In a technology-driven industry, developing a solid strategy is essential to success. Organizations with clear, well-structured data approaches can better protect sensitive information and unlock the full potential of their machine learning (ML) models.
A thoughtful strategy ensures data is accessible and aligned with business goals, resulting in more reliable insights and faster, smarter actions. It also builds a stronger security framework by addressing compliance, access control and governance from scratch. Most importantly, it provides consistent and high-quality information to train powerful ML models that can drive innovation in various departments.
1. Underestimate data governance and security
Ignoring compliance, access controls, and data ownership puts companies at serious risks beyond technical issues. In 2024, the average cost of breaching for U.S. companies reached $9.36 million, highlighting the cost of plans.
When security is not prioritized, businesses will be vulnerable to attacks, internal threats and non-compliance with regulations. Weak strategies often leave gaps in how sensitive information is stored and protected. This is why security and governance frameworks are built into your organization’s strategies starting from day one. As ecosystems grow, they ensure accountability, transparency and resilience.
2. Collect data without a plan
Not all data is valuable – collecting all data without a clear plan creates more problems than a solution. When organizations try to collect every possible data point, they end up with a sea of chaotic systems, higher storage and security costs, and an unrelated ocean of information that is difficult to navigate. In fact, data professionals spend 80% of their time finding and preparing information, rather than analyzing it or generating insights.
This slows down the analytical workflow and weakens the machine learning model by introducing noise and unnecessary features. Strong strategies should focus on quality rather than quantity – prioritize relevant, well-structured data that directly supports organizational goals. By narrowing down what really matters, teams can work faster, smarter, and safer.
3. Unable to define clear ownership of data
Confusion about who has it quickly when data roles and responsibilities are not yet clearly defined. Lack of accountability can lead to quality inconsistency and delay in decision making. Without a clear chain of ownership, teams may work repeatedly or ignore critical errors, affecting everything from reporting accuracy to machine learning outcomes.
This is why clear roles must be identified early in the strategy. Assigning dedicated butlers helps to ensure that everyone knows who is responsible for managing, verifying and maintaining the integrity of critical data assets. Clear ownership allows teams to collaborate more effectively and keep the process going smoothly.
4. Ignore business goals
The inability to align data plans with clear business goals is an expensive mistake that can consume time, money and momentum. When teams do not have a clear purpose for projects, they usually invest heavily in their efforts to keep the needle. Companies often focus on squeezing short-term customer revenues rather than using insights to build better, lasting relationships. This is especially common in the consumer goods market, where the company is 1.7 times more likely.
Strong strategies should always be linked to measurable results – improve customer retention, reduce risk or increase operational efficiency. Starting from the end, you can ensure that each dataset and model answers meaningful business questions and provide real value.
5. Skip data quality check
Machine learning models and analytics are only as good as the data that powers them, making quality a non-negotiable priority. About 80% of information organizations collect unstructured, so the risks associated with messy input are higher than ever. Inconsistent formats, duplicate entries or missing values can easily undermine the accuracy of the model and make decisions based on flawed insights.
Even state-of-the-art algorithms can be difficult to realize value when trained in unreliable data. This is why it is crucial to implement regular verification and cleaning processes as part of a strong strategy. Clean, accurate and timely information ensures that the model performs best and the analysis reflects the actions that real-life leaders must take.
6. Ignore the right stakeholders
When a strategy is formulated in isolation, it often misses the mark by ignoring the actual needs and insights of the people who rely on it every day. Real-world success depends on the input of the entire organization – data scientists, engineers, compliance teams and business leaders bring unique perspectives that can help shape a more effective, realistic approach.
Ignoring such collaborations can create expensive blind spots, especially when it comes to cybersecurity, with 68% of security leaders saying the talent shortage puts their companies at greater risk. Programs involving technical and non-technical stakeholder programs allow businesses to establish comprehensive, scalable strategies that align with the broader goals.
Build smarter people from the beginning
Organizations should take the time to review their current strategies and identify any gaps that are of quality, safety or aligned with business goals. Fixing these blind spots early creates a stronger foundation for future growth and more reliable results.
The 6th post was first seen on DataFloq 6 common errors.
