A 2021 Educase report shows that 75% of surveyed institutions have started or are planning extensive digital transformation strategies to improve their data utilization. The importance of a strategic approach to data utilization in this context cannot be overstated. Effective data management frameworks ensure that data across the institution is accurate, consistent, and used in alignment with internal policies and external regulations.

Data ingestion is important in collecting and transferring data from various sources to storage or processing systems. In this blog, we compare the best data ingestion tools available in the market in 2024. We will cover their features and pros and cons to help you select the best software for your use-case.

Metadata governance is the systematic approach to managing metadata, or data about data, to ensure it is accurate, consistently formatted, and securely stored. The main goal of metadata governance is to improve data quality, ensure data privacy, and streamline metadata management. As metadata provides the necessary details about data, such as when it was created, by whom, and its content or purpose, it becomes easier for employees to find, understand, and trust the data they use.

No matter their size, all organizations rely heavily on the data they collect and manage. This data ranges from customer information to sales records, employee performance, and more. However, if this data is inaccurate, outdated, or incomplete, it becomes more of a liability than an asset, making it more important to measure its health. To do so, they need data quality metrics relevant to their specific needs.

Enterprises have complex IT and technology needs. To reach business goals, organizations must often stitch together a patchwork of software and IT systems. So, it’s understandable that the thought of adding yet another technology—low-code—might cause trepidation. But low-code excels at integrating seamlessly with any technology. Low-code tools speed up development work and enable rapid application development.

One of the key processes in healthcare data management is integrating data from many patient information sources into a centralized repository. This data comes from various sources, ranging from electronic health records (EHRs) and diagnostic reports to patient feedback and insurance details.

Data governance and data quality are closely related, but different concepts. The major difference lies in their respective objectives within an organization’s data management framework. Data quality is primarily concerned with the data’s condition. It ensures the data is complete, accurate, reliable, and consistent. On the other hand, data governance refers is the overall management, maintaining compliance, and ensuring the security of data assets within an organization.

Data forms the foundation of the modern insurance industry, where every operation relies on digitized systems, including risk assessment, policy underwriting, customer service, and regulatory compliance. Given this reliance, insurance companies must process and manage data effectively to gain valuable insight, mitigate risks, and streamline operations.

Many organizations are turning to SharePoint to store their files and share documents. Allowing your systems to access these files might seem like a challenge at first, but with the help of the Microsoft Graph API, you can quickly download the file to start processing on your back-end. Download the example solution to get started. Use the created functions in your solution to simplify Microsoft Graph integration.

Microsoft Graph API allows developers to access data and services across a wide range of Microsoft cloud services. Admittedly setting up permissions and security can be challenging, but once that is sorted, you will be able to access Microsoft services such as Office365, Outlook and Azure. Calling the Graph API with Linx allows you to efficiently automate tasks, or integrate with Microsoft products to ensure that functionality and data is accessible by your other systems.