Bad data can decrease sales, increase costs and make your business inefficient. What constitutes bad data includes: incomplete, inconsistent, inaccurate and outdated data. As you collect more and more data, bad data can creep up across all business departments including: marketing, sales, operations, human resources and finance. Here are just some examples of why investing in data cleaning should be your top priority this year:
- Decreased sales – if your sales team has to spend time finding the right phone number to dial, it decreases their selling time. Inconsistent data makes it impossible for marketing to segment based on key fields, therefore decreasing their ability to target their message to individual segments. Not having accurate data may make your operations team make the wrong decision which will at the end of the day come at a cost.
- Increased costs – While the cost to clean your data may come at a price, consider the time that employees spend on dealing with bad data. The data cleaning maybe a fraction of their efforts. For example, if the marketing manager spends three hours each week sending out emails by manually selecting certain industries to target, means that by the end of the year they have spent over 150 hours on something that could have been standardized for a fraction of the cost of their hourly rate.
If you are missing key information such as geography of the prospect or company size you may waste a lot of company resources by finding that information as oppose to automating data appending.
- Inefficiency- Finally the hours spent by your organization dealing with data that is not usable does not only cost money but also makes other processes inefficient. For example, human resource department may receive multiple resumes for the same person throughout the year. The duplicates that are found in their CRM mean that they not only have to identify which resume was the latest one but also review all previous applications to figure out why they were not hired. This not only makes the H.R. manager inefficient in their work but also adds extra steps to process resumes by those applicants that are submitting for the first time. As now, the H.R. manager has to check if they submitted that or not.
These are just the top three reasons to clean your data, there are many more including: inability for accurate decision making, lack of proper analytics, marketing’s ability to market using different segments and so on. Cleaning your data may not be a daunting task as you think, by hiring a professional data cleansing company you can save on time and money, and improve your overall sales and make your employees much happier.