The Cost of Bad Data: Impact on Leadership KPIs

Bad Data and Its Impact on Leadership KPI’s

Bad data can have a negative impact on an organization’s Leadership KPI’s.

The relationship between business processes and bad data is no longer only focused on isolated errors; it has expanded to a broader definition including: reliability, consistency, and timeliness of the data available at the point of decision making.

The Hidden Costs of Bad Data

Many expenses incurred by an organization because of “bad data” cannot be reflected in a direct line item in their financial statements but rather are incurred from many different aspects of the organization (e.g., customers, sales, marketing, product development…) and the total impact of these costs will be absorbed by every aspect of the business.

Bad data will accumulate in organizations because it delays decision making within the organization. Because Leadership is spending time reconciling numbers they cannot concentrate on the decisions necessary to bring the organization success as a result of the delays created by bad data.

Bad data creates doubt within a company regarding the data available for the decision-making process and therefore creates situations where organizations take much longer to make decisions concerning the future direction of the business and subsequently lose opportunities for growth and profit.

The third way in which incorrect data has a financial impact on organizations is that it causes organizations to change their strategy more than they would utilizing correct data. Every time an organization has to change their strategy, they will incur a cost associated with re-working their strategy due to the fact that they discover incorrect data. Changing their strategy also creates delays in moving forward with their business strategy and results in a loss of opportunity for their organization, as well as additional costs to either clean up incorrect data or verify data accuracy.

Impact to KPIs

As mentioned previously, over the past couple of years, there have been many studies which support the idea that bad data impacts every KPI the same; however, some KPI’s are affected by bad data more than other KPI’s, and the amount which any particular KPI is affected is dependent on the amount of bad data within the organization and the actions taken to fix the bad data that created it.

Examples of 2 key KPI’s that could be impacted by bad data include Revenue and Growth:

Revenue and Growth key KPI’s are generally established based upon the organization’s definition as to what constitutes a product or service, as well as, the way that the organization has integrated their financial and operational systems. Some of the ways that an organization has been negatively impacted by bad data include:

• The reliability of forecasts is impacted.
• Revenue is either spurts of activity (i.e., massive ups and downs).
• There is often a disagreement between leadership on how to attribute product revenue which results in companies delaying critical decisions for investing in their revenue sources and growing their companies.
• Based on the inaccurate or incomplete data, leaders underinvest in profitable sources of product revenue and, conversely, they overcompensate in areas where the reported revenue is overstated and/or understated.

Bad data is classified as “bad” when there are inconsistencies between how the data is used and how it should be used.

Here are some common effects of the poor delivery of Data:

Feature Impact Confusion, Extended Adjustments to Product Roadmap, Non-Alignment of Product with Engineering, Investing Resources into Feature build that do not impact Results.

Operational Efficiency Metrics Affected by Bad Data

There are three Metrics in terms of Operational Efficiency: Cost, Utilization, and Reliability.

The degradation of data quality results in:

• Costs that go unidentified and are, therefore, never reported
• Inefficiencies as ‘the norm’
• Automation Projects being put on hold and/or discontinued
• Leadership being unable to generate Optimal Designs for an organization, or the inability to derive an Ideal Baseline

Sources of poor-quality Data include:

• The lack of consistent instrumentation, as Events occur without any rule sets
• Siloed Metrics where Multiple Definitions of KPIs create conflicting results
• Manual Data Handling with the use of spreadsheets or other one-off fixes that create errors
• Lack of Accountability for and Ownership of Metric Health
• Quality Checks not occurring until after a decision is made

Behavioral Impacts of Poor Data

As your organization continues to grow, the Challenges Associated with Poorly Delivered Data will continue to emerge.

Poor Data causes leaders to make decisions based not on well-defined Signals, but out of Fear and/or Subjectivity.

Therefore:

• Leaders will spend more time validating or questioning data
• There will be less trust in the Analytics function of the organization
• Execution will ultimately be impacted, even though Employees are fully capable of executing their tasks

Executive leaders must quantify bad data and its costs.

Leaders can monitor directional metrics such as Decision Timeliness, Roadmap Changes, Analyst’s Time reconciling Metrics, and Incident Reports stemming from bad data.

Examples of Return on Investment from Fixing Bad Data

• Agencies aligning KPIs Up to the Executive Level – Using standardized metrics, leaders minimize time discussing interpretations of metrics and reduce scheduling needs for meetings.
• Product Testing / Experimentation – Verifiable and high-quality data allows product teams to dismiss low-value features faster, conserving engineering resources.
• Customer Retention Programs – Quick reaction to unhealthy products/services enables proactive intervention using accurate health metrics.
• Cost Minimization – Clean usage data helps track cost inefficiencies that would otherwise be missed.

All four examples correlate poor data quality with lower effectiveness and lower action on the part of Leadership Teams.

Mistaken Methods of Fixing Bad Data

Common errors include:

• Buying tools to fix bad data without understanding definitions or ownership
• Over-centralizing control creating resistance and performance issues
• Treating bad data as a one-time project instead of continuous monitoring
• Failing to address Change Management and team perceptions

Leaders must adopt a balanced approach to managing and resolving bad data issues.

The Risk of Using “Bad” Data – Strategic Risk

When utilizing bad data at different points in the business cycle, risk to the company and Leadership increases.

Evaluate exposure when:

• Preparing for AI/advanced analytics
• Scaling across multiple markets/products
• Expanding into new regulatory environments
• Preparing to be audited or acquired

Leadership KPI’s must be consistent and substantiated. Bad data can harm credibility.

Executive Plan for Reducing “Bad” Data Costs

Executives can take actions without slowing down innovation. Key steps include:

• Define a limited number of Executive KPI’s – Focus on informed decision-making
• Assign ownership and accountability for bad data resolution – Document definitions and measurement methods; implement lightweight quality checks and ongoing Data Health checks
• Focus on achieving significant impact, rather than perfection.

Business Perspective

At Logiciel Solutions, we help Leadership Teams convert data from a friction-generating asset into a strategic asset.

Engineering teams focus on “AI First” and developing a network of trusted, reliable data.

When Executive KPI’s are Clean and Consistent, leaders can make faster, confident decisions.

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