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Salesforce is the world’s leading CRM (customer relationship management) software, with a 20 percent market share. The Salesforce CRM software is chock-full of features for business intelligence (BI) and analytics so that you can capture hidden insights and make smarter, data-driven decisions. The traditional ETL (extract, transform, load) process extracts data from one or more sources and then deposits it into a centralized data repository.

Businesses rely on personal data to better tailor their approach to customer relations and streamline a marketing strategy suited to their target audience. In today's business climate, holding onto the personally identifiable information (PII) of specific individuals for various marketing and customer services purposes requires secure storage and extraction. PII pseudonymization is the latest and greatest method for protecting personal data.

With news of a devastating data breach constantly in the headlines, you need to take proactive steps to safeguard the personally identifiable information (PII) that your organization stores and processes. Along with techniques such as PII masking, PII pseudonymization is one of the most popular and practical ways to protect sensitive data. But what is PII pseudonymization, exactly, and how can you pseudonymize PII? We’ll answer these questions and more in this article.

Knowing best practices for Amazon Web Services (AWS) data pipelines is essential for modern companies handling large datasets and requiring secure ETL (Extract, Transform, Load) processes. In this article, we discuss AWS data pipeline best practices to ensure top performance and streamlined processes — without complications that can impede the execution of data transfer.

When it was revealed in September 2017, the massive Equifax data breach made international headlines. As one of the three major credit agencies in the United States, Equifax is responsible for processing personally identifiable information (PII) such as individuals’ names, addresses, and social security numbers. According to Equifax, 143 million people were affected by the data breach, making it one of the biggest cybersecurity disasters in history.

With technology innovations raging at incredible speeds over the past few decades, new and exciting platforms for gathering, storing, transforming, and manipulating data are entering the market every day. Apache Hadoop was one of these disrupters when it entered the market in 2006, offering distributed storage and big data processing using a network of many computers.

The Healthcare Insurance Portability and Accountability Act (HIPAA) has been an important federal law in healthcare since 1996. Part of its purpose was to create standards meant to protect sensitive patient information, and it took on even more important once the digitalization of patient health records became widespread. Now it’s required for certain types of businesses to protect patient health information—or face fines that range from $100 to $50,000 per violation.

Data analytics technology helps organizations make sense of an ever-increasing volume of data. As this technology matures, it gets better at delivering actionable insights and helping companies determine outcomes. Prescriptive analytics is a modern solution that builds upon other analytics technology and guides organizations to the right decisions for a particular situation.

Organizations have access to massive amounts of data, but they don’t always give enough thought to how they’re going to keep it private and protected. Dozens of data privacy regulations are in effect or in development globally, and the average consumer is learning more about how much of their data gets collected and used by businesses. For this reason, companies need to focus on keeping data safe while it's under their control, but it’s easy to make mistakes.

Extract, Load, Transform (ELT) technology makes it easy for organizations to pull data from databases, applications, and other sources, and move it into a data lake. But companies pay for this convenience in many ways. ELT solutions can have a negative impact on data privacy, data quality, and data management.