Overcoming the barriers to becoming a data-driven organisation: Common challenges and practical tips for success

1. The reactive mindset: from firefighting to forecasting

Many organisations still take a reactive approach to managing their day-to-day operations. Instead of using data to predict trends and optimise performance they rely on their ability to react quickly to daily challenges. This reactive approach often results in missed opportunities, inefficiencies and customer dissatisfaction – especially during peak periods.

The solution: Shifting to a proactive mindset requires integrating predictive analytics and real-time monitoring. Businesses that invest in monitoring and forecasting tools and embed data-driven thinking into daily operations can anticipate challenges rather than constantly putting out fires. For instance, by implementing predictive analytics for automation, it is possible to forecast when components are nearing the end of their lifespan. This proactive approach helps to prevent critical areas from failing during busy periods. 

2. Unreliable data: rubbish in, rubbish out

Even when businesses attempt to be more proactive and data-driven, unreliable or poor-quality data can undermine their efforts. Inaccurate, inconsistent, or incomplete data leads to flawed decision-making and reduces confidence in analytics. This issue is often exacerbated by a lack of accountability—when employees don’t see themselves as stewards of data integrity, errors multiply.

The solution: Organisations need to invest in data governance, and employee training to improve and maintain data quality. When employees understand that good data benefits them directly, they are more likely to follow best practices. Assigning data stewards to oversee specific areas of data ensures that teams are held accountable for maintaining the accuracy of their data.

3. Lack of data ambassadors: driving cultural change

Despite having high-quality data, businesses struggle to embed a data-driven culture if employees aren’t engaged. Without champions advocating for data use, resistance to change slows progress.

The solution: Identify data ambassadors at all levels of the business. These champions can demonstrate how data-driven decision-making improves performance and efficiency. Coaching people to think about data in a user-friendly manner and celebrating small wins can go a long way in making everyone in your organisation become a data ambassador.

4. Siloed & non-standardised data: Too many versions of the truth

We often see businesses using multiple platforms, Excel, Power BI, WMS, emails, and more to collect, report and analyse the same data, leading to discrepancies and confusion. When businesses operate in silos, collaboration suffers, and teams waste valuable time reconciling conflicting reports.

The solution: Standardise and consolidate data into a single source of truth which is easily accessible. Businesses should streamline reporting, eliminate redundant reports, and establish clear communication channels to ensure every team works with the same reliable data.

5. People & Change Management: Navigating resistance and rmbedding lasting change

One of the most significant challenges companies face when becoming data-driven is the human element. Resistance to change is a natural reaction, particularly when employees are asked to abandon old methods for new, unfamiliar approaches. It’s common to hear teams give lip service to the idea of data-driven change, agreeing to the vision but failing to deliver on the commitment.

The solution: Implementing a structured people and change management framework is essential. This framework should guide leaders in fostering a data-driven mindset, ensuring the cultural shift is not just surface-level but embedded deeply across the organisation. By actively engaging employees, providing continuous training, and demonstrating quick wins, organisations can create lasting adoption of new data-driven behaviours.

6. Underutilised automation and systems

More businesses are investing in automation to support their operations. Most integrators offer built-in analytics software as standard, but clients fail to use them effectively. Having the right technology is useless without the proper processes, routines, cadence, and training to ensure these tools are used properly.

The solution: Businesses should also focus on adoption and integration when acquiring automation solutions. The systems gather a wealth of data that is often misunderstood or not used at all. A deeper understanding of the capabilities of the automation and systems in your organisation can unlock hidden value that can propel your growth journey. 

7. Drowning in data

On the other side, some businesses have too much data. They collect vast amounts of data but lack the ability to prioritise key insights, leading to analysis paralysis. Teams get stuck in endless reporting cycles, unsure of what really matters.

The solution: Businesses should identify the most critical KPIs and focus on extracting actionable insights from them. A well-structured data strategy helps teams extract actionable insights that drive performance rather than drowning in irrelevant numbers.

8. Capacity & prioritisation issues: Aligning data work with business goals

With limited resources, data teams often struggle to balance high-value insights with low-impact requests. Without clear prioritisation, they risk being consumed by minor reporting tasks rather than contributing to strategic decision-making.

The solution:  Businesses should align data initiatives with key business priorities. Simplifying the data pipeline to match the company’s strategic goals helps maintain efficiency and effectiveness. Frequent reviews of the pipeline ensure the backlog is prioritised accordingly and stays relevant.

9. Lack of change control: Managing data requests

Even with clear priorities, businesses often struggle with uncontrolled data requests. Data teams create ad-hoc reports to satisfy immediate needs, or “hot topics” only to be forgotten about or duplicated later, leading to data team burnout and more irrelevant numbers. Without a structured approach, businesses waste time responding to short-term demands rather than focusing on long-term business strategies.

The solution: A structured change control process—including a roadmap for data projects and a clear request management system—ensures that reporting efforts remain strategic rather than reactive.

10. Adapt to the changing landscape: Staying ahead of the curve

The use of data is increasingly becoming necessary for organisations to gain an advantage in a competitive marketplace. The availability of tools, models and agents like AI, Machine Learning, Natural Language Processing is improving and needs to be reviewed for use in your organisation.

The solution: A data driven culture does not begin and end with establishing a data team or adopting an IT solution. The behaviours and decisions of your people need to be driven by data on a continual basis. The use of these latest tools can be a gamechanger for your team in accessing and using your data to make insightful and timely decisions and make it seem less daunting.

Final thoughts

As businesses increasingly prioritise digital transformation and data utilisation, the challenge is not just collecting data but using it effectively. A strong data-driven culture requires clear leadership, standardisation, coaching, and strategic alignment. The companies that succeed are those that embed data into their DNA, making it the foundation of every decision.

If your organisation faces any of these challenges, now is the time to take action. Which barrier will you tackle first?

To find out how Hatmill can support you in becoming a data-driven organisation, please contact us or find out more here.