What is Data Connectivity?
Data connectivity is the ability to integrate different applications or data sources to exchange data between them. It is important to not only create a holistic view of data but also to enhance collaboration and enable data-driven decision-making.
The Growing Need for Data Connectivity
The data connectivity market is witnessing a shift towards the cloud and mobile devices and is projected to reach a market size of USD 6.10 billion. The growth of the data connectivity market is driven by factors such as the increasing demand for real-time information, the rising number of connected devices, and the increasing need for IoT solutions. However, a lack of awareness about the benefits of data connectivity solutions is expected to hinder the growth of this market, as well as the disparate data sources and the staggering volume of data that businesses continue to accumulate.
Challenges of Data Connectivity
Organizations face daunting data connection problems today, especially when it comes to the volume, fragmentation, and quality of that data. Some of the common challenges that businesses face when integrating data from disparate sources include:
Multiple Incompatible Sources of Data
With customer information, business data, and financial numbers spread across these varied apps, there are multiple data sources to collate. While this may seem like a straightforward task, it becomes difficult due to the incompatibility of data coming from these sources.
With data being siloed in multiple applications and databases, it is hard to aggregate and integrate the data, especially because each application has its own unique database structure. While some of these applications are cloud-based that can be integrated using cloud integration solutions, such as Azure ETL tools, there are others that are on-premise, which makes it harder to integrate with other sources.
Unstructured Data in Multiple Formats
Moreover, the data is also unstructured in multiple formats such as images and videos. To be able to derive value from this information, businesses need to convert them into structured information that can be integrated with other data sources and used easily for analytics and business intelligence tools.
Data Quality Issues with Missing Values and Inconsistent Data Types
Another challenge is related to the quality of the data being collected from various applications through various interfaces. For instance, some of the source systems may not have consistent data types or may have missing values for particular attributes or fields. In order to integrate this disparate data together effectively, businesses need a way of validating the quality of information prior to using it for analytics purposes.
How to Collate and Leverage Enterprise Data for Business Intelligence
To leverage enterprise data for business intelligence, businesses need to collate and integrate the information coming in from multiple sources into a single platform. This can be achieved with data connectivity or Salesforce integration tools that facilitate self-service, and governed data access to help users easily find, manipulate, and use the data for their specific use case.
Leveraging data for BI also requires the cleansing and standardization of data prior to integrating it with other data sources. This means ensuring that any inconsistent or missing values are removed from the source system before the data is sent to the central data repository. It also means making sure that any duplicate records are eliminated so that there is only one record for each transaction or event captured by the system.
