“I'm not surprised that this company is struggling. The data is so bad that it's impossible to make informed decisions. I left because I didn't want to be a part of it.” In today's dynamic business landscape, the promise of a data-driven organization often falls short, leading to employee dissatisfaction and, in extreme cases, resignations. McKinsey's 2023 study underscores the prevalent issue, with 78% of employees expressing doubts about their organizations' effectiveness in using data for decision-making. My friend, Al, experienced this firsthand as the head of marketing. Frustrated by the prevalent "Building stories on fake data" culture, he eventually resigned. His journey highlights the broader problem of data misuse and misinterpretation, echoing Gartner's findings that 52% of employees feel decisions are often intuition-driven rather than evidence-based. The root causes of this problem are varied. Many organizations struggle with overwhelming data volumes, lacking the infrastructure and expertise to harness it effectively. Consequently, data remains siloed and inaccessible, hindering informed decision-making. Al's story, transitioning from a shoe marketer to a role promising data-driven innovation, emphasizes the critical importance of trustworthy data. His initial excitement turned to disappointment as he faced a lack of data trust within his team, leading to manual report generation and a subsequent breakdown in communication. Recognizing the need for change, Al sought external help. Despite initial optimism, the company's abrupt decision to revert to old reporting methods shocked everyone, resulting in a mass exodus from the marketing and data team and Al's eventual resignation. Al and I talked and came up with a set of questions every candidate should ask: Can you share examples of measures or processes in place to validate and maintain the accuracy of X-related data? How accessible is the X-related data for the X team, and are there any specific challenges or restrictions they encounter in accessing relevant datasets? Can you tell me more about the tools I will be using to access the relevant data, and how much manual work is involved in it? Are there established protocols for managing gaps in X-related data, and how is this information communicated to the X team? 5 Crucial Tips Al and I brainstormed about what he hoped to be shared with him during the interview, and we came up with five crucial tips: Don't overpromise a data-driven culture if it can't be delivered. Clearly communicate the challenges and commit to providing the necessary support for transformation. Transparent Communication: If data is currently unusable, define expectations for candidates. Be transparent about how it affects evaluation and communicate mid-term plans for resolving data issues. Set Clear Expectations: Address common data quality challenges openly. Discuss ongoing initiatives to tackle data silos, outdated data, and inconsistent formats—or honestly admit constraints and challenges. Acknowledge Common Challenges: Emphasize how accurate data is crucial for driving innovation. Share examples of how the company leverages reliable data for developing products, services, and business strategies. Highlight Data's Role in Innovation: Showcase any data literacy training or workshops available. This commitment to enhancing employees' data skills signals a proactive approach to handling data effectively. Prioritize Data Literacy Training: Al will find a job soon again; he is a talented person, but the experience of the past months had a large tool on his health and ability to function, or as we love to call it, “He had a burnout.” Try to avoid it by addressing the disconnect between data promises and reality is paramount for startup leaders. Prioritizing data governance, employee feedback, and data literacy can foster a more engaged workforce, enabling companies to make informed decisions, drive innovation, and achieve lasting success.

This story contains new, firsthand information uncovered by the writer.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Employees Are Leaving Companies Because of Bad Data

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