Business value metrics must directly align with organizational goals
There’s a lot of energy going into data infrastructure right now. AI, machine learning, automation, every one of these depends on good data. But too many organizations get stuck justifying those investments through outdated IT metrics. It doesn’t work. What matters at the executive level is whether the investment pays off, whether it drives real business value, reduces risk, or opens up new revenue channels.
To move the discussion forward, start measuring outcomes that align with what your business actually values. Time-to-insight is a good start, how fast can your teams turn raw data into decisions? Data ROI is even better, how much value are you getting compared to what you spend on storage, processing, and management? These numbers, especially when expressed in dollars, make things clear for boards and executive teams.
Yakir Golan, CEO and Co-Founder of Kovrr, said it well: reducing forecasted risk exposure by $2 million carries a lot more weight in the boardroom than showing uptime or ticket resolution. Srujan Akula, CEO of The Modern Data Company, offers a sharp point too, calculate data ROI the same way marketing tracks attribution. Link decisions back to the data that powered them, and show how those decisions drove performance.
Accelerating time to data and time-to-insight is essential for effective dataops
When you’re moving fast, whether building product, scaling operations, or launching into a new market, you need answers quickly. That means your data pipeline can’t move like a slow train. If your analysts need to wait a day to get a dataset or if teams are making decisions with yesterday’s numbers, you’re flying blind.
Time-to-data is a clear signal of how efficient your dataops really is. Cutting that time from days to hours, while still maintaining governance and security, puts you in a much stronger position competitively. You gain speed. You gain certainty. And your teams can pivot fast when market conditions shift.
Pete DeJoy, SVP of Products at Astronomer, puts this into focus. He highlights that when business units can access trusted data in hours rather than weeks, the value of investing in mature dataops becomes undeniable.
For C-suite leaders, this tells you something bigger than pipeline performance. It tells you whether your org is structurally agile. Tighten the cycle between collecting data, extracting value, and executing. When that loop is fast and reliable, you build a system that compounds insight over time, and your competitive edge gets sharper, quarter after quarter.
Stop tolerating delays. Tech that moves quickly and securely is how you scale up without friction. Speed is value.
Data trust scores, derived from quality and governance measures, are key to data credibility
If decision-makers can’t trust the data, they’ll ignore it. That’s a failure, of the system, of the process, and eventually, of the business strategy itself. Data trust is a hard measure of how effective your data pipelines, your governance policies, and your organizational processes really are.
Trust scores work because they’re composite. They pull in real signals, accuracy, completeness, consistency, and whether governance policies are being followed. You also want to gather signals around user confidence. Track sentiment through surveys. Review logged service desk issues tied to invalid or stuck data. If users flag a recurring problem, use it. That input is real, and it’s actionable.
One area that often gets overlooked is measuring how many datasets actually meet internal governance and security standards. A low number tells you where the holes are. A rising figure tells you your policies are written, implemented and adopted.
The more trusted your data is, the faster your teams will use it. That speeds up decisions without dragging in extra validation rounds or calls to IT. It means go-to-market timelines shrink, and customer interactions are driven by systems that reflect reality in real time.
Focus on quality and governance, then bake those into trust scores. What you get isn’t just cleaner data, it’s acceleration across the board.
Robust data governance requires clear data ownership
Data governance only works if it’s practical. Policies have to be applied, and people across the organization need to follow them, consistently. That starts with ownership. If data doesn’t have a clearly assigned owner, no one is accountable for its accuracy, availability, or classification. That opens the door to duplication, misuse, or outright compliance failures.
Pranava Adduri, CTO and Co-Founder of Bedrock Security, outlines this directly. He recommends tracking how much of your data lacks designated owners, how much has been classified, and how quickly users get access when they request it. These are measurable signs of whether governance is functioning.
Start by focusing on three areas: eliminate redundancy, reduce the time it takes to clean and prepare data, and drive clear ownership. From there, build in measurement of policy adoption across departments. This gives visibility into how teams actually use governed data, and what friction still exists.
Srujan Akula, CEO of The Modern Data Company, suggests tracking sensitive data exposure and duplication rates. These measure real governance gaps, and more importantly, signal organizational risk. Amer Deeba, GVP of Proofpoint DSPM Group, also pushes for transparency in adoption rates. If workers bypass policies to get faster access, governance has failed, no matter what the documentation says.
Effective governance is practical, measurable, and enforceable. When those three line up, business velocity increases, not just compliance scores.
Regulatory compliance and data sovereignty metrics are critical for mitigating risks in global data operations
Global operations are facing growing regulatory pressure. Compliance is no longer an afterthought, it’s a risk multiplier. To deal with it, you need data that’s traceable, secure, and provably compliant with jurisdictional rules.
This takes more than passing one audit. You need ongoing measurement. Start by tracking data lineage, where data came from, how it’s transformed, and who uses it. Look at how much of your sensitive data is exposed, whether by oversight or design. Then check your data locality, how much of your critical data is stored or processed in regions that align with legal requirements.
Jeremy Kelway, VP of Engineering for Analytics, Data, and AI at EDB, calls out key metrics: data exposure incidents, data lineage accuracy, and compliance scores tied to data locality. These are specific, enforceable, and necessary for building an infrastructure that works under regulatory scrutiny.
