Introduction to Data Integration
The key to business longevity and scalability is access to big data in which management can base informed decisions. However, without data integration, pulling information from different sources can quickly become unorganized. By implementing integration software, organizations can optimize data management and promote business expansion.
Companies need access to a collection of internal and external data, known as big data, to gain a broader scope of their business and overall performance. This information can be qualitative or quantitative and ultimately contributes to a bigger picture of how a company is running and how it can improve.
With modern data integration tools, advanced software automates data exchange and gathering between departments and third parties, eliminating the need for manual consolidation.
What is Data Integration?
Data integration is the business process of aggregating data from disparate sources into one database, allowing quick and easy access to information. This process enhances overall data management by streamlining the exchange of accurate information from multiple sources.
While there are many integration methods available to businesses, advanced technology has automated data consolidation. Data integration software is programmed by developers to automate the integration process by connecting various services and routing data to and from target systems. This integration solution gives users quick access to information, further improving accuracy and communication.
Application Integration vs. Data Integration
While application and data integration share similar qualities, each process has different goals. The data integration platform was created to respond to the booming popularity of technological databases and the need to transfer information between the linked systems, known as data at rest. Data at rest is information that is not actively moving to and from solutions, such as information stored on a hard drive, spreadsheet, or in an archive.
On the other hand, application integration handles live data from operational systems providing users with real-time information from several applications. This integration tool seeks to enable individual applications to work as one through shared data, operations, and functions. By successfully connecting applications, users can access real-time data from all existing systems through one interface.
Types of Data Integration
Data from different origins use various formats and coding, requiring unique strategies to pull, translate, and exchange the information. Conventional data integration methods include-
Data consolidation reduces the number of information storage locations by physically gathering data from separate systems and establishing an aggregated database. To successfully do this, the extract, transform, and load (ETL) technology is used. ETL pulls information from sources, translates it into a standardized format, and transfers it to a centralized database.
- Single Interface Access
- Time Savings
- Enhanced Compliance
- Optimized Data Control
- Need Software Expertise
- Security Risks
- Extensive Implementation Period
Also known as application-based integration, data propagation uses event-driven applications to copy information from one service to another whenever new data is introduced. Many models, such as the enterprise application integration (EAI) and enterprise data replication (EDR) solutions, utilize the data propagation method. Pros
- Simple Process
- Seamless Data Exchange
- Limited Human Intervention Needed
- Limited Access
- Complex Setup
- Difficult System Maintenance
Data virtualization uses a single interface to collect information from multiple sources and offer data in a uniform fashion. While the data can be viewed in one location, it can also be stored at another site. Virtualization does not require a standardized format or single point of access, but rather retrieves and interprets data within its own processes. Pros
- Minimized Repetitive Data
- Centralized Database
- Complex Setup
- Inefficient Large Batch Processing
Data federation is a virtualization model, in which it uses a virtual database to display information in one location. However, while virtualization uses a single point of access for similar processes, federation uses a standard data model that allows data from disparate sources to integrate.
Enterprise information integration (EII) applies federation to abstract data from different sources and presents it in a unified view. This allows different applications to interact with the integration solution to analyze the data with various tools.
- Short Development Time
- Simplified View of Different Data
- Minimized Storage Requirements
- Allows Tailored Data Protection
- Promotes Scalability
- Must Adhere to All Data Sharing Policies
- Unpredictable Response Time
Also known as common storage integration, data warehousing filters, reformats, and stores data in designated locations. Similar to the virtualization and federation models, warehousing provides users with centralized uniform access. However, this method creates and stores additional copies of the information in a data warehouse. Pros
- Optimized Data Analytics
- Enhanced Data Version Control
- Organized Appearance
- Increased Storage Costs
- Extensive Maintenance
Manual data integration requires individuals to collect, filter, and aggregate information without the help of automation. ProsCons
- Limited Access
- Hinders Expansion
- High Risk for Human Error
- Requires Extensive Resources, Time, and Money
- Inefficient Data Gathering
Benefits of Data Integration
While there are many different strategies, each data integration solution aims to enhance data management while saving time and resources. Modern data integration provides several other benefits, including-
Optimized Business Intelligence
Business intelligence (BI) is a blanket term that describes the overall process of utilizing technology to analyze enterprise data to help management make educated decisions. With integration, data quality is enhanced by the various tools that pull, structure, and prepare information for further analysis. The integrated data can then be generated into detailed reports and actionable insights.
Enhanced Decision Making
Advanced BI ensures decision makers have adequate information from all business processes to improve performance, functionality, and scalability. Without an integration system, data is unorganized, housed separately, and has limited access. However, by determining the proper data integration tool that addresses a company's needs, management can easily pull accurate information from multiple sources to make data-driven decisions.
Efficient Data Management
Master data management (MDM) occurs after the initial integration process, where policies and principles from the various data source administrators are enforced. This procedure is essential because it ensures all of the data on the user's end is compliant. Therefore, all low-quality and unreadable data is filtered out of the database, improving decision making and system response time. With MDM, users do not have to worry about manually converting or standardizing data, saving time and effort.
