by Sheryll de Leon
Data is not born bad. Like food in your refrigerator, it spoils over time, due to various factors such as human error (incorrect data entry by an employee or customer), data siloes, failed data migrations/integrations, vendor changes, and complications stemming from mergers and acquisitions.
While individuals within organizations often recognize that low-quality data can be detrimental, many companies have not thoroughly quantified its impact. As organizations work with larger and larger datasets, it’s important to measure the exact cost and negative effects that poor data quality has on operations and decisions.
Without a rigorous, data-driven approach to evaluate these consequences, organizations may fail to appreciate the full extent of the risks associated with maintaining poor-quality data, which can lead to suboptimal performance and missed opportunities for improvement.
Take for instance the Customer Relationship Management (CRM) systems. CRM systems, just like Analytics, are highly effective in improving efficiency and increasing revenues. To get the most from these technologies in the long run, however, businesses will need to provide the systems with clean, structured, high-quality data. That means taking the time to fully prepare the data before handing it over to these solutions is an essential step.
Bad data is never going to fix itself, and most likely it will continue to get much worse if left alone. In many cases, bad data can be actually worse and even more dangerous than having no data at all.
Bad data is never going to fix itself, and most likely it will continue to get much worse if left alone. In many cases, bad data can be actually worse and even more dangerous than having no data at all.Â
You can’t make informed decisions based on nothing, but you can make wrong decisions based on wrong information. In other words, there’s a cost to doing something wrong, and there’s also a cost to doing nothing at all.
Here are some points illustrating how poor data quality diminishes value, particularly in CRM Systems:
1. NEGLECTED SALES OPPORTUNITIES
CRMs are designed to assist sales teams in managing promising leads and advancing them through the sales funnel. Working with outdated or incorrect data can cause significant sales opportunities to slip through unnoticed, or worse, result in wasted efforts on less promising Leads.
2. SKEWS FORECAST ACCURACY
Incomplete, erroneous, or untraceable data hampers the development of reliable forecasts. This could lead to misallocation of resources and under/oversell the gravity of each opportunity.
3. SEGMENTATION AND NURTURING
Customers from different demographics have unique needs and engage differently throughout their customer journey. Insufficient or flawed data hinders effective customer segmentation and can lead to a poor experience. The right message to the wrong customer will still be ineffective and could lead to complications down the line that would turn more people away from your business.
4. CUSTOMERS COMMUNICATION
Poor quality data can lead to ineffective email campaigns and direct mailings, despite the creation of visually appealing emails and graphics. The situation worsens when teams allocate excessive time to data verification instead of concentrating on core activities like sales or delivering superior customer service.
5. COMPROMISED ANALYTICS
The integrity of Business Analytics is undermined in the absence of precise data. Without it, generating practical insights becomes unfeasible. The insights derived from analytics are only as trustworthy as the data input into your dashboards, which are essential for managing operations, strategizing, forecasting, and ultimately affecting the financial outcomes.
All of these are just for CRM systems. The amount of data generated today is huge, and won’t be getting any smaller. With the advancement in Big Data, the Internet of Things (IoT), and Artificial Intelligence (AI), the amount of information is set to shoot up even further. That’s why businesses must invest in robust data management strategies to ensure data accuracy, completeness, and reliability, which will enhance overall operational efficiency and competitive advantage.
Without proper data management, business owners and operators will have access to these huge amounts of information they cannot fully trust. Much of the data will remain as noise without any substance, especially those emanating from new data sources.
Data quality is important because, without it, the data can’t fulfill its intended purpose. If you don’t make the most of the data, what is the point of having it in the first place?
Discover how Data Management can help make your data profitable and impactful by booking a meeting with us!