The stakes are rising. Governments are enforcing lineage tracking, sovereignty laws, and export limitations. Executives need dashboards that make this clear, how much data is within acceptable frameworks, and how quickly non-compliance is flagged and fixed.
If your organization trains AI with global data, this becomes even more relevant. You need to understand how data is flowing into the models, where it’s stored, who can access it, and how it’s processed. One misstep here can trigger serious legal and financial consequences.
So focus on metrics that reduce uncertainty. Use them to build a system where compliance isn’t just a checkbox, it’s an active signal of operational strength.
Cultivating a culture that embraces data security and governance through departmental KPIs
Achieving strong data governance and security comes from culture. When departments treat governance and security as shared responsibilities, implementation scales faster and with fewer bottlenecks. Without internal buy-in, even well-funded data programs stall.
Alastair Parr, Executive Director of GRC Solutions at Mitratech, recommends comparing adoption and compliance across departments. Set KPIs that reflect how fast teams assign data ownership, follow classification policies, and reduce access issues. When leaders see where their teams stand relative to others, accountability increases. Internal comparisons encourage attention to detail without forcing alignment through top-down mandates.
This also solves a common problem: inconsistent governance maturity across units. By tracking measurable performance through team-level OKRs, like time to classify data, policy enforcement rate, or incident-prevention scores, CIOs and CISOs can identify which departments are lagging and which are leaders. That creates a feedback loop, pushing every group to match the highest-performing ones.
For executives, these metrics help assess more than just compliance. They reveal how responsive and aligned the organization truly is. A department that treats security as someone else’s job adds risk. But when governance is visible, measurable, and embedded across the org, risk drops and agility increases.
Adoption won’t happen by accident. Build a KPI structure that promotes healthy pressure, shared accountability, and clear metrics. That’s how governance becomes part of execution.
Data security metrics anchored in established frameworks provide a comprehensive view of resilience
If you’re not measuring the right things in security, you’re guessing. That’s not acceptable, especially when you’re dealing with sensitive data at scale. Mature organizations track metrics defined by industry-accepted frameworks like ISO 27001, NIST CSF, or CIS. These models give structure and help ensure your security posture can stand up to real scrutiny.
Greg Anderson, CEO and Founder of DefectDojo, lays it out clearly: monitor actual security performance through measurable indicators. Track incident frequency. Know your mean time to detect (MTTD) and mean time to respond (MTTR). Capture how many vulnerabilities remain unpatched, how often teams complete security training, and what percentage of sensitive information is encrypted.
If your teams don’t know how long it takes to detect a breach, or how fast remediation happens, that’s risk. If end-users aren’t trained, or too much data is exposed to too many people, the system only appears to be secure.
Also track how much of your data has been cataloged and ranked by severity. This insight feeds both security enforcement and governance. It tells you where your exposure is, and where response resources should go first.
Executives need to know whether security measures are effective against active threats and compliant with regulatory requirements. Once visibility improves, performance improves. And in security, you don’t get many second chances.
So define your benchmarks using established frameworks, and then insist on clean, automated reporting. What’s measured can be improved, especially when the stakes are this high.
A unified, measurable strategy linking data initiatives with business outcomes is key
There’s no shortage of metrics in dataops, governance, or security. But if those metrics aren’t tied to real business outcomes, faster innovation, operational savings, or risk reduction, they lose strategic traction. Effective organizations are aligning cross-functional metrics to reflect core business objectives, and they’re doing it in a way that scales.
Kajal Wood, VP of Software Engineering at Capital One, outlines a comprehensive model to do exactly that. Her team looks beyond isolated technical KPIs. Instead, they measure quality, accessibility, and lineage readiness across the data lifecycle, while factoring in deployment agility, observability, and incident resolution. These are chosen not for their technical completeness, but because they drive real outcomes: innovation speed, improved decision-making, and reduced operational risk.
This unified approach works because it translates data operations into clear business signals. Data quality determines how actionable insights are. Deployment speed affects how fast new services reach customers. Lineage tracking shows you how trust and visibility are maintained across the system.
For executives, connecting these systems through shared KPIs ensures visibility at every layer, from the engineering trenches to the boardroom. But volume isn’t the goal. Starting with 3–5 metrics that consistently reflect impact will do more for strategy alignment than dashboards filled with noise.
The right question isn’t “Are we measuring this?” It’s “Does this metric explain how our investment improves precision, speed, or outcomes?” When the answer is yes, performance compounds. Data stops being an expense, and starts becoming a competitive asset.
Concluding thoughts
Data doesn’t generate impact on its own. It depends on how well it’s governed, how fast it moves, how clean it is, and how secure it stays. And if you’re not measuring those things with precision, and aligning them to actual business outcomes, you’re not leading the strategy, you’re reacting to its consequences.
For executives, the question isn’t whether to invest in dataops, governance, or security. That’s already settled. The real question is whether those investments are moving the needle in the right direction, on speed, on risk, on efficiency, on trust. Strong metrics give you that clarity. They separate theory from execution.
Track fewer numbers, but track the right ones. Make sure they’re understood across departments and tied to goals that matter. Because at this stage, data is a lever. And what you choose to measure defines how far you’re able to push.