Improved Customer Relationships
Customer data integration (CDI) enables businesses to access consumer information in a structured fashion rather than having to sift through silos of documentation. With customer information readily available, companies can improve their customer service and relationships. Additionally, employees can quickly request personal and business information to answer questions and provide support.
As one of the modern integration models, data virtualization allows users to request, alter, and view data digitally, eliminating the need to pull information manually. With a robust infrastructure, integration software maintains data on the backend, allowing users to access structured data on the front-end. This advanced method reduces paper waste from physical documentation, human error, and lagging response time.
5 Steps to Implement Data Integration Software
An integration project requires research and a detailed look into a business's systems and objectives. While the process may seem tedious, each step must be completed thoroughly to integrate data properly. Successful data transformation can significantly impact how a company makes decisions, manages operations, and views performance.
Five comprehensive steps any organization can take to begin implementing data integration include-
1. Determine What Data Will Collaborate
Before a company can set up data integration, they must first have practical expectations and a good understanding of what data they wish to access. Businesses can choose to aggregate data from existing solutions, such as marketing, sales, accounting, and point-of-sale (POS) systems. Therefore, management should create an organized outline that describes-
- Source - The systems in which data is pulled from.
- Provider - The actual software in which the data lives.
- Target - The location where the data will be transferred.
2. Choose an Integration Solution that Meets the Business Need
Once the desired systems and data are determined, businesses can accurately choose what method of integration best addresses their needs. When selecting an integration platform, management should consider the price, syncing abilities, data storage, number of systems that can be connected, and batch size limitations.
3. Outline the Connectors, Objects, and Fields
Mapping out the fields that link each solution to the desired platform enables a seamless integration. However, developers should keep in mind the connectivity of their chosen platforms as it directly affects how the data will sync.
For example, bi-directional syncing allows data to update whenever new information is inputted, providing real-time data. However, this also means that all previous data will be completely overwritten. Therefore, businesses should decide whether they want to implement additional components to store historical information.
4. Set Up Filters to Refine the Integration
Now that systems are integrated, filters should be placed to refine information and ensure the platform is not overwhelmed with irrelevant data. Programming personalized filters lets the software know what information should be shared and at what time.
For example, if a marketing team implements an integration system, access to sales and customer data is essential. Therefore, filters can be placed to weed out other systems, such as inventory and ordering data, so the platform is less crowded and easy to navigate.
5. Implement Integration and Review Historical Data
Before the integration solution's final launch, management should determine if they would like to incorporate all of their historical data into the system or start fresh. Many platforms can virtually store old data separately or in the database for quick access, allowing departments to discard the physical documentation.
However, if organizations choose to move forward without uploading historical information, they will need to store this data physically. This method would require employees to sift through different systems and documentation if historical information is needed.
4 Tips for Successful Data Integration
Data integration is a significant digital transformation, requiring detailed planning and extensive efforts to implement successfully. However, there are a few tricks to ensure the process runs smoothly and yields impactful results, such as-
- Understand Data Integration Trends
Trends show that many enterprises are migrating their systems and data into cloud-based solutions due to their advanced sharing and accessibility. However, it may not seem plausible to completely convert operations for businesses that still utilize legacy systems or traditional data integration methods.
Therefore, management should develop a plan to slowly migrate the data to cloud-computing systems rather than trying to move processes all at once. Businesses should also consider the security measures of current and desired software to ensure data is protected during transference.
- Find the Most Suitable Provider
There are countless data integration tool providers on the market, each focusing on specific functions. However, advanced data integration software should provide connection flexibility, fast response time, and extensive storage. By choosing a modern solution with high functionality, companies can eliminate the need to custom code.
For businesses that still utilize legacy, or traditional, systems, it is vital to modernize established solutions to promote performance and expansion. Otherwise, companies put themselves at a disadvantage compared to other organizations with modern technologies.
Therefore, slowly replacing outdated systems with cloud-based technologies mitigates the risk of falling behind the competition. This paced evolution also allows the business to plan a budget and focus on security when transferring data from the original system to the new software.
Companies with disparate sources require a hybrid integration system capable of pulling and translating different data formats. Without an advanced integration platform, this process quickly becomes complicated, requiring customized scripts and encryption to transfer data.
Hybrid systems are developed with sophisticated architecture that can connect multiple software solutions regardless of their infrastructure and format, and securely integrate data. Therefore, businesses should consult with their IT departments to discuss the integration capabilities of their existing solutions and determine what platform enables seamless connectivity.
Regardless of the industry, every business relies on
quality data to review their performance and discover new ways of improving functionality. However, without a proper integration solution, gathering and consolidating data can be time-consuming.
With modern integration software, organizations can retrieve accurate, updated information from various systems through one interface. This not only reduces the time and resources needed to access insights but also promotes business growth.