Since it’s in direct contact with operations, data now also serves to train AI. With this new use, data management needs to be more rigorous than ever to maximize data value in Salesforce Data Cloud. And that’s not all: data governance cannot be successful without using a sustainable approach.
With this in mind, data quality helps companies to use their data on Salesforce Data Cloud more responsibly.
1. Current Trends in Data Management
Data management within companies is constantly challenged by new constraints that include AI, regulation, and the rise of first-party data. Practices have to adapt, and several optimization trends are emerging:
Enhancing Data Management With Salesforce Data Cloud
Data is a strategic asset for companies. However, their data environments often remain compartmentalized, which obstructs a unified view of customers, and brings about approximations and even operational errors. A data platform like Salesforce Data Cloud gets past these limitations. With a platform like this, data can be federated from across the organization and eliminate silos. This optimized management approach offers a number of benefits:
- Personalized Customer Experiences: By reconciling data scattered across the organization, Salesforce Data Cloud enables access from a single interface, facilitates the evaluation of data assets, and optimizes how they’re used. A 360-degree view means each customer can be known and recognized, and it enhances the personalization of experiences that are offered.
- AI-Ready Data: Salesforce Data Cloud facilitates integrating other technologies, in particular AI. By giving access to the organization’s complete dataset, the platform provides the means to take algorithmic model training a step further.
- Support For Regulatory Compliance: Regulations such as the GDPR require control over how data is stored, retained, profiled, and used. Salesforce Data Cloud offers a dashboard to keep track of all data processes beyond just compiling them.
Taking Full Advantage of First-Party Data Assets
First-party data, collected directly from prospects and customers, is becoming more important as third-party cookies are deprecated. This data is extremely valuable. Provided by users and stored directly by the company at a lower cost, they’re authentic, collected in an ethical manner, and present a real interest to the company.
Collecting, qualifying, and using this data is becoming a priority. However, this first-party data is often fragmented, as it comes from a variety of unconnected sources (including CRM tools, social networks, and subscriptions). To use this first-party data to its full potential, it needs to be rigorously managed.
Taking Sustainability Into Account in Data Management
Data management has become a sustainability issue. Storage and processing rely on underlying physical infrastructures that contribute to the overall impact of digital technology. This impact is embodied in the high energy consumption required to run the machines – 2% to 3% of the world’s annual energy consumption – greenhouse gas emissions, the use of water resources for cooling purposes, resource extraction, and electronic waste.
The rise of AI accentuates this impact, including when we talk about data used to feed machine learning. AI must therefore be sustainable and responsible, to enable ethical innovation that continues in the long term. Salesforce is already committed to this, notably through its AI for Impact initiative and its six-point plan to measure and manage the impact of AI use.
2. Taking Data Management to the Next Level of Sustainability
Companies need to make responsible use of digital technology and data. Cleaning up their databases and training AI with healthy data contributes to this, which calls for high-level data quality management.
Salesforce-Native Data Quality Tools: Aim Higher!
Cleansing data also means deduplicating it. Duplication is a scourge that places unnecessary demands on storage capacity and processes and degrades operational efficiency.
Salesforce Data Cloud’s native tools can handle duplicates, but only to a limited extent. These tools are limited to merging three records at a time and offer few matching possibilities. For example, it doesn’t offer cross-object matching. This means that organizations don’t have access to automated batch processing, which is truly a difficulty when dealing with large volumes of data. Duplicate management for custom objects and imports is also absent.
To avoid time-consuming data quality operations, it’s best to augment Salesforce with a specialized third-party tool from the AppExchange. A data quality expert tool is more powerful and more comprehensive and automates the detailed processing of millions of records without overwhelming processes. Optimally, this tool also qualifies the collection of new data in real time before it enters the database.
Free Trial: DQE Duplicate & Data Quality Management Platform for Core & Data Cloud
Managing Large Volumes of Heterogeneous Data
Data management problems are multiplied tenfold for databases with several hundred thousand or even millions of records. Duplicates present this exact problem. This is why it’s so important to automate data quality at scale in order to handle the largest databases and avoid wasting time and resources on useless data. Moreover, clean, deduplicated data gives a 360-degree view of customers, enabling better personalization of their experiences. Another benefit is that analyses and reporting are more accurate and reliable as they’re no longer distorted by duplication.
Facilitating Data Governance
Robust data governance requires data that is accurate and compliant in Salesforce Data Cloud. When the platform reconciles qualified and reliable data, standards, processes, controls, and responsibilities are more easily managed. Furthermore, data hygiene throughout the entire lifecycle, from source to activation, provides preventive benefits against the circulation of erroneous data throughout the environment.
Putting Data Governance in a Sustainable Loop
With the rise of AI and the storage of countless first-party data elements, we now have to reduce the impact of CO2 emissions when using customer databases. Data quality contributes to this effort:
- Qualifying databases frees the environment from having to store erroneous data that burdens IT infrastructures.
- Eliminating duplicates also eliminates unnecessary processes such as sending multiple text messages, emails, or letters to the same contact.
- Feeding AI with cleansed data curbs the energy consumption of machine learning processes and the associated CO2 emissions. Data quality also makes generative AI responses more reliable and influences the explicability and interpretability of AI models.
To promote these best practices, we need to demonstrate that processing carried out on data quality translates to reducing carbon emissions. This is why an eco-calculator is necessary, capable of quantifying the reduction in CO2 emissions achieved on cleansed databases. This real-world calculation raises companies’ awareness and encourages their sustainable data management initiatives. It then helps fuel a virtuous circle of responsible data use in Salesforce Data Cloud.
Summary
Adopting a data platform like Salesforce Data Cloud guarantees rigorous data management. To that rigor we must now add the aspect of sustainability to guarantee responsible use of data. This is because data uses storage and processes, in other words, the underlying physical IT infrastructure whose carbon impact increases with AI training.
Switching to responsible data management means avoiding unnecessarily using IT capacity by cleansing databases. Enhancing Salesforce Data Cloud with a powerful, specialized data quality tool is proving to be an effective practice. When we demonstrate to companies the very real carbon savings involved, we encourage their efforts made in this area.
Don’t miss out! Evaluate DQE One, the AppExchange’s expert tool for data quality management.