Information is called the oil of the 21st century. Every business collects data about their customers including their names and addresses, phone numbers, email addresses, order history and preferred payment modes.
However, it isn’t the amount of data collected that determines its usefulness but the quality of this data. If your data doesn’t match high-quality standards, it could harm your business initiatives. Poor quality data can cost organizations up to $12.9 million annually! Let’s take a look at the top 5 ways poor data can sabotage businesses.
1. It lowers productivity
Low-quality data can make your processes sluggish and waste your team’s time. According to a Lead Jen study, using unverified data can waste 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, they’re spending their time deciphering patchy data. Poor data quality also limits the organization’s ability to automate processes.
It is only when data is accurate and complete that it can be trusted and sales reps can automate calling from a list and focus on building relationships with customers.
2. It lowers the profit margin
United Airlines flight tickets that normally 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. For example, not getting customer demographics right could result in opening stores in the wrong locations or misaligned promotional campaigns. Something as simple as having a wrong customer address can double shipping costs and eat into the company’s profits.
3. It influences employee turnover rates
When companies fail to take data quality seriously, employees have to spend their time manually sifting through redundant data. It brings down their efficiency, demotivates them and makes them feel undervalued.
Today’s skilled employees have limited patience for slow processes. According to a survey, 37% of employees who quit their jobs, did so because they felt they weren’t valued or appreciated.
Replacing your top employees can be difficult, time-consuming and expensive. Resources must be spent on hiring as well as training. And, 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 big role in this. The marketing team may be frustrated when the leads they deliver aren’t being followed up on. On the other hand, the sales team may find that the leads are inaccurate and hence not trust the data being delivered to them. Worse, the leads may be duplicated thus doubling the frustration.
There is no need to say that a team that don’t trust each other cannot move forward. What you need is a central database that minimizes the risk of dealing with siloed data. This database must meet the highest quality standards in terms of accuracy, completeness and validity.
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 to them, they may be tempted to manipulate data and let decision-makers hear what they want to.
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 supports their biases.
In Conclusion
Today, every company holds customer data but having data that employees don’t trust is of no use. 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 too 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 truly be a data-driven company.
