How Poor Data Quality Negatively Impacts Your Organization

Every successful business organization today relies on data to make informed decisions that will result in high-value outcomes. At first, data management included manual collection by talking to customers in person or surveying them over the phone. Employees had to make entries manually, and data management work was minimal.

Today, organizations have sophisticated analytics and data management tools in place that can do these tasks relentlessly. Not only is data entry standardized, but the info collated is measurable and can assist you to derive meaningful conclusions and make significant decisions. Data can aid you resolve problems, improving processes, monitoring performance, resolve problems, and get a better understanding of the market. However, poor data quality impacts your business negatively.

Before we dig into the details of the impact poor data quality can have on enterprises, let’s see some general adverse effects that companies suffer. Did you know that poor-quality data can cost organizations up to $12.9 million annually?

Let’s look at the top 5 ways poor data can sabotage businesses.

1. It lowers productivity

Low-quality data can make your processes sluggish and save your team’s time. According to a Lead Jen study, unverified data can save up to 27.3% of a sales representative’s time. That works out to 546 hours annually. Why — because they’re calling customers on invalid numbers, they’re talking to people who no longer need the company’s services or spending their time deciphering patchy data. Poor data quality also limits the organization’s ability to automate processes.

Only when data is accurate and complete can it be trusted, and sales reps can automate calling from a list and focus on building customer relationships.

2. It lowers the profit margin

United Airlines flight tickets that usually cost hundreds of dollars were sold for around $5 for a brief spell in 2013, all because of a data error. Rather than lose their customers, the company had to honor the tickets and bear the losses.

Examples like this make it easy to connect data quality and its impact on revenue. There are other indirect implications as well. Getting customer demographics right could result in opening stores in suitable locations or misaligned promotional campaigns. Something as simple as having a wrong customer address can double shipping costs and waste the company’s profits.

3. It influences employee turnover rates

When companies fail to take data quality seriously, employees must manually sift through redundant data. It reduces their efficiency, demotivates them, and makes them feel undervalued.

Today’s skilled employees need more patience for slow processes. According to a survey, 37% of employees quit their jobs because they felt they weren’t valued or appreciated.

Replacing your top employees can take time, effort, and money. Resources must be spent on hiring as well as training. Unless you improve your CRM data quality plan, there’s no guarantee that you won’t have to repeat the process in a few months.

4. It widens the gap between departments

For a business to be successful, the different departments must function in sync with each other. Data quality plays a significant role in this. The marketing team may need more support when the delivery leads must be followed up. On the other hand, the sales team may find that the leads need to be more accurate and hence not trust the data being delivered. Worse, the leads may be duplicated, thus doubling the frustration.

No need to say that a team that doesn’t trust each other cannot move forward. You need a central database that minimizes the risk of dealing with siloed data. This database must meet the highest accuracy, completeness, and validity standards.

5. It increases the risk of data manipulation

“The company received 5,000 orders from India.” This statistic may push the company to open a branch in India. But what if the total number of orders received was 50,00,000 — the orders from India would have accounted for only 0.1% of total sales. Suddenly, the idea of an Indian branch isn’t as appealing.

When employees do not trust the data available, they may be tempted to manipulate data and let decision-makers hear what they want.

They aren’t fabricating data. They’re simply looking at it from an angle that suits them. These biases are stronger in organizations with poor data quality standards. Since they don’t trust data, they are more willing to hunt for data that support their preferences.

How to deal with poor data quality

Dealing with poor data quality can be a complex and ongoing process, but there are several steps that organizations can take to address the issue:

Establish a data governance program: This includes clearly defining roles and responsibilities for data management, creating policies and procedures for data quality, and implementing systems for monitoring and enforcing compliance.

Assess the current state of data quality: This includes identifying the sources and types of data quality issues, their impact on the organization, and the data elements most critical to the business.

Develop an improvement plan: This includes setting goals and objectives for improving data quality, allocating resources and budgets accordingly, and outlining specific steps and timelines for achieving those goals.

Implement data quality controls: This includes processes and tools for data validation, cleaning, and monitoring, as well as systems for capturing and integrating new data.

Regularly monitor and maintain data quality: This includes monitoring data quality metrics and taking action to correct issues as they arise. Maintaining and improving data quality should be ongoing rather than a one-time project.

Communicate with the stakeholders: Make sure to communicate with the stakeholders about the data quality project and give regular updates about the progress; also, make sure to collaborate with them on the data requirements and what they need in terms of data quality and accessibility.

Continuous Improvement: With all the data quality techniques, processes, and tools in place, it’s essential to evaluate and measure the performance to improve continuously.

Final Thoughts

Today, every company holds customer data but having data that employees don’t trust is useless. The above examples of how poor-quality data can sabotage growth are reasons to invest in improving data governance policies. Every organization needs to pay attention to where its data is coming from, how it’s being used, and so on.

All data must be verified and validated before it can enter the database. Even good-quality data in your database is susceptible to decay — customers won’t necessarily inform you when they shift addresses. Hence, existing data must be checked and filtered to maintain a high-quality database.

Amongst other benefits, building a trustworthy database will smoothen workflows, make your team more efficient and allow the organization to be a data-driven company.



Polestar Solutions | Data analytics company

As an AI & Data Analytics powerhouse, PolestarSolutions helps its customers bring out the most sophisticated insights from their data in a value oriented manner