1. Reduced costs and higher productivity
According to a study by IBM, bad data management costs the US over $3 trillion because of reduced employee productivity and costs related to data maintenance. Moreover, about 50% of employees' time is wasted looking for data, correcting errors and verifying data sources. Furthermore, more time is wasted as employees start correcting errors they find. If these errors are not corrected, they can create redundancies and limit productivity.
2. Improving prospect data for sales
While data can be a useful tool for your business when it comes to predicting leads for sales and marketing trends, having bad data can lead to mistrust in sales analysis outcomes. This can result in sales teams, analysts and managers relying on arbitrary benchmarks in finding potential customers. SnapLogic survey indicates that 77% of decision-makers and data managers stated that they do not trust their business data. On the other hand, 82% stated that they do not modify their projects because of the inadequate quality of data. With a clean database, guesswork can be mitigated and sales and marketing strategies informed, leading to efficiency in reaching new customers.
3. Enhanced customer survey
Customer satisfaction is important, and that is why they say the customer is king. Therefore, when the customer is upset because of poor delivery of the product in terms of address or time, it can hurt your business and lead to losses. Lack of inadequate data can result in misguided marketing strategies and incorrect or irrelevant communication with customers, thus affecting their relationship.
Database cleaning process
A database that has issues can be difficult to use in decision-making. However, there are various methods you can use to ensure that your data is clean so that your business can operate efficiently without issues. Here are some processes involved in cleaning data.
1. Get all your departments on the same page
The biggest impediment on productivity of any organization is the disconnect between the data uses and goals between different departments. However, you can avoid this by ensuring that all areas in your organization know and understand how specific data sets need to be used. Ensure that the data managers know how sales team intends to use the information. This will enable the data managers to generate information that is relevant to the purpose.
2. Concentrate on creation of new data
While the existing data can be crucial in fixing errors, it might not be of any value to your business and might cost you time and money. Furthermore, this approach may not address the cause of incorrectness in data. To sort this, it is crucial to implement processes to be followed by all during when gathering new information.
3. Adopt a CRM software
Although your organization may have a content management software (CMS) for managing data, adopting a customer relations management (CRM) software might be the most cost effective and efficient approach. With a CRM, you will be able to keep track of customer data and use its various versatile tools to manage leads, create sales funnels and manage workflows. Modern CRMs also uses artificial intelligence (AI) components to automate areas that are prone to human error.
