Do You Trust Your Data Dashboards?

Trust your data dashboard


The data dashboards are a mere reflection of the underlying data. It is indeed amazing to look back at how far along we have come in the data age. Decision-makers today rely on data far more than we ever did in the history of mankind. Picture this: a staggering 91% of companies say that data-driven decision-making is important for their growth!

Renowned statistician and engineer W. E. Deming once famously said, “In God we trust; all others must bring data.”

Since data is so crucial to success, that brings us to a mission-critical question:

Can you trust your data?

Unfortunately, underlying problems with your data can remain hidden for years. It is not unusual for businesses to blindly trust their data only to discover severe discrepancies later on. In fact, 57% of businesses get to know about their data issues only when it’s reported to them by their customers or prospects. 

In this article, we lay bare a proven way to restore and embed trust in your data and data dashboards.

Let’s get the ball rolling!


7 Ways to Restore and Embed Trust in Your Data Dashboards

Data Quantity vs. Data Quality.

The More The Merrier? Not necessarily.

In our chase for more data, we sometimes forget to focus on the right kind of data.

Interestingly, studies show that in most cases, 75% of the information is all we need to make a decision. Hence, more data is not always the answer to our data and decision-making challenges. In fact, beyond a threshold point, more data hardly helps – especially if the data quality is compromised.

This sentiment was also echoed in Gartner Data & Analytics Summit 2022, where a major takeaway for the audience was the following.

 “We don’t need lots of data. We need the right data, the data that makes us smarter.”

The golden rule of data dashboards is that bad data inevitably leads to poor insights, no matter how sophisticated the modeling is. Therefore, we at Verb take data quality very seriously – right from day one.

But how to know for sure whether your data is reliable or not?

The simplest way to go about this is to start by asking the right questions about your data. Below, we list a comprehensive set of questions to dig deeper into your data problems. As a SaaS-focused, no-code dashboard builder, these are the very questions that we find answers to through our product daily. We are confident that these questions will help you determine the trustworthiness of your data – be it internal or client data.

#1 Are your data dashboards accurate?

Data dashboards and their accuracy is a result of error-free data – that is, data that represents the real world exactly as it is. Studies show that human error leads to more than 60% of dirty data. Plus, communication lapses cause 35% of data inaccuracy problems. Often, the users analyzing the data are different from the ones using the insights for decision-making.

The impact of data inaccuracy is staggering. In healthcare, inaccurate patient data could mean misdiagnosis that turns out to be fatal. In business, inaccurate data on consumer preferences could mean launching a product that is doomed to fail from the start.

The good news is that ROI on data projects can be increased by up to 2X simply by ensuring data accuracy. Verb simplifies the end-user experience by involving the right users in the right data operations at the right time. By managing the way various data stewards interact with the data, Verb ensures trustworthiness. Everyone can trust the reports, metrics, and insights that are used by them in decision-making.

#2 Do your data dashboards act as a single source of truth?

Data dashboard accuracy issues get compounded when multiple departments or user groups define the same data in different ways. A common example of this is the array of challenges that businesses face when measuring Key Performance Indicators (KPIs).

When a user looks at the reported KPI values in each period, the following questions often arise:

  • Have these values been arrived at using the correct and updated data?
  • Who has specified the way in which the system has calculated these KPIs?
  • What is the underlying method or formula that has been used?
  • Do the method and approach match our requirements?
  • Has anyone verified if the numbers reported here are accurate?
  • Have they been calculated in the same way across all the reports generated in the organization?

Since the data user who is viewing the KPI report is usually different from the one who has created data dashboard, such questions are bound to crop up. Further, the one who has created the KPI is not necessarily the one who has developed the data pipeline, and so on.

IT/data teams within an organization act as technical stewards of the data. They are more familiar with technical aspects of the data like schema and data structures as compared to non-technical users. However, non-technical users act as business stewards of the data dashboard and are equally important. They have a much better understanding of the data dashboard context, purpose, and underlying logic behind the KPI. If the KPI reports don’t act as a single source of truth for both technical and business stewards, then confusion and lack of trust are inevitable.

To mitigate this challenge, Verb ensures that every data and every insight from data dashboard is uniformly presented to every user in the organization. Verb acts as the single source of truth for all your data. While users access only the right data at the right time, the underlying data definitions are centralized and standardized. Verb ensures uniformity by setting up a single, universal data management core for each client. This way, it leaves no room for any unwanted data variability in reports or other shared data experiences.

#3 Is your data adequate?

Truth be told, in most cases, inadequate data = useless data.

Your data tells a story. That story is incomplete if various pieces of the puzzle are missing. Imagine a restaurant having data on customer orders only for 17 days of a month. Or imagine the same restaurant having the customer order data only for the beverages.

It would wreak utter havoc on their decision-making! Data adequacy helps in building reliable data dashboards.

Obviously, data inadequacy is more complex than just that. But you get the drift, right?

Research indicates that 27% of revenue in the U.S. is wasted on inaccurate or incomplete customer and prospect data. Incomplete data is a nuisance for IT/data teams and data tools alike. While there are modern technologies like synthetic data generation to make up for data inadequacy, they have not proven to be much effective. Verb solves this problem by incorporating checks and balances that catch and report inadequacies on the go. The data pipeline is designed to have automated fail-safes that don’t let inadequate data make it to the next stage.

#4 Is your data consistent?

Let’s say that you take a random sample of your data and find it to be accurate, noise-free, and adequate.

Great, right?

Not necessarily.

If your data changes its value or behaves differently at different times or in different usage instances, then it is inconsistent. The sample you picked randomly may be grossly misleading in that scenario. Therefore, data inconsistency makes it harder to spot problems with your data dashboard. Different users can look at the same data having different values or behavior patterns but have no clue about it, for example.

Verb takes a centralized approach to distributed data. This means that the same data instance is simultaneously reused across multiple data operations. This way, data inconsistencies are a thing of the past. Even with multiple data sources, seamless data integration eliminates the risks of data inconsistency.

This brings us to our next point.

#5 Is your data integrated?

Data silos are notorious. They can cause glaring discrepancies in data that is otherwise clean and consistent.

Today enterprises must deal with a vast number of data sources that reside both within and outside the organization. Often, a single data entity has multiple attributes that are ingested from different sources. Things fall apart if data from these sources are not aligned and integrated. Data dashboards and analytics models are bound to crumble. This is because real-world data is not just rows and columns that float in a vacuum. It is rich, interconnected, and interrelated. Hence, data integration is the way to go.

Not surprisingly, Data Aggregation is ranked as the second top challenge of using customer data for better audience targeting. Furthermore, more than 80% of Business Operations leaders say that data integration is critical to their ongoing operations.

Verb collects, collates, analyzes, and makes sense of data across all sources. It comes bundled with world-class features like data cleansing, pre-processing, data transformations, and de-siloing. Hence, it always ensures spotless data quality and seamless data integration across all sources.

#6 Are your data dashboards updated?

Time can make even the best data obsolete.

Data is moving and changing faster than ever before. Obsolete data can make or break businesses. In investment decision-making, for example, using data that is just a few minutes old can have devastating consequences. Given this, the recent buzz around real-time analytics is not surprising. By 2025, nearly 30% of data generated will be in real-time. In fact. 77% of respondents say that the lack of timely data has cost them business opportunities.

Our pre-configured, no-code data dashboard for SaaS products matches the speed of data operations to the pace of data generation. Data can be automatically updated at source in real-time, at all times. This way, SaaS companies can provide accurate and timely data experiences to their customers without being limited by data obsolescence.

#7 Is your data traceable?

Believe it or not, your data is a living, breathing entity. And it is on a long, twisted journey. Much like a river that originates in the mountains and meanders its way into the sea.

A significant aspect of data trustworthiness is your ability to trace its journey right from the source to the final destination. Along the way, data passes through multiple processes and gets accessed and manipulated by various users. If your datasets are not traceable, data breaches will go unnoticed and unaddressed. This will amplify your compliance burden as well, which can be a major risk for businesses.

Data Traceability has 3 major components – data lineage, data cataloging, and metadata management.

Verb makes use of these 3 features in a synchronized and well-orchestrated manner that requires no manual intervention. This way, it always ensures complete data traceability. It helps you to streamline your data policies, data compliance, data glossary, and data dictionary. It also enables you to implement them seamlessly across the data environment. Furthermore, our end-to-end metadata management helps to create a uniform data language across the entire organization.

Interested to know more about how Verb can help you achieve the optimum data quality? Eager to serve your clients with smart, trustworthy, and market-winning data experiences?

If you want a tour of our newest features get in touch!