Bad Data Prevention: Best Practices for Developers
Bad data quality creates trust issues. It can destroy the trust your sales team places in sales funnels, the trust your customers place in your company, and the trust executives place in data-driven decision-making. As a developer, you are in a unique position to stop bad data from entering your databases.
Sometimes good data goes bad
The first threat to your databases is natural decay, which occurs at an estimated rate of 31 percent per year. Without a proper data cleansing process in place, natural decay is going to take place due to everyday occurrences like job changes and promotions, contacts moving to different departments or regions, and companies merging or closing.
Sometimes bad data creeps in
The second threat to your databases is dirty data entering your systems due to things like human error at the input source and duplicate data that could have been prevented. This added bad data creates problems for everyone who touches that database or makes decisions based on it.
The problem of bad data is pervasive
No matter how much bad data exists in your database or enters it, it creates issues for developers. Some possible impacts:
You may be asked to customize solutions in a Salesforce instance that temporarily or partially deter bad data entry, but don’t really address the underlying problem A lack of trust in the data creates indifference among users, thwarting your attempts to establish and maintain data quality controls You create user interfaces, deploy new software applications, establish testing parameters, or generate analytics that are based on inaccurate or incomplete data A workplace culture complacent with poor data impedes your ability to help Salesforce admins improve CRM data and streamline its maintenance Recognizing the business need to address bad data
Some companies have a healthy respect for data quality, creating an internal culture that enforces data integrity and an external reputation for achieving it. For many others that don’t, the problem of bad CRM data eventually becomes impossible to ignore.
Company databases grow over time and the teams that rely on those databases to create operational efficiencies or generate important metrics often reach a tipping point concerning data quality. At this critical stage, the organization is forced to acknowledge that bad CRM data is exerting a significant and persistent drag on its operations and ability to meet strategic goals.
What can you do before bad data reaches crisis stage? Make a solid business case for good quality data. Help your organization and its key players understand that having a competitive advantage with data integrity has never been more important. Getting business executives to take notice and take action doesn’t always require a customer outcry about data breaches or a massive loss in expected revenue due to poor data quality. Move beyond the native tools of your CRM. Data-driven organizations need more than the standard toolsets. Adopt advanced solutions that integrate with your API and can identify, flag, and fix data issues ahead of time. Investigate third-party options on the