Data is often referred to as the lifeblood of businesses, especially when you consider that the amount of data generated daily is up to 120 zettabytes
Social media posts, databases, spreadsheets, sales reports, and API forms are common data sources for business. However, to analyze and make sense of these data sources, businesses need to extract, clean and transform them. This is where data integration comes in. Data integration helps businesses to solve data-related issues like unclean, fragmented, and inconsistent data. To put it in a better perspective, it is the process of combining data from disparate sources to provide an accurate and unified dataset in a way that makes it easy to analyze, access and use.
For a better understanding of how data integration has gained traction in recent years, let’s take a quick look at how it has evolved over the years.
Evolution of Data Integration
The evolution of data integration dates back to the 80s when computer scientists designed systems to exchange heterogeneous information. However, it was not until 1991, at the University of Minnesota, that the first data integration system was created, which, at the time, was known as the Integrated Public Use of Microdata Series (IPUMS). The traditional data integration methodology used the ETL (Extract, Transform, Load). This technique allows data from heterogeneous sources to be prepared and processed in a unified format.
Fast forward to today, data integration has gathered a lot of attention as it has proved indispensable not just for businesses that want to scale in the right direction but in every big data project. Its core importance lies in how it has made it easier for businesses to integrate complex data silos and perform automation tasks. Some data integration tools even have self-service capabilities that allow non-technical users to perform data integration tasks.
Some data integration solutions today have become more sophisticated as they now connect to other solutions such as cloud apps like Salesforce and GSuite, storage apps like GoogleDrive and Dropbox, and data warehouse solutions like Google BigQuery and Snowflake. They also integrate with other security solutions to facilitate end-to-end encryption and protect data from potential data breaches.
Benefits of Effective Data Integration
Given the huge amount of data modern businesses deal with, data integration has proved essential in achieving business goals. If an organization’s data is well integrated, it elevates its performance across the board and allows it to gain real-time intelligence and actionable insights. The following are some of the benefits of effective data integration:
Improves Data Integrity and Quality
This is the primary goal of every data integration process. It helps businesses to integrate data from multiple sources and standardize it in order to improve the overall data quality and make your data more consistent and reliable. The higher the quality of data, the better your chances of making informed business decisions from your data.
Improves Collaboration
Data Integration improves the smooth flow of data exchange between multiple teams and applications within and outside an organization. This goes a long way to enhance collaboration and operational efficiency.
Improves Decision-making
Through data integration, businesses can have a unified view of their data, which in turn increases their chances of making informed business decisions. For example, by using data integration tools, marketing teams can better manage their campaigns and determine which strategy works best for their organization.
Saves Time and Money
In business parlance, we often hear that time is money. Using a data integration tool can eliminate manual data integration processes and reduce data errors. This will save you time, boost employee productivity and ultimately save you money in the long run.
Improves Security
With API capabilities, data integration tools can easily integrate with data security tools to provide an extra layer of security to business data. What this means is that data integration tools are not only limited to extracting, cleaning, and transforming your data but can also help identify potential security threats and respond to them swiftly while meeting regulatory compliance.
How Data Integration Works
To understand how data integration works, think of a business organization that runs a global e-commerce operation. Most likely, they will be dealing with data from disparate sources like customer profiles in different locations, third-party sellers on the platform, marketing, warehouse, sales, application and accounting data. In order to get a full view of their business operation, they’ll need to synchronize all this data into a single view. Doing this involves a data integration process, which, in this case, will involve aggregating the data from their different storage systems, data ingestion, data transformation or cleansing, and unifying the data into a single source of truth for business intelligence. This process also involves maintaining data integrity and quality, as well as making the data available for use in real-time.
Generally, data integration uses a combination of people, processes and technologies to extract, load, transform/cleanse, and turn data into a single, unified repository.
Although there’s no one-size-fits-all when it comes to data integration techniques, the best approach for you depends on your data needs. You can implement a physical data integration strategy or alternatively consider a data virtualization approach.
Physical data integration is one of the oldest data integration approaches. It involves manually creating a new system that replicates data from the source systems; this is done to help manage the data independence of the original system. Conversely, data virtualization provides you with a virtual view of your data. It allows you to query, retrieve and manipulate data from various sources such as databases, big data sources, and the cloud without centralizing it.
Common Data Integration Techniques
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ETL (Extract, Transform, Load): ETL is a data integration process that combines data from different sources into a central system. This approach extracts your data from various sources and then transforms it on a secondary processing server to conform with the business rules in the target system. The clean data is then loaded into a target system for business intelligence.
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ELT (Extract, Load, Transform): ELT is the newer data integration method and a flexible alternative to ETL. With this technique, you can load raw data into a target system and simultaneously transform it. ELT is mostly useful for high-volume, unstructured datasets since it allows data to be loaded directly from the source into the target system.
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Change Data Capture (CDC): Refers to identifying and tracking changes made in data sources such as databases and data warehouses – it captures all the changes made to data, no matter how small, in real-time. CDC is perfect for cloud architectures since it is a highly efficient way to track data across a wide range of networks and keeps data in sync.
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Data Replication: This is the process of intentionally storing the same data in multiple servers in case of server downtime or heavy traffic to the server. The data replication technique also allows users to share the same data without inconsistency, regardless of where the data is being accessed.
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Data Virtualization: This is a data integration technique where users can integrate data from multiple sources without moving the data physically from its original location. In a typical data virtualization scenario, data remains where it’s originally stored but can be accessed and analyzed virtually through data virtualization tools.
Considerations for Improving Data Integration
Data integration is all about making different pieces of information work together smoothly. To improve data integration, businