Data has become digital gold today. As a valuable asset for most businesses whether it is an aggregator startup, small and mid-sized firm, or a global corporate-businesses leverage data-driven insights to roll out multi-channel marketing campaigns. Keeping records of potential leads for immediate use and future reference is a topmost priority for most organizations.
As most people continue to change their contact details such as phone numbers, email, and postal addresses, the data stored in your database becomes old and irrelevant. Duplicate contacts, old email addresses, or misspelled names often impede a company’s marketing and sales efforts. And, CRM and marketing tools are as strong as the data in them. So, a question worth wondering here is what happens to the customer database when it is left idle for the long run.
Just collecting heaps of data isn’t enough for businesses; instead, there is a need for accurate quality data to facilitate successful CRM initiatives and this is where data cleansing comes into the spotlight.
Understanding the Process
As the name suggests, data cleaning is the process of editing, eliminating, and upgrading information within a database to maintain its quality. Leveraging this approach, businesses get segmented and accurate data at easy disposal, which can then be used for multi-channel marketing initiatives and streamlining business operations.
By engaging in professional B2B data cleansing services, organizations can address an array of challenges including troubleshooting, error rectification, manual data correction, and incorrect invoice data. Remedying all such issues add up to high operational costs, which can be kept at bay if businesses keep their database competent and clean.
After all, correct, complete, and relevant data is crucial to explore and leverage the true potential of customer data in a meaningful way.
Importance of the Process
With a majority of business processes, actions, and decisions relying on data-driven insights, it is vital to manage error-free data, especially in data-intensive industries such as insurance, banking, retail, telecoms, healthcare, and so on. Regular and structured data cleansing offers a plethora of benefits for organizations. Some of these are listed here:
- Data Errors Can Be Costly
There’s a golden rule of ‘cost of quality’ of 1-10-100, which states that prevention is less costly than correction. According to this rule, it costs exponentially more money to identify and correct data errors than the longer it takes to find them. Hence, data cleansing is the best practice for avoiding costs that crop up when businesses are busy processing errors, rectifying incorrect data, or troubleshooting.
- Make Data Work Across Different Channels
Clean, consistent, and coherent data makes way for the successful management of multichannel customer data. After all, accuracy in customer data such as phone numbers, postal, and email addresses allows your sales representatives to successfully execute contact strategies across different channels.
- Greater Customer Acquisition
Companies with well-maintained data at their easy disposal are best placed to develop lists of sales-qualified leads, which in turn convert to customers using updated and accurate data. As a result, businesses can streamline as well as increase the efficiency of their customer acquisition and onboarding operations.
- Accelerated Decision-Making Process
One of the tangible benefits of data cleanup is accelerated decision-making as nothing supports the straightforward process like clean and accurate quality data. High-quality data supports MI and other key analytics, which in turn provide companies with the insights that are required to make well-informed business decisions.
- Increase Productivity of Internal Teams
Data cleaning is also important as it improves the data quality and, therefore, leads to increased productivity. Employees need not waste time looking for correct contact details and then pitch in the sales tone. When incorrect data is removed or replaced with accurate information, they are left with the highest quality information. This means that their teams don’t have to use time and resources to wade through incorrect and irrelevant data.
By now, it is clear that garbage data in is garbage analysis out. Cleaning data is, therefore, an indispensable part of the entire data analytics process. After all, stakeholders want to analyze data that is correctly formatted, accurate, complete, and unique so that the insights generated are of the highest quality. As a result, businesses can accelerate their decision-making process, increase efficiency, and maximize productivity. Apart from this evident ROI, clean and accurate databases help companies stay GDPR and CAN-SPAM compliant.
Wrapping Up
Even though data cleanup and migration processes are complicated businesses, there are professionals to help businesses out. Engaging in outsourced data migration services or cleanup services is a smarter way out-companies get clean and accurate data at easy disposal without compromising its integrity. First of all, you must begin with finding an appropriate data migration and a cleanup company that understands your unique requirements and aligns the outcomes accordingly. So, what’s stopping you from getting started?
The post 5 Reasons Why Businesses Must Invest in Data Cleansing Practices appeared first on Datafloq.
