What to Do When You Can‘t Trust Your Business Reporting Data
As a business leader, you‘re under constant pressure to make fast, informed decisions that drive growth and profitability. But what happens when you suspect the data guiding those decisions is incomplete, inconsistent, or just plain wrong?
Poor quality data is a pervasive problem that affects organizations of all sizes. In fact, research from Gartner shows that the average company loses $12.9 million per year due to bad data. IBM estimates that poor data quality costs the US economy $3.1 trillion annually. And a study by MIT Sloan Management Review found that only 3% of companies‘ data meets basic quality standards.
Untrustworthy business data is all too common, but that doesn‘t mean it‘s harmless. Flawed data leads to flawed choices that can erode revenue, productivity, and customer satisfaction. If your sales forecasts are overly optimistic due to dirty CRM data, you might make inventory and hiring decisions that drain cash. If your marketing reports contain duplicate or bogus leads, you might waste budget and annoy prospects with irrelevant outreach.
The longer you wait to address data quality issues, the more your business risks falling behind the competition. Organizations that consistently trust their data are 3 times more likely to significantly outperform their rivals according to Accenture. Leaders in data trust also make better decisions faster and enjoy higher employee and customer satisfaction.
But getting to a place of data trust is easier said than done. Legacy processes, disconnected systems, and misaligned teams all contribute to inconsistent and unreliable business reporting over time. The good news is that no matter how messy your data is today, you can steadily improve its accuracy and integrity by taking a systematic approach. Here‘s how:
Diagnose Your Data Quality Issues
Start by taking an honest look at the current state of your business data. Gather a baseline by auditing the information feeding your key reports and dashboards for completeness, validity, consistency, accuracy and timeliness.
Completeness
- What percentage of records are missing critical data points like email, phone number, or product details?
- How far back does your transaction and interaction data go? Are there any major gaps in your history?
Validity
- Does quantitative data fall within expected ranges? Are there any obvious outliers that warrant investigation?
- Do key fields adhere to standardized formats and picklists? Or is information entered haphazardly?
Consistency
- Do you have conflicting data about the same customer or product across different systems?
- Are you using the same metrics and calculation logic to track KPIs over time?
Accuracy
- If you spot check data values against outside sources, do they match up?
- When you manually total up a row or column of figures, do you get the same result as your BI tool?
Timeliness
- How quickly is source data captured and synced to your reporting tools after key actions take place?
- Are you making decisions on stale or outdated information because systems aren‘t refreshed frequently enough?
For a deeper dive into data quality dimensions, check out this helpful primer from Precisely:
(Source: https://www.precisely.com/blog/data-quality/what-is-data-quality-and-why-is-it-important)
In addition to technical analysis, gather qualitative input from data creators, stewards and consumers throughout your organization:
- Interview frontline employees about their confidence in the data they use to do their jobs
- Survey business analysts on the most error-prone reports and dashboards
- Conduct workshops with functional leaders to understand data-driven decision making bottlenecks
Identify Root Causes of Data Mistrust
Once you‘ve baselined the quality of your business data, it‘s time to dig into the underlying drivers of bad data at your organization. Some common culprits include:
Lack of data governance
Without a framework for ensuring data is captured and used properly across its lifecycle, quality will naturally degrade over time. Set up a data governance council with cross-functional leadership representation to:
- Define critical data elements and quality KPIs
- Document data capture and management processes
- Resolve data issues and approve process changes
- Drive adoption of data quality tools and best practices
- Coordinate data literacy and training initiatives
Siloed systems and teams
If different departments are using disconnected systems and spreadsheets to track overlapping information, your data will quickly get out of sync. Inconsistent metrics and "multiple versions of the truth" will erode trust.
Invest in a master data management (MDM) platform to create a single, authoritative view of your business data. Integrate core systems via API wherever possible so data flows seamlessly.
Unclear roles and responsibilities
Confusion over who owns different data sets, who has the rights to edit records, and who is accountable for data quality leads to gaps and inconsistencies.
Document a RACI (Responsible, Accountable, Consulted, Informed) matrix clearly spelling out data stewardship roles. Embed data quality KPIs into performance goals and reviews.
Poor data entry hygiene
If employees aren‘t trained on why data quality matters and how to properly format information, you‘ll end up with a mess of invalid, incomplete and inconsistent records.
Roll out data entry guidelines and checklists for each core system. Train all users on data quality expectations for their role. Implement data validation rules and self-service data cleanup workflows to catch issues at the source.
Lack of data literacy
When business users don‘t understand what data is available, where it comes from, and how to effectively analyze it, they‘ll make decisions based on faulty assumptions or gut feel.
