hand, is the extent to which a human can understand a model’s predictions and outcomes. Model interpretability is particularly crucial in highly regulated industries such as banking, finance, and insurance, where a model’s use for decision-making may be restricted unless it is highly interpretable. Several techniques can be used to assess and enhance a model’s interpretability, including feature importance analysis, Local Interpretable Model-Agnostic Explanations (LIME), and Grad-CAM.

Before deploying any model into production, it is necessary to verify that the model functions as intended. Model validation involves evaluating the accuracy, reliability, and performance of the model, often by testing it on independently sourced datasets distinct from the data used during its development. After a model has been deployed, model monitoring, which entails having continuous oversight of the model’s performance, allows you to verify whether the deployed model is working as expected.

Extensive model validation and continuous model monitoring make your model outputs more reliable by:

• Detecting model drift early on before it can have a significant impact on a model’s outputs.
• Identifying anomalies and outliers in datasets so that these values don’t skew a model’s performance.
• Providing insights and supporting evidence that form the basis of the decision to retrain a model.

Data-driven decisions based on models whose performance has deteriorated over time can often lead to financial losses, regulatory risks, and various other issues.

Let’s now examine some of the best practices that organizations seeking to implement data-driven decision-making can follow:

• Creating a data-driven culture: A data-driven culture is one in which every employee in an organization understands the necessity of data for their work and actively contributes data that will be utilized downstream. Through regular training that reinforces this mindset, organizations can expect a significant improvement in data quality.
• Regularly update data and models: Organizations must keep their data sources in sync to maintain consistency across systems and minimize errors. Model retraining should be carried out when a model is deemed to be no longer fit for purpose.
• Conduct data audits: Organizations must conduct periodic data audits and reviews to verify that the data within the organization is indeed of high quality. 
• Establish data governance: Organizations must establish guidelines regarding the use of their data. These guidelines should cover all aspects, such as what data is collected, where it is stored, and who has access to it.
• Ensure regulatory compliance: Regulatory standards like the SR 11-7 and SS1/23 lay out the best practices for model documentation, model validation, model monitoring, and governance. Organizations adhering to these guidelines minimize their risk of making bad decisions from faulty models.

After everything we’ve discussed so far, you might feel daunted by the task of initiating data-driven decision-making. However, there’s no need to worry; you don’t have to do it alone! You can rely on a Model Risk Management (MRM) platform like Yields to assist you.

Yields is an award-winning, adaptable technology that can help you in the process of building a truly data-driven organization. It ensures that the quality of your data stays intact, and that your models are transparent and interpretable, as well as enabling validation and continuous monitoring of your models. Let us see how.

Data is sourced from the official data sources to minimize manual error-prone extractions. It is standardized in terms of its structure and content, and then processed via script-based transformations. The models and algorithms can thus make use of this clean, high-quality data to provide accurate results. Moreover, Yields historicizes data with its full lineage to allow for tracing back and reproducing results.

The Yields MRM platform ensures model transparency by capturing all important, up-to-date information and making it visible to all stakeholders in a centralized manner at all times. It documents the lifecycle of a model via the automated generation of documentation to include various types of content including technical data (e.g., equations), as well as evidence of qualitative and quantitative assessments (e.g., test results). When it comes to model interpretability, Yields leverages well-known techniques that are either open source or proprietary to the customer.

Workflows in the Yields MRM platform allow you to manage the end-to-end execution of a model, including validation and monitoring. These workflows are fully configurable from the UI to capture the required level of granularity of steps, in line with internal governance. You can perform a quantitative analysis in an automated and reproducible manner using routine tests that can be executed with the required frequency based on events (e.g., new data available), or due date triggers. In case of any anomalies or issues, notifications are sent for investigation or deeper analysis.

Begin your organization’s journey to making data-driven decisions by booking a demo today!