/ Data connectors: An important part of BI that drives success
Data connectors: An important part of BI that drives success
Businesses generate data from many different sources, and it is crucial to connect these sources into one system to gain novel insights. Data connectors are a beneficial part of business intelligence analytics platforms because it gives analysts the ability to work with data all in one place. While the importance of data connectors to businesses can’t be overemphasized, it is important to consider some factors before choosing a data connection tool for your business. This will make sure you integrate your data in a timely manner in order to build efficient analytical solutions.
Factors to consider before choosing data connectors
Here are factors to consider before choosing data connecting strategies as part of a business intelligence (BI) tool:
Data sources supported
Security and compliance
Minimize operational risks
The volume of your business data needed for integration at the beginning may not be much. However, the volume tends to increase as your business scales. You’ll want to choose a BI tool with data connectors that can easily scale per your business data needs.
Data sources supported
Just like the volume of data increases with business growth, the complexity of integration of business data also increases. It is important to recognize that there are a variety of different systems and data storage methods used within different departments of an organization. As an organization or department grows, these systems may change. With this in mind, choose a data connector that can accommodate your ever-growing number of data sources.
Security and compliance
Data can determine business growth and failure. As one of the critical parts of a business, you need a business data connecting tool that will offer top security and compliance to your resources.
The business data extracted from various sources exists in different formats. For a thorough analysis, it’s important to get the data into the appropriate format. This process is known as data transformation. Choose a data connector that supports seamless data transformations based on your use case.
Minimized operational risks
Executing analytics jobs within operational systems could lead to the risk of expensive analytics jobs interfering with the functions of operational systems. Using data connectors to gather data from disparate sources into the data warehouse of a full-stack BI tool eliminates this risk.
Different methods of data integration
Combining business data from different sources into a unified system can be achieved through data virtualization, manual integration, and application integration.
Many companies that don’t utilize data connectors within a BI tool use manual data integration to gather data from multiple sources into a single system before adopting a systematic approach. In this method, business analysts log into source systems manually to analyze and export data before creating reports from their data analysis. Manual data integration has many drawbacks, such as time consumption, outdated data, and security risks from analysts requiring access to different operating systems.
Data virtualization is another approach a company may use to integrate their data. Business data that is collected from different sources is kept in a place and accessed through a virtualization layer. The virtualization layer, which serves as a single data storage, joins responses from different source systems to a central unit. Data virtualization has its setbacks, though; analysis jobs are carried out on operational systems and this interferes with their functioning.
In this method, many applications are linked together and data is copied from one application to another. This data integration method also has its drawbacks. It involves multiple copies of data across different source systems, which increases cost and traffic between different systems. With data replicated across multiple applications, it’s challenging to know which data is the most accurate.
This method involves seamlessly incorporating business data from different sources into a centralized system. In a full-stack BI tool, this central location is a data warehouse. Through data connectors, information is accessed and analyzed by an organization from a single location, eliminating data redundancy and ensuring that everyone has access to the most current data. This is the best method to use for integrating all your data into a single location, as it offers many benefits.
The benefits of data connector integration
The benefits of data connectors include the following:
Consolidating data from different sources into one location using data connectors is beneficial for many different departments as they aim to make their processes more efficient. Departments such as logistics, finance, IT, sales, and marketing can all benefit from insights gained from accessing and analyzing data in real time.
Going to different departments and systems to collect data is a long and challenging task. It reduces productivity and increases costs. By utilizing data connectors within a full-stack BI tool, analysts are able to easily collect different kinds of data from different systems.
Predicts the future
Historical data collected through seamless data connector integration allows you to predict the behaviors of customers and generate forecasts using machine learning tools.
Having business data integrated into a centralized system enables business analysts to request the data they need directly from the central system instead of operational source systems. With this, security is improved as business analysts don’t need admin-level access to each operational system within the organization.
The bottom line
Every business has, in one way or the other, implemented various data analysis practices using dispersed data. Data connectors allow you to integrate data from different sources into a centralized system. This results in many benefits, such as making better business decisions, improving security, and providing a seamless flow of information that improves the data management process.
Check out some related resources:
Modern BI for All Field Guide: Data Agility
Product Overview: Domo Integration Cloud
Building Data Integrations on a Modern BI Platform
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