Why Data Quality is Crucial for AI and Automation on the Salesforce Platform

Data quality is the foundation of business success.  

As organisations increasingly adopt automation and artificial intelligence (AI) tools, particularly through Salesforce implementation, the accuracy and reliability of your data becomes even more critical.  

In this blog, we will explore why maintaining high data quality in Salesforce is essential for maximising AI and automation and your return on investment.

The importance of data quality to your agents

Data quality is essential for the effectiveness of automation and AI systems, including tools like AgentForce.  

At its core, data quality refers to the accuracy, consistency and completeness of the data stored in a system. In Salesforce, where customer data is used to drive everything from marketing to customer service, maintaining data quality has far-reaching implications for both the user and customer experience and, therefore, business outcomes. 

Data quality plays a crucial role in enhancing both user and customer experiences. When data is accurate and up to date, users — human or agent — can access reliable customer information, enabling them to offer a personalised and efficient service. For example, with complete purchase histories and preference details readily available, interactions become more tailored and efficient, improving customer satisfaction. 

To truly understand the importance of data quality in AI, think of your bot as a user navigating Salesforce.  Just as a human user would struggle with incomplete or inconsistent data or complex UI, so too will an agent. Agents rely on accurate and complete data, and they absolutely require consistent metadata labelling to provide reliable outputs. Agents cannot say sorry like a human does or think on their feet without the right prompt. 

The consequences of poor data quality

A Salesforce system relying on flawed data compromises the value of agents and can harm the user and customer experience in the process. 

Imagine a scenario where your Salesforce platform is overwhelmed by duplicates, missing data and incorrect entries; this could seriously undermine your efforts to introduce agents into a process or trigger incorrect workflows. For example, AI models trained on incomplete or erroneous data may produce incorrect predictions and skewed analysis, leading to suboptimal decisions and missed opportunities. Decision-makers depend on Salesforce for strategic insights, meaning inconsistent or unreliable metrics could compromise business planning and operational efficiency. 

The impact of poor-quality data extends beyond internal operations; it directly affects customer interactions. When agents access incomplete or incorrect customer profiles, they can offer irrelevant or inaccurate solutions. This not only frustrates customers but also damages brand reputation, transforming potentially seamless interactions into frustrating experiences that erode trust and loyalty.  

How to maintain good data quality

Here are some best practices to maintain good data quality… 

Deduplication

Regularly scan your Salesforce database to identify duplicates. Be very wary of ‘easy to use’ deduplication tools that could result in significant data loss when your de-duplication logic is not applied correctly to child objects and associated records. A properly thought-through process and a lot more human input to decisions are required than you often first imagine. 

Standardisation

Ensuring you have a standardised data dictionary that works at an organisation level across all your systems as well as Salesforce is crucial. Using consistent field labelling across all your systems will significantly enhance your AI and automation capabilities. Equally, at the field level, enforcing consistent formats like dates, telephone numbers and addresses will help enormously. 

Validation rules

Implement validation rules in Salesforce to enforce data accuracy. For instance, you can set up rules to prevent incomplete or incorrect data entries at the correct time in the process, which supports users to deliver good input and also sets standards across your platform. 

Data enrichment

Use tools to enrich your data by filling in missing information or enhancing existing records with additional insights. There are a wealth of Salesforce applications that will integrate and enhance your data quality without interference from humans. 

Ongoing monitoring

Implement regular data audits and use monitoring tools to catch errors or inconsistencies early. 

AgentForce integration

It is also worth noting that one of the most transformative prospects for agents is their ability to actively improve data quality.  

Through dynamic interactions with users and customers, agents can play a pivotal role in ensuring data is collected, verified and updated accurately. AgentForce can run automated processes for cross-checking entries for accuracy and completeness, flagging inconsistencies and prompting for corrections. Real-time data correction also becomes possible as the system identifies and fills gaps during user interactions. For instance, if a customer reaches out to an agent and their address is missing, AgentForce can prompt them to provide the necessary information, instantly updating their record. 

In essence, Salesforce implementation becomes far more powerful when supported by high-quality data, enabling businesses to maximise the potential of their AI-driven tools. 

Data quality should be more than a back-office concern — after all, AI and automation tools are only as good as the data they consume. So, whether you are looking to enhance customer interactions, drive automation or make data-driven decisions, clean and accurate data in your Salesforce implementation is the key to success. Contact Technicus today at info@technicus.co.uk to find out more about our data health check services.