9 common signs it's the right time for data reliability
Investing in data reliability does not require achieving a specific milestone. The term "data reliability" itself implies the importance of having accurate and dependable data. For organizations that rely heavily on data to make decisions, data reliability is a critical component of their success.
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:
- Lack of trust in internal analytics and dashboards
If there is a lack of trust in your organization's analytics and dashboards, it may indicate the need for data reliability. When executives doubt the accuracy of reports, this skepticism can permeate throughout the entire organization. Whether this mistrust stems from past negative experiences or because the data does not align with their expectations, trust in data can be easily eroded. Implementing data reliability measures can restore faith in the numbers and enable teams to confidently move forward with data-driven initiatives.
2. Engineers and data scientists ignore most data alerts
When engineers and data scientists receive an excessive number of alerts regarding potential data issues, they may become desensitized to them. An excess of false positives or trivial alerts can lead to an alert fatigue problem. To ensure that alerts are meaningful and timely, it is important to quickly identify and resolve any errors. Failure to do so can lead to a situation similar to the "boy who cried wolf," where a genuine problem is ignored.
3. You had an incident impact your customer-facing ML models
Having an incident that impacts your ML models can be a painful and common occurrence, particularly when it affects customer-facing systems. This is often a wake-up call for organizations to recognize the importance of having reliable data. With the prevalence of real-time recommendations powered by ML models, the need for accurate and consistent data has become more critical than ever.
It is crucial for your ML models to produce reliable predictions, as any inaccuracies can lead to potential losses or damages. For instance, if your ML model for customer credit limit uses a data source that experiences a pipeline bug and goes to zero for several weeks, the credit limits might be significantly reduced without a valid reason. This can result in rejected purchases and unhappy customers, which can be detrimental to your business.
4. Your data quality initiatives keep failing
Despite your best intentions, your data quality initiatives have repeatedly failed, costing more than anticipated and encountering roadblocks along the way. These failures often stem from a lack of clarity and alignment among stakeholders. If your data quality initiatives have been vague and ineffective, it's crucial to establish data reliability by tying your investment to measurable metrics such as NPS scores and business outcomes.
By measuring the impact of your data quality initiatives against specific metrics and outcomes, you can gain greater clarity and identify areas for improvement. This will enable you to optimize your investments and achieve better results. Focusing on data reliability can help overcome common roadblocks and ensure the success of your initiatives.
5. You have a huge number of duplicate tables
Having a significant number of duplicate tables is usually a sign that people are unsure about where to locate the data they need, resulting in the recreation of existing tables. This can lead to inconsistencies and inaccuracies in key metrics that can spread throughout the entire organization. Investing in data reliability can help establish a single source of truth for your data, reducing confusion and errors.
By establishing a reliable and centralized source of data, you can eliminate the need for duplicate tables and ensure that all stakeholders have access to accurate and consistent data. This can help avoid inconsistencies and inaccuracies and enable better decision-making based on reliable data. With a clear and reliable source of data, you can streamline your operations, minimize errors, and achieve improved business outcomes.
6. PMs are unable to answer simple questions to inform product choices in a timely manner
To determine whether your organization needs to invest in data reliability, a simple test can be conducted by having a newly onboarded product manager answer basic analytics questions. For example, they could be asked how many users are using a specific feature, how often they use it, or what its impact is on retention or revenue. If the PM cannot provide a satisfactory answer within a reasonable amount of time, it is a clear indication of data reliability issues.
Product managers should have access to reliable and timely data insights to make informed and effective decisions regarding their products. Failing to provide them with such data can result in missed opportunities to innovate, optimize, or pivot products based on customer feedback or market trends. Therefore, it is essential to invest in data reliability to ensure that PMs have access to accurate data that they can use to drive business decisions.
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.
If any of these signs resonate with you, invest in data reliability platform which should help 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