Prioritize data literacy by providing company-wide training on topics like:
- Data analysis and interpretation best practices
- Data visualization and storytelling techniques
- Self-service reporting and business intelligence
- Evaluating the quality and source of data sets
Implement Processes To Improve Data Quality
With a solid understanding of your current data quality challenges, you can start putting guardrails in place to prevent bad data from entering your systems and reports. Some key steps:
Standardize data definitions and formats
Create a central business glossary that clearly defines all of your core data entities, metrics, and dimensions. Specify required formats and picklists for each field to eliminate ambiguity.
Example:
- Customer: Person or business with whom you have a selling relationship, identified by a unique account ID
- Sales Revenue: Gross revenue from product/service sales. Calculation = (units sold * unit price) + shipping + taxes. Excludes refunds, discounts.
- Region: Sales territory where customer is located. Standard values: North, South, East, West, International
Implement data validations
Use form field validations, conditional logic, and regular expression pattern matching to enforce data quality at the point of entry. Automate checks for proper formatting, range and uniqueness.
Examples:
- Email must contain "@" and "."
- Phone must be 10 digit number
- Order quantity must be greater than 0
- Discount percent must be between .01 and .99
Dedupe and cleanse data
Run automated data cleansing flows on a regular schedule to standardize formatting, fix invalid values, and remove duplicate records. Tools like Trifacta, IBM InfoSphere, and Talend can streamline the process.
Deduplication logic examples:
- If company name and address are the same, merge leads
- If first name, last name, and email match, merge contacts
- If product ID is the same, merge catalog entries
Enrich data with third-party sources
Boost the completeness and accuracy of your business data by integrating trusted third-party sources. Services like Dun & Bradstreet, ZoomInfo, and Clearbit can fill in missing firmographic details and verify existing fields.
Data enrichment use cases:
- Populate missing contact details like phone, email, title
- Add industry, revenue, and employee count to accounts
- Append geolocation data to enable location-based analysis
Set up data quality alerts
Proactively surface data quality issues by setting up automated alerts and exception reports. BI tools like Tableau, PowerBI and Looker make it easy to track data quality KPIs and spot anomalies.
Example metrics to monitor:
| Metric | Definition | Trigger |
|---|---|---|
| Record completeness | % of records with all required fields populated | When ratio drops below 90% |
| Data freshness | Time since record was last updated | When 25%+ of records are 30+ days stale |
| Anomaly count | Number of records with values +/- 3 standard deviations from mean | When count exceeds 5% of total records |
| Duplicate percentage | % of records that are duplicates | When ratio exceeds 5% |
(Example data quality KPI and monitoring table)
Rebuild Trust in Reporting
As you put the people, processes and technology in place to improve your foundational data quality, you‘ll gradually earn back trust in your business reporting outputs. To accelerate this trust:
Implement data lineage tracking
Use a data lineage tool to automatically map the flow of data through your systems, from ingestion to insight. Understanding data provenance helps validate the source of reporting and identify root causes.
Expose confidence scoring
Give business users more context on data and insight trustworthiness by displaying confidence scores in your dashboards and reports. Incorporate dimensions like data completeness, consistency, and age into an overall trust rating.
Example confidence scoring methodology:
| Dimension | Excellent (3 points) | Moderate (2 points) | Poor (1 point) |
|---|---|---|---|
| Completeness | <5% of records missing data | 5-10% of records missing data | >10% of records missing data |
| Consistency | <1% variance between sources | 1-5% variance between sources | >5% variance between sources |
| Age | All data <1 day old | All data <7 days old | Data >7 days old |
Confidence Score = Sum of points across dimensions
- High confidence: 8-9 points
- Medium confidence: 5-7 points
- Low confidence: 3-4 points
(Example trust scoring rubric and methodology)
Spotlight data success stories
Combat lingering data mistrust by celebrating examples of data driving positive business outcomes. Regularly collect and share stories like:
- Insights that led to cost savings or revenue gains
- Decisions that reduced risk or sped time to market
- Predictive models that drove efficiency or automation
Democratize data access
Empower employees to explore trusted data on their own to cultivate a culture of data-driven decision making. Self-service BI tools like ThoughtSpot and Sigma make it easy for business users to securely access and analyze governed data without SQL knowledge.
The Payoff of Trusted Data
Taking control of your data quality is hard work, but the juice is well worth the squeeze. With a foundation of accurate, consistent and reliable business information, you can:
- Make confident decisions aligned to strategic priorities
- Uncover hidden insights about customers, products and markets
- Empower all employees to find answers to business questions
- Predict future outcomes like demand, churn and profitability
- Innovate with AI and automation to create breakthrough products and experiences
The road to data trust is a journey, not a destination. But every step you take to understand, improve and promote your business data quality is a step towards the many rewards of a truly data-driven organization.
