Data Quality & Its Impact on Data Cloud/ Salesforce AI Success

Over the years, Data Quality has played an indispensable role in the overall success of the Data Cloud and Salesforce AI. In today’s data-driven era, almost every major organisation and multinational company relies on data cloud solutions and Salesforce AI for delivering accurate and high-quality data and facilitating rational decision making. Based on current projections, approximately 120 zettabytes of data (where 1 zettabyte= 1 billion terabytes) was created in the world in the year 2023. This number is expected to surpass 180 zettabytes by 2025.

Data Quality is a term that refers to the completeness, timeliness and accuracy of the data for its intended purpose. The availability of high-quality and complete data can serve as a solid foundation for enabling valuable insights, interactions with clients and the optimum utilisation of resources.

Accuracy- High accuracy implies that the data is free from any bias, inconsistencies or errors and truly aligns with true values it purposes to describe. Do you possess up-to-date information about your (potential) clients? Or do you need to work on the accuracy of your data? Think again.

Completeness- Incomplete data can lead to potentially false conclusions and can significantly undermine the effectiveness of AI models, interfering negatively with the decision-making processes. 

Timeliness- Outdated data can lead to wrong conclusive analysis, hindering the success of AI models since Data Cloud and Salesforce AI are heavily dependent on real-time insights and predictions. Having access to the most recent and up-to-date data ensures that the decisions are taken based on the current market conditions, thus preventing ineffective engagement strategies. 

Data cloud solutions mainly serve as repositories for huge volumes of data from diverse sources. While poor quality data can impede operational efficiency causing delays, errors and other major inefficiencies, adhering to data quality principles can potentially help in mitigating compliance risks and safeguarding sensitive information. Recently, GS1 US conducted a test of 24 companies which showed that around 50% of the data analysed was inaccurate. By maintaining the standards of data quality, organisations can witness significant reduction in costs and improved productivity, while fostering long-term stakeholder and customer relationships and complying with all the regulatory requirements and government frameworks. Prioritizing data quality can help organisations to unlock great potential of their data cloud environments and foster sustainable growth. 

AI relies on various sources for collecting data- from CRM systems to external databases and marketing platforms. The completeness and accuracy of customer data can drive higher levels of consumer engagement and satisfaction and deliver suboptimal results. While high-quality data boosts the reliability of these integrated datasets and maximises the effectiveness of Salesforce AI, poor quality data can cause problems like erroneous conclusions, inconsistent formatting and duplicate records. As per sources, poor quality data costs business around 30 percent of the company’s average revenue, that can be as high as $700 billion a year!

According to Salesforce.com, an average customer’s contact database consists of nearly 90% incomplete records, with 20% of the records being useless due to some reason or the other, such as 74% requiring major updates and more than 25% of those being duplicates. Given the hefty price that the organisations have to pay for these inefficiencies in data quality, the adoption of the following key strategies can greatly help in improving the data quality and altering these consequences by great lengths:

Automated Data Validation- It involves dependence on automation tools like automated workflows and triggers to perform system checks on the data entered into the Salesforce or other systems within the data cloud.  This is backed by real-time feedback as the users update or enter data, wherein if the data fails the validation checks, the users are immediately notified about the errors and prompted to correct them. 

Performance Indication and Measurement- By defining and regularly monitoring Key Performance Indicators (KPIs), organisations can adjust their strategies and ensure data completeness, fostering accurate sales forecast and optimisation of data quality processes and tools. Establishing clear goals against each KPI can thus, help in setting benchmarks for data quality standards, making room for improvements and creating accountability.

Data Governance Framework- A Data Governance Framework consists of rules and regulations which regulate the lifecycle of the data from data acquisition, formatting, classification and deletion. It involves the use of data masking techniques and data encryption control systems in order to protect sensitive information with comply with regulations like CCPA and GDPR. 

Data Cleansing and Profiling- By incorporating data profiling and data scrubbing (or data cleansing) tools, users can rule out any duplicates, inconsistency, inaccuracy and errors, while analysing the data withing the cloud and Salesforce to generate comprehensive profiles. These profiles can then be use to identify areas of improvement, standardise formats and ensure data consistency.

It should come as no surprise that the organisations who proactively address the problem of poor data quality, while prioritising the accuracy of their data have been able to forecast and address deviations and have excelled in delivering exceptional customer experiences. Thus, Data quality is a critical success factor for organisations in order to thrive in today’s digital economy.