Despite all the technological innovation over the past 30 years, we are still asking the same data quality questions. Is my data accurate, complete, consistent? No wonder the issue isn’t going away: Without high quality data, enterprises can’t be data-driven. And if enterprises are not data-driven, there are greater instances of inefficiency, missed opportunities, and ultimately, financial loss. And no enterprise—regardless of size—can afford to operate like that.
Gartner’s Data Quality Market Survey showed that the average annual financial cost of poor data is to the tune of $15 million. The survey also revealed that poor data quality practices undermine digital initiatives, weaken competitive standing, and affect customer trust. We also have seen that poor quality of data is now amplified when it’s reused in models and data products like reporting and analytics, and this can also exponentially increase the financial costs.
To help mitigate this, enterprises must start by listening to the most powerful voice in the room: the chief information officer.
Gone are the days when a CIO was mostly responsible for keeping the computers up and running. Thanks to AI, there has been a significant shift and CIOs are now a major player in strategy planning and moving projects beyond the initial pilot. As a result, these subject matter experts now have a bigger part to play with other C-suite members. According to Deloitte, 63% of CIOs are reporting to the CEO. And this recognition extends to pay as well. According to consulting firm Janco Associates, compensation for CIOs increased on average 7.5% [large enterprises] and 9% [midsize enterprises] from mid-2023 to mid-2024.
While this is a good indicator of the power that CIOs carry, this clout is only valuable if CIOs master the fundamentals of data quality while also prioritizing another key component: data governance. Let’s look at the role that data governance plays alongside data quality and four issues that will shape the CIO role over the next three to five years.
We’ve been encountering the same reliability issues with data for decades. These issues include completeness, accuracy, consistency, validity, uniqueness, and integrity. Thanks to AI, there are also new issues and one that has popped up in the past couple of years is an increase in real-time data pipelines. Previously, data was much more batched base. Today, it’s about dealing with real-time data that fuels reports and AI models embedded in operational processes like product recommendation engines. And as a result, CIOs are having to ask themselves whether these pipelines are ready and able to keep up.
The challenges do not stop with the quality of the data but rather extend to the people and processes behind the data. Unsurprisingly, more often than not, data quality issues arise from a people and process problem. When people lack reliable data, it percolates across the whole life cycle of the data. This is where data governance comes into play.
Typically, the Chief Data Analytics Officer (CDAO) is in charge of data governance. Today, there are multiple stakeholders that need to be involved—including the CIO—and this is the key to successful data governance. When it works, it’s because you have business and technical interests at play.
Now let’s look at the three issues that will shape the CIO role over the next few years.
Getting quality data
We know that AI uses a tremendous amount of data. When that data is inaccurate or poor quality, we get incorrect predictions and flawed decision-making—risks that are multiplied with AI helping to automate so many processes in today’s organizations. Accurate data is crucial for the models that fuel AI to function effectively. And it’s not just the data that matters; it’s how easily the data can be observed. Indeed, data observability means companies can be notified of quality problems and their causes before they affect the output of downstream systems.
Putting ethics first
It’s easy to forget that data is neutral when it comes to ethical decisions. As humans, we naturally have biases which is why it’s vital that CIOs create strategies that prioritize responsible data access and use. Who should have access to your data across the business? For what purposes can it be used? Define your data access and use policies, identify your data assets, and most importantly, implement a data and AI governance solution to manage and monitor data access and compliant use.
Keeping up with real-time data
CIOs are now dealing with real-time data pipelines that fuel reports and AI models for training and operational decision-making. This is a full-time job in itself, but CIOs are no longer just computer experts; they now have to be business experts. They now have to know as much about what’s going on across the business as every other C-suite leader.
It’s a juggling act—and it’s only going to get harder thanks to the acceleration of AI and the amount of reliable data these models need to function.
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