What would you say is the biggest challenge in managing
data across industries?

– One of the biggest issues is data quality. In my experience, poor data quality is the number one reason why data projects fail. I’ve seen it happen repeatedly across different companies and sectors. Many projects fail because the data simply isn’t accurate or consistent enough to deliver meaningful insights. This is especially true when organizations treat data quality as an afterthought, something that can be fixed later, rather than addressing it from the outset.

How do you define data quality, and why is it so critical
to success?

– Data quality measures how well data meets its intended purpose, often assessed by accuracy, completeness, timeliness, and consistency. For example, even if you have all the necessary data, it loses its value if it’s not current. Missing key details, like customer emails, can also hinder project outcomes. And let’s not forget consistency — when different systems don’t align or when there are duplicate entries, it creates problems.

Data quality isn’t just an IT issue. It’s a business-wide concern requiring collaboration between tech teams and business units. Without it, data often fails to reflect real-world business needs, causing project delays and failures.

Can you give an example of a project where poor
data quality led to failure?

– One notable example is when I worked on a predictive maintenance project, using machine learning, for the heavy vehicle industry. The idea was fantastic; if you could predict when a part was likely to fail, you could prevent downtime for a vehicle that’s out in the field, which is crucial in sectors like forestry, where downtime is costly. But despite having vast amounts of data, the quality wasn’t sufficient enough to make accurate predictions. After several attempts, the project had to be shelved due to inadequate data quality.

What steps should companies take to prevent data quality
from undermining projects?

– First, acknowledge that data quality must be prioritized from the outset. Identify and focus on critical data points to build a solid foundation. It’s also vital to assess data quality continuously, especially with new projects or systems.

Documentation is also essential. By recording both issues and validated high-quality data, future projects can leverage previously vetted data rather than starting from scratch.

How can companies ensure data quality when dealing
with large amounts of data across multiple systems?

– A governance framework is key. At one client, we implemented processes for regular data quality assessments, including automated KPIs to monitor accuracy, uniqueness, and timeliness. We also categorize data to prioritize what’s critical for the business. Transparency is essential; a data catalog can show data scientists and business users the quality status, such as indicating if a data point is only 80% accurate, helping them determine its suitability for specific use cases.

How does security fit into the data quality picture?

– Security and data quality go hand-in-hand. You can have the best data in the world, but it must be safeguarded. Secure data at all levels, ensuring sensitive data is encrypted and accessible only to authorized users. This prevents unauthorized access and preserves data integrity.

Also, many modern data systems, such as those that use data lakes, are unstructured. Then, it’s especially important to ensure privacy regulations are followed to prevent regulatory risks and data integrity issues.

Looking ahead, what trends do you see shaping
the future of data projects?

– One major trend is the shift to real-time data. Many companies are moving away from batch processing and want insights for immediate decision-making, but real-time data introduces complexity for both quality and security. Making decisions based on inaccurate real-time data can have disastrous effects.

Another trend is using AI and machine learning to assess data quality. Emerging tools can automatically check datasets for accuracy and flag issues before they escalate. Although this area is still evolving, it shows great promise.

Any final thoughts on data quality?

– Data quality needs to be embedded into the organization’s culture. It’s not just about tools or processes; everyone, from data scientists to business leaders, plays a role in ensuring high-quality data that supports the organization’s goals. At HiQ, we help businesses address these challenges so they can unlock the full potential of their data.

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