Thought leadership
April 3, 2023

9 common signs it's the right time for data reliability

There's never a bad time to invest in data reliability. But if your business model depends heavily on data for decision-making, it's absolutely vital. Without it, you may not find the path to streamlined customer experience and revenue generation. So here are some common signs it's time to invest in data reliability.

Liz Elfman

There’s no specific milestone that you should hit before investing in data reliability. From the name alone, "data reliability," you can infer that it's a great thing to have. Especially if you depend heavily on data to make decisions, data reliability is crucial.

But like so many things, data reliability is easier named than implemented. So many factors serve as blockers: from complex and dynamic data pipelines, to lack of visibility and governance, to human errors and biases, to insufficient tools and processes.

How do you know if your organization needs to invest in data reliability, and how should they go about it? Here are nine common signs that indicate it's time to take action:

1. Nobody trusts your internal analytics/dashboards

Lack of trust in your analytics and dashboards is a telling sign: you need data reliability. If your executives doubt the reports, that doubt trickles down throughout the entire organization.  Whether it's because they've been burned before, or because the numbers aren't saying what they expected, trust in data is easily broken. But with data reliability measures in place, faith in the numbers is restored. Your teams can confidently move forward with data-driven directives.

2. Your engineers and data scientists ignore most of the data alerts they get

If your engineers and data scientists receive too many alerts about potential data issues, they'll grow desensitized. Too many false positives or trivial alerts bring about an alert fatigue problem. Your data alerts should be meaningful and timely, so that you can quickly detect and resolve any errors. If they're not, you risk a "boy who cried 'wolf'" situation, where a real problem goes overlooked.

3. You had an incident impact your customer-facing ML models

Customer-facing incidents precipitated by data issues are one of the most painful (yet common) ways that an organization realizes that they need more reliable data. Now that many companies are running ML models to give real-time recommendations, the stakes are higher than ever for the underlying data. Your ML models should produce accurate and consistent predictions. If they don't, you expose yourself to potential losses or damages over that faulty data.

For example, imagine if your ML model for setting customer credit limits used a data source that went to zero for several weeks in a row due to a pipeline bug. You might drastically reduce credit limits without a valid reason, causing rejected purchases and unhappy customers.

4. Your data quality initiatives keep failing

You've launched data quality initiatives with the best intentions, but they keep failing, costing more than you thought, or getting blocked. Common reasons for that include a lack of clarity and a lack of alignment between different stakeholders. If your data quality initiatives have felt nebulous and ineffectual, data reliability can tie the investment to measurable metrics like NPS scores, and to business outcomes.

5. You have a huge number of duplicate tables

If you have a huge number of duplicate tables, it's generally because people don't know where to find data, so they reinvent wheels. What follows are inconsistencies and inaccuracies across key metrics, rippling throughout the organization. Invest in data reliability to establish a single source of truth for your data, reducing confusion and errors.

6. PMs are unable to answer simple questions to inform product choices in a timely manner

To see whether you need to invest in data reliability, you can have your PMs run a simple test: have a newly onboarded PM answer some simple analytics questions, such as how many users are using a certain feature, how often they use it, or what impact it has on retention or revenue. If they can’t answer the question in a reasonable amount of time, it’s a clear sign that there are data reliability issues in the organization. Your product managers should be able to leverage reliable and timely data insights to make informed and effective product decisions. If they can't, then they will miss opportunities to innovate, optimize or pivot their products based on customer feedback or market trends.

7. It's someone's job to "babysit" the data pipeline

If it's someone's job to "babysit" the data pipeline or to manually debug data discrepancies, it's a sure sign your pipeline isn't reliable. It takes up valuable time and resources that might be deployed towards other data engineering projects. It's also unlikely that the babysitter can debug every single issue, which means that data issues inevitably get dropped. By investing in data reliability, organizations bring more rigor to the babysitting process. Rather than reacting to data issues, you proactively detect and resolve them. Rather than debugging data issues one-by-one, you correlate them.

8. You deliberately schedule the data pipeline to run on Fridays so engineers can debug on the weekends

Organizations have been known to schedule data pipeline runs for Fridays, so that errors may be debugged over the weekend. Like having someone babysit the data pipeline, this is a coping mechanism for the lack of data reliability. In an ideal world, your data should be ready for consumption at any time, so that you can deliver fresh and accurate data to stakeholders on demand. If you can't, you're compromising data quality and timeliness, and putting unnecessary pressure on your engineers.

9. You are planning an IPO

Once your company goes public, you're required to file accurate and auditable data reports on a regular basis to meet various regulatory standards. If your data is unreliable or inconsistent, you face legal risks and reputational damages from potential errors or misstatements in your filings.

How Bigeye can help

If any of these signs resonate with you, invest in data reliability with Bigeye. Bigeye's data observability platform helps you monitor, measure, and improve your data reliability across your entire data stack. That means you can:

  • Automatically discover and catalog all your data sources
  • Track and validate key metrics for data quality, freshness, distribution, lineage, and more
  • Detect and alert on any anomalies or errors in your data pipeline
  • Drill down into root causes and remediation actions for any data issue
  • Generate comprehensive and customizable reports on your data reliability status and trends

Take control of your data reliability today.

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Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
Data analyst
Business analyst
Data/product manager
Total cost
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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