In a recent Forbes opinion piece, the author argues that some executives are hesitant to move from experience-driven decision making out of concern that this shift will undermine their value. After all, the average executive has decades of experience in their organization, industry, or functional domains, and many may feel this experience will be negated as the data-driven transformation accelerates.
To alleviate these concerns and smooth the transition to data-driven decision making, the Forbes article stresses that organizations must build trust in data. Read on for more on how to do exactly that.
Step 1: Treat Data Accuracy as a Team Sport
The first step in engendering trust is making sure that all stakeholders in the data supply chain understand the critical role that data plays. This doesn’t just mean intelligence at the edge, clickstream data, or other digitally generated data; it means ensuring that the entire organization is aware of the importance of data accuracy. Think data entry into customer relationship management (CRM) systems, human resources (HR) platforms, or other workflows that require employees to frequently input information. These functions are often dismissed as corporate checkboxes, yet they play a key role in critical business decisions. Incentivizing people to enter this data accurately, completely, and in a timely manner lays the foundation for greater trust in data.
Step 2: Overcome the Limitations of Your Data Platform for Better Accuracy
Organizations must also eliminate restrictions on the volume of data needed for accuracy as well as the performance required for proactive actions. It’s not uncommon for data scientists or individual business units to build machine learning (ML)-powered projects using specialty platforms but only a sample or subset of relevant data. Whether this practice is due to siloed data environments or platform limitations, it’s important that companies overcome these restrictions. Building trust in data-driven decision making will be difficult unless you have a platform that can handle the performance and scale needed to look at all your data concurrently.
Step 3: Be Transparent and Replicate for Proven Results
Another critical step is ensuring that all ML-powered analytics and the actions companies take as a result of these insights can be replicated and proven. The Forbes piece cautions that in far too many situations, ML is considered a “black box” that people know very little about—sometimes even the data science team that built it. This issue understandably leads to distrust and fear of data-driven outcomes because it’s difficult to feel confident when you are accountable for something you don’t understand. As such, the ability to replicate, demonstrate, and articulate how decisions are made is a core requirement of data-driven decision making.
For more on these steps and how you can apply them to your business, check out the Forbes article in its entirety.