The Quality Question: How to Improve Resource Data Management

Data governance isn’t the hottest topic, but it’s fundamental to both resource management and business success. 

Yet our research found that half of you feel your data isn't reliable enough to help you plan projects, allocate resources, or manage capacity. And that’s a big problem.

While it isn’t your fault that your organization’s data isn’t up to scratch, it is in your circle of influence. So let’s fix it together – with our step-by-step guide to improving resource data management.

• How does your data confidence measure up?
• Why is reliable resource data essential?
• Why don't resource managers trust their data?
• Step-by-step guide: How to improve your resource data management
• How can you validate and continually improve data quality

How does your resource data confidence measure up?

Runn’s State of Resource Management 2025 research found that only 10% of you have complete confidence in your data. And nearly the same number don’t trust their data at all. 

This is one of the biggest blockers to resource management and business success – see why below – and deserves attention at the highest levels within your organization. 

• 10% wholly trust their data – Chef's kiss 😘
• 47% say their data is mostly accurate – Great start 👍
• 35% somewhat trust their data – Not ideal 🫤
• 8% don’t trust their data at all – Oh dear 😱

This means that two-fifths of you are operating with such low data confidence that it's a significant risk to your resource management decision-making abilities – and therefore business success. 

As for the (almost) half who have mostly accurate data: they still have room for improvement to reach the highest level of RM maturity and impact. 

But don’t worry. By the end of this article, you’ll have the strategies you need to join the top 10% who positively glow with complete data confidence.  

Why is reliable resource management data essential? 

Trustworthy resource management data drives better allocations, higher forecasting accuracy, right-time-right-person hiring decisions, and more. All those data-driven decisions you make to improve business performance and profitability. Every. Single. Day.

When you can’t trust your data, that’s a massive obstacle: hampering your ability to do your job effectively, deliver on your resource management KPIs, or quantify the value of resource management to a sometimes skeptical C-suite.   

This translates into sub-par performance – both of the resource management function and the organization overall. 

• For the business, this risks reputational damage, reduced demand, and falling revenue.
• For your team, it may mean less buy-in, lower engagement, and even budget cuts.

Together, these can create a downward spiral of resource management investment, influence, and impact – reducing project success and business sustainability overall. 

Why don’t resource managers trust their data? 

There are several reasons why resource managers can’t or don’t trust their data. 

• Decentralized data ownership – Data lives under different teams like HR, Finance, and Delivery, making it hard to get a single source of truth.
• Inconsistent collection and classification – Skills, roles, or other data are defined differently, so reports don’t align. 
• Siloed, inaccessible data – The information exists, but it’s  locked away in spreadsheets or systems that don’t talk to each other.
• Out-of-date information – Data that isn’t collected or synced regularly becomes worthless in fast-moving environments like yours. 
• Unfit systems – Inadequate tools make it difficult to access, analyze,  and act on your data with confidence.

These issues are supported by our research, which finds that outdated tools and lack of visibility are the top two inhibitors of resource management effectiveness, closely followed by silos and fragmentation.

But here’s how you’re going to fix it in your organization.

How can you improve resource data management? A step-by-step guide

1. Audit your current resource management data 

If you’re at a lower level of resource management maturity, the first step may be to understand what resource management data you need to collect and why. This will help you identify any gaps in what you’re currently collecting. 

Identify:

• What you collect and where it’s kept (eg, teams and tools)
• What’s duplicated and where
• What’s missing and why

2. Do data triage

Once you know what resource management data you have or haven’t got – or, perhaps, have got five times in different systems, using different definitions – identify your biggest problem areas and address these first.

For example:

• Do you have consistent skills data for every person on your payroll? 
• Do some teams track overall utilization, while others break it down into billable vs. non-billable utilization? 
• Are all projects monitoring schedule and budget variance?  

3. Centralize your resource management data

Disconnected data systems are the biggest barrier to accurate, trustworthy data.

To overcome this, implement a dedicated resource management platform that centralizes and automatically syncs your resource management data – making it easier to access and act on up-to-the-minute insights:

• Choose a system with data and analytics tools that make insights easier to get (see 6 below).
• Look for customizable dashboards, intuitive data visualizations, and in-depth reporting.
• Check for integrations with your other key people- and project-related tools.

4. Automate resource management data collection and updates

Manual updates are error-prone and impossible to scale. Use APIs and integrations to automatically sync data between systems.

For example, between your resource management platform, HR system, time-tracking tools, and invoicing application. 

Doing this reduces duplication of data and admin work, which cuts risk of mistakes and increases efficiency. It also improves data consistency, so you can compare like-for-like data points. And it keeps everything up-to-date, which is essential in a fast-paced environment. 

• Connect all key systems so updates happen in real time.
• Schedule regular automated checks for data consistency.
• Reduce reliance on spreadsheets and manual imports.
• Test integrations periodically to ensure accuracy and reliability.

5. Standardize resource management data taxonomies

‘Taxonomies’ means the agreed words you use to describe things in your data systems.

Without standardization — which is often the case — you might have software engineers listed as ‘frontend dev’ in one place and ‘web engineer’ in another. 

That makes it harder to find the right people for specific tasks, and risks poor allocations or project outcomes. Mismatched terms also distort reporting and analytics. If you’re assessing developer capacity, for instance, but not everyone tagged as a developer is included, your numbers won’t reflect reality.

• Create a shared data dictionary with agreed terms for skills data, roles, and proficiency levels.
• Use dropdowns or picklists in systems instead of free-text fields.
• Regularly review and update terms to keep them relevant as skills and roles evolve.
• Train team members on using the standard terms consistently.

6. Use analytics and dashboards

For faster, more confident decision-making, it’s important to make your data actionable. This is especially important when you learn that the average resource manager spends 7.5 hours a week on manually creating essential resource management reports. That’s a whole day a week!

Analytics, dashboards, and data visualization tools are a great way to find actionable insights fast. And they’re built into resource management platforms like Runn.

Keep reading: Check out Insights to learn more ➡️

How can you validate and continually improve resource management data quality?

If you follow the steps above, you’ll be well on your way to joining the 47% who are broadly satisfied with their resource management data. But how can you get into the exclusive 10% of highly confident data users? Through data governance best practices. 

Clean, centralized, and automated data is only as good as the people and processes that maintain it. Without regular care and attention, it can easily return to chaos – and your decision-making confidence will crumble too. 

Good governance needs: clear ownership, defined processes, a regular schedule, and ongoing validation. 

• Assign a steward for each critical data set – they’re in charge of validating it and making sure it’s up-to-date and still fit-for-purpose.
• Schedule recurring reviews of key datasets to maintain accuracy – to maintain accuracy and catch errors early.
• Establish a feedback loop where users can report inconsistencies or gaps – such as a simple Slack channel or online form.
• Implement staff training on data literacy and why it matters – to boost engagement, link it to team and individual benefits – such as more balanced and interesting workloads.

Improve your resource data management with Runn

Runn makes resource data management easy. 

• Centralized, standardized data – your single source of truth
• Trustworthy, live data – for confident decision-making 
• Automated analytics and insights – for agility and action
• Dropdown menus and taxonomies – for consistent data capture 
• Controlled access – to prevent unauthorised access or changes 
• Data visualization – for at-a-glance insights
• Customizable dashboards – that show the metrics that matter to YOU