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There is a familiar pattern in many planning transformation projects. A company invests in a modern planning platform, builds sophisticated forecasting models, and improves reporting speed. Dashboards look impressive. Refresh cycles are faster than ever.
Yet six months later, executives are still questioning the numbers.
Sales forecasts do not align with operational plans. Inventory projections conflict with demand. Finance teams spend more time validating reports than analysing outcomes. The platform is working. The planning outcome is not.
At the centre of that gap is often bad data in planning. Many organisations assume planning failures are caused by weak forecasting methods, outdated processes, or software limitations. In reality, the root cause is often simpler and more structural. A sophisticated planning model built on poor-quality data does not create better answers. It creates inaccurate answers faster.
Poor data quality does not usually show up as a visible system failure. It spreads quietly through forecasts, reports, planning models, and executive decisions.
The cost is rarely labelled "bad data" in a finance report. Instead, it appears as:
When planning outcomes deteriorate, organisations often focus on fixing the planning process itself — while ignoring the underlying data issue.
Every planning process is a chain of assumptions. Revenue forecasts depend on sales data. Supply chain plans depend on demand signals. Workforce plans depend on operating assumptions. Financial plans depend on all of the above.
When source data is incomplete, outdated, duplicated, or inconsistent, the entire planning process becomes compromised.
The risk becomes greater in Connected Planning environments, where finance, supply chain, operations, and sales all rely on the same ecosystem. One inaccurate source can create a ripple effect across multiple business functions.
Consider a demand planning model. If customer demand data contains duplicate transactions, incorrect product mappings, or delayed updates, forecast accuracy drops immediately — even the most advanced models struggle when the inputs are unreliable.
Missing information is one of the most common causes of planning errors. A revenue forecast built on partially captured sales activity will almost always understate future performance. The issue is that missing data often remains invisible until planning outcomes are compared with reality.
Different departments frequently define the same metric differently. Sales may calculate revenue one way. Finance another. Operations a third. When planning models combine inconsistent datasets, executives end up debating whose numbers are correct instead of focusing on decisions.
Planning depends on current information. Yet many organisations still make decisions using reports that are days, weeks, or months old. This becomes especially damaging during periods of market volatility, when conditions change faster than planning cycles.
Duplicate records distort planning outputs in subtle but serious ways. Duplicate customers inflate demand forecasts. Duplicate transactions exaggerate revenue projections. Because duplicates often exist across multiple systems, they can remain hidden for years before their impact becomes visible.
Many organisations believe upgrading planning software will automatically solve planning challenges. It will not.
Modern EPM platforms improve connectivity, collaboration, and refresh speed — meaning they often expose data issues faster rather than hiding them. Platforms such as Anaplan, Jedox, OneStream, and BOARD can significantly improve planning visibility and operating cadence, but they cannot turn unreliable source data into trustworthy forecasts.
The principle still applies: Garbage in, garbage out. The real value of EPM software appears when it is supported by strong data governance, clear ownership, and consistent definitions.
Poor data quality creates a problem that extends beyond forecast accuracy. It destroys trust.
Once executives lose confidence in planning outputs, adoption declines quickly. Teams begin maintaining separate spreadsheets. Departments build their own reports. Decision-makers bypass the planning system entirely.
Scenario planning has become a critical capability for modern organisations. Market conditions shift faster than annual planning cycles can accommodate. Leaders need the ability to model multiple futures quickly and confidently.
However, scenario planning is only as reliable as the data supporting it. Poor-quality inputs produce unreliable scenarios. Leaders may believe they are evaluating possible futures when they are actually comparing different versions of inaccurate assumptions.
The result is false confidence rather than better preparedness — one of the most dangerous outcomes of bad data in planning.
Bad data is rarely caused by a single issue. Common causes include:
Most organisations experience several of these challenges simultaneously. This is why data quality cannot be solved through technology alone — it requires governance, ownership, accountability, and business-wide discipline.
Organisations with mature planning capabilities tend to share several characteristics:
Organisations looking to improve planning performance should resist the temptation to begin with technology selection. Instead, start with three simple questions:
If the answer to any of these questions is "no," a planning transformation should begin there. Technology can accelerate planning. Data quality determines whether planning is accurate. The most successful planning transformations focus on both.
Keansa helps organisations build trusted planning foundations for FP&A, Connected Planning, S&OP, and Supply Chain Planning initiatives.
That typically means starting with the data layer first — identifying critical datasets, defining ownership, standardising business rules, and setting up validation controls before scaling the planning model.
Keansa works across leading EPM platforms including Anaplan, Jedox, OneStream, and BOARD, helping mid-market and enterprise teams improve planning accuracy without treating technology as a substitute for governance.
When planning processes fail, organisations often blame forecasting methods, planning cycles, or software limitations. In reality, the root cause is frequently much simpler — bad data.
Poor-quality data distorts forecasts, weakens scenario planning, slows decision-making, and erodes trust across the organisation.
The organisations achieving the greatest value from Connected Planning, FP&A Transformation, and modern EPM platforms are not necessarily those with the most advanced technology. They are the organisations with the most reliable data foundations.
Because the quality of your planning process will never exceed the quality of the data that powers it.
Related Resources
Keansa helps organisations build trusted data foundations for FP&A, Connected Planning, S&OP, and Supply Chain Planning initiatives across Anaplan, Jedox, OneStream, and BOARD environments.
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