For organizations looking to automate processes for better performance, a combination of RPA and process mining can help. Let’s take a step back to understand why. Automation that’s implemented without data-backed decisions often yields poor results. And data without action leads nowhere. To get the most out of any technology investment, you have to be sure you're using the right combination of tools and techniques.

OLTP is a transaction-centric data processing that follows a three-tier architecture. Every day, businesses worldwide perform millions of financial transactions. This fact brings to mind client-facing personnel such as bank tellers and supermarket cashiers tapping away on keyboards and at cash registers, and with good reason. According to ACI Worldwide, a payment systems company, there was a 42.2% growth in global real-time transaction volumes in 2023, amounting to 266.2 billion transactions.

Data mining, also known as Knowledge Discovery in Data (KDD), is a powerful technique that analyzes and unlocks hidden insights from vast amounts of information and datasets. Data mining goes beyond simple analysis—leveraging extensive data processing and complex mathematical algorithms to detect underlying trends or calculate the probability of future events.

When using Linx, you may wonder how to access isolated or hard-to-reach resources. It may be that that resource is unreachable because of firewalls or strict control and governance policies. There are a few options to access these gated resources.

Data filtering plays an instrumental role in reducing computational time and enhancing the accuracy of AI models. Given the increasing need for organizations to manage large volumes of data, leveraging data filtering has become indispensable.

The success of data warehouse solutions depends on how well organizations implement test cases to guarantee data integrity. As organizations evolve, data warehouse testing becomes crucial to adhere to industry best practices.

AI injects “intelligence” into automation, enabling systems to execute tasks, comprehend complex data, make informed decisions, and learn from outcomes. Unlike technologies such as robotic process automation (RPA), which follow predetermined rules, AI leverages data to evaluate situations and determine the best course of action. Now that we've explored how AI augments traditional automation tools, let's delve deeper into the realm of intelligent automation.

Intelligent automation removes barriers to scaling automation that have hindered business productivity for years. This powerful fusion of technologies automates complex processes, empowering organizations to adapt to change faster and more effectively than ever before.

According to Gartner, 80% of companies worldwide are expected to have efficient data management systems in place by 2025. This projection highlights the growing recognition of data governance tools as essential enablers for maintaining and enhancing the quality and security of organizational data within these data management systems. In this blog, we will talk about some of the best data governance tools and software to consider in 2024.

Data integrity refers to protecting data from anything that can harm or corrupt it, whereas data quality checks if the data is helpful for its intended purpose. Data quality is a subset of data integrity. One can have accurate, consistent, and error-free data, but it is only helpful once we have the supporting information for this data. Data integrity and quality are sometimes used interchangeably in data management, but they have different implications and distinct roles in enhancing data usability.