Top 5 des problèmes qui peuvent ruiner la confiance dans vos données

Stratégie   |   Shane Remer   |   28 nov. 2023

When people don’t trust the data they use, it can lead to poor decisions. Not only that, but a lack of trust in data can lead to a lack of confidence in the people and teams responsible for managing it.

It’s a problem.

This lack of trust can come from many different areas. We’ve identified the top five reasons for you and provided a few quick tips on how to fix them.

1. Dirty Data

Dirty data comes in many forms. From misplaced decimal points and manual data entry errors to missing information and mixed formats, dirty data can have devastating effects.

For example, one wrong digit or a misplaced decimal can impact a credit loan application decision and even lead to regulatory fines.

While many errors in data happen due to manual processes, human error isn’t the only reason these mistakes happen. Sometimes, they’re also due to a lack of a data governance framework.

This issue is a common obstacle that organizations face, and, without regular audits to identify inconsistencies, the problem can only intensify. What’s more, it can compound as organizations scale data operations.

The silver lining is that developing the right strategy and implementing consistent measures can quickly resolve most of these issues.

One way to do this is to establish a comprehensive data governance framework. You might also employ real-time data validation tools to catch errors as they occur. Doing this creates a safety net that guards against data inaccuracies.

The goal is to build a culture that prioritizes data accuracy and integrity. You can set this culture in motion by doing the following:

  • Adopting real-time data validation tools
  • Conducting regular data accuracy audits
  • Automating repetitive analytic processes
  • Establishing a data governance committee
  • Implementing standardized data handling protocols

2. Inconsistent Data Formats

Imagine this scenario: You’re in a meeting where a sales department is discussing the effectiveness of a campaign from the last quarter. One person says the campaign yielded average results, increasing sales by 4.2%. Another person disagrees and says the campaign yielded gains of 50%. Who’s right?

In this scenario, it’s both — but also neither.

For example, if you calculate the percentage using the total sales amounts down to the dollar, the yield is 7.55%

  Q2  Q3  Growth 
Sales (Rounded to 100,000) 

 

 

$2.4 Million 

 

$2.5 Million 

 

4.17% 

Sales (Rounded to Million) 

 

 

$2 Million 

 

$3 Million 

 

50.00% 

 

Sales (Actual) 

 

 

$2,362,762 

 

$2,541,192 

 

7.55% 

Although the data is error-free in this scenario, the reporting format is inconsistent. And while this representation is exaggerated, the effect of inconsistent data formats isn’t.

These inconsistencies impact more than the bottom line, too. They can also affect the credibility and reputation of people and their teams.

Fortunately, this can be easy to fix, especially with automation and a well-defined analytics reporting process.

Simple things you can do to solve this issue include:

  • Setting clear data standards across all platforms
  • Automating repetitive processes
  • Embracing data integration technologies
  • Prioritizing data training sessions for staff
  • Ensuring regular audits of data sources

3. Incomplete Data

Whether due to software glitches, gaps in data collection, or overlooked details, incomplete data often leads to misinformed conclusions.

For example, suppose you received a quarterly report missing a month or even a week of data and used that report to make forecasts or budgeting decisions.

And those decisions lead to poor results.

While you can point to the data and say what you were using was incomplete, you wouldn’t be likely to trust the data again.

For these reasons, the absence of data can be just as misleading and disastrous as dirty and inconsistent data.

While some gaps in data may come from outside your organization’s control, there are proactive approaches you can take to help mitigate or even eliminate the issues of incomplete data, such as:

  • Implementing stringent data collection procedures
  • Ensuring data sources are reliable and frequently updated
  • Regularly backing up data to prevent accidental losses
  • Investing in tools that highlight missing data points automatically

4. Outdated Data

Even if your data is error-free, complete, and shared in a consistent format, none of that will matter if the data is no longer relevant.

Using outdated information to forecast, plan budgets, or make decisions can lead you to miss out on current opportunities or misjudge the market.

While retaining historical data for specific analyses is essential, ensuring that the data you use to make decisions is current and up-to-date. In many instances, the data must be processed in near real-time, such as when companies purchase supplies, materials, and resources for their business operations.

To ensure that your data sources and the repositories people use to access them are updated regularly, here are a few things you can do:

  • Implement automated data refresh cycles
  • Automate data prepping and cleaning processes
  • Establish a routine review process to identify and purge stale data
  • Speed up processes by running them using your cloud data warehouse

5. Non-Compliant Data

Even when people trust that their data is high-quality, consistent, and transparent, they still might feel unsafe using it.

There are many reasons for this feeling, but one of the main concerns is the fear of using data in a way that violates one of the thousands of regulations encompassing data use.

No one wants to be responsible for costing their company millions, and, as the saying goes, it’s better to be safe than sorry.

Robust security measures, strong data governance, and effective data privacy policies only go so far in making employees feel safe using data.

The good news is organizations can take many actions to increase their employees’ trust in using data.

One is to refine data policies and security measures continually. Another is to provide data privacy training. They can also invest in platforms and tools with safeguards that automatically filter an organization’s data into safe-to-use sources housed in a centralized repository.

The goal should be to remove the burden of responsibility from the people who use the data and instead place it into the hands whose job it is to manage it.

While this is a complicated process, here are five things any company can do to achieve all of this:

  • Draft transparent data usage policies
  • Adopt the latest encryption techniques
  • Regularly update security measures
  • Provide training and workshops
  • Establish safe data repositories

Conclusion

The main areas that ruin trust in data are common issues that all companies face at one point or another. However, with the right strategy and platform, companies can turn their data into the foundation of their decisions — and reap the value that comes with it.