Increase usability Often separate departments/platforms are accountable for storing and sharing uncontrolled, out of date versions of data. The data becomes more usable by combining data from isolated sources into one single system.
Complete data By connecting your product lifecycle management systems with your other systems, every stakeholder within your organization can impact the flow of product data through your organization.
Better insights Productivity can drastically improve when an enterprise has insights into optimising current products and processes. Doing this requires teams to have access to up-to-date, accurate product data.
Better decisions By connecting systems, everyone in your organization will have access to real-time data allowing them to make better product decisions. Decisions and actions driven by up-to-date information will result in higher product quality.
Increased productivity By combining data, any user within your organization can access contextual, up-to-date, real-time information. Automation of the process will reduce user errors and increase overall effectiveness.
Increase in sales Providing customers and partners with up-to-date, validated and complete data will increase their trust in your organization and pursue them to purchase your products.
Data Connectivity solutions help o-r-g-a-n-i-z-a-t-i-o-n-s connect to disparate data sources such as Saas platforms, Databases, legacy systems, etc. in real-time to gain access to data for improved decision-making.
It can make these data sources simpler to use, more affordable, more dependable, and easier to integrate with other solutions, hence broadening the scope of how data can be used.
High performant: The performance of an operation that reads from or writes to a data store is a challenge primarily because storage is slowest among CPU, memory, or network. This performance problem might well become a bottleneck in your data processing pipeline. Hence you should ensure that you use the best technologies and storage mechanisms so that this challenge can be mitigated to some extent.
Intelligent: Data connectivity should be intelligent and adaptive. As an example, you can have data that is flat or relational, or even hierarchical. The data might reside in relational databases, document databases, CSV files, etc. When the data is present in the CSV files there might not be any column headers. When reading from such files the data connectivity solution should be adaptive—it should be adept to learn the schema and also able to read the data gracefully even if the schema has changed.
Flexible: Data connectivity should be flexible; it should be adept at establishing connections of various types and be able to work with various authentication and authorization mechanisms.
Secure: Data connectivity solutions should be secure; they should support authentication and authorization mechanisms so that unauthorized access to data can be prevented.
Broad and Ubiquitous: Your data might reside in the cloud, on-premise, in a relational database, a NoSQL database, data warehouse, anywhere! In other words, you might have your data spread across data stores that reside on the cloud, on-premises, relational or NoSQL databases, file systems, message buses, etc. You should be able to connect to a wide range of data sources.
In today's world, we need fast, reliable connectivity to get more done. Data matters much more than it did a couple of decades back. Whether you have your data in the cloud, on-premise, behind a firewall or etc., you should be able to provide uninterrupted real-time connectivity to the data. Data can help you improve processes, understand customers, solve problems and make better decisions.
Needless to mention, you should make sure that your connectivity is secure as well so that you don't get attacked. Today's developers should be able to build robust applications that are fast and can work with massive datasets using the latest technologies.
