Poor data quality is not a minor operational nuisance. It is one of the most expensive and least acknowledged problems in modern business, and yet, 59% of organisations do not measure or analyse their data quality regularly. The problem is expensive, widespread, and largely invisible – until it isn’t.
The consequences surface downstream, rarely at the point of failure. A forecast that nobody trusts. A dashboard that two departments read differently. An AI tool that produces confident, plausible, and entirely wrong outputs. A board decision made on figures that have passed through four spreadsheets and three manual reconciliations before anyone saw them.
The five dimensions of data quality – and how to fix each one
Fixing your data is not a single project with an end date. It is an ongoing discipline, one that, once established, compounds in value over time. Gartner’s research shows that organisations with mature governance practices see 30% improvements in core financial performance metrics including EBIT, revenue growth, and operational efficiency. There are five dimensions that matter most.
1. Accuracy – Are your numbers actually correct?
Accuracy is the most fundamental dimension of data quality, and the one most commonly assumed rather than verified. In a finance context, inaccurate data most often arises from manual data entry errors, misapplied accounting codes, duplicate records in source systems, and figures adjusted in transit without any audit trail.
How to fix it: Start with a data profiling exercise. Profiling tools scan your datasets and surface statistical anomalies – values outside expected ranges, inconsistently populated fields, conflicting records. Use the output to build a heat map of data quality problems by domain, then prioritise remediation by business impact.
2. Completeness – Do you have everything you need?
Incomplete data creates blind spots that prevent accurate forecasting, proper cost allocation, and meaningful variance analysis. Common examples include vendor master records with missing tax codes, employee records with incomplete cost centre assignments, and transaction data lacking the dimensional attributes needed for reporting.
How to fix it: Define completeness requirements by use case, not by system. Ask what data each process needs to function correctly, then audit against that standard and establish validation rules that prevent incomplete records entering your systems. This is consistently the first barrier encountered in digital finance transformation programmes.
3. Consistency – Does your data mean the same thing everywhere?
Consistency is the dimension that causes the most visible problems. It is why two departments can look at the same underlying data and produce different figures. Inconsistency arises when the same concept is defined differently across systems – different chart of accounts structures, cost centre hierarchies, currency translation methodologies, or definitions of a “closed” sale.
How to fix it: Establish a business glossary – a single, agreed definition of every key metric and dimension. Fifty percent of analytics reports are never used by decision-makers because they do not trust the source data. The business glossary is how you earn that trust back.
4. Timeliness – Is your data fresh enough to be useful?
Even perfectly accurate, complete, and consistent data becomes a liability if it is too old to reflect the current state of the business. Month-end close processes that take two weeks mean the board receives a picture of the business that is already outdated by the time it lands. Manual data transfers introduce lag. Batch processing cycles mean operational data from yesterday does not appear in the planning environment until tomorrow.
How to fix it: Map your data flows and identify where lag is introduced. The biggest delays usually come not from core systems but from the manual processes connecting them, overnight ETL jobs, weekly spreadsheet updates, monthly report cycles produced because that is how it has always been done. Modern finance demands data that moves at the speed of the business, as explored in how FP&A professionals can stay top of their game.
5. Governance – Who owns the data and what are the rules?
Data governance is the framework that holds the other four dimensions together. Without it, data quality improvements are temporary. The problems come back because nothing has changed about the conditions that created them.
Good governance requires three things: clear ownership (every dataset has a named individual responsible for its quality), clear rules (definitions, validation requirements, and acceptable ranges are documented and enforced), and clear escalation (when issues are identified, there is a process for resolving them quickly).
How to fix it: Assign data ownership to individuals, not teams. Shared ownership is no ownership. Document the rules for your highest-priority datasets, the ones feeding your planning models, board reports, and external submissions. For organisations using Jedox, the platform’s integration and governance capabilities make this significantly more manageable, something covered in our analysis of Jedox’s BARC 2026 market leader recognition.
Where to start
Data quality can feel like an infinite problem. The key is to start narrow and build momentum.
Start with the data that matters most. Identify the three to five datasets that feed your most important decisions – your planning model, your board report, your cash flow forecast. Focus your initial effort entirely on those.
Appoint an owner before you write a rule. The first act of data governance is assigning responsibility. Pick a person, not a team. Give them authority to define the rules and accountability to maintain them.
Profile before you fix. A data profiling exercise gives you a factual view of where quality problems are concentrated and how severe they are. That evidence makes the business case for remediation and prevents effort being spread too thin.
Automate the connections. The most common source of data quality degradation is the manual handoff – the spreadsheet that sits between two systems, updated by a person, vulnerable to error, invisible to any governance framework.
Measure and report. Establish a small number of data quality KPIs for your priority domains and include them in regular reporting. For CFOs building this case internally, our piece on the real benefit of AI in EPM and FP&A provides useful framing for the board conversation.
The bottom line
The organisations that will get the most from the next generation of planning and AI technology are not the ones with the biggest budgets. They are the ones that did the unglamorous work first. They audited their vendor masters. They standardised their cost centre hierarchies. They documented their definitions. They assigned ownership and wrote the rules down. They built data pipelines that removed the human in the middle.
If you would like to understand where your organisation’s data readiness stands, speak to our team about a data health check. It is the fastest way to identify where your foundations need strengthening before your next technology investment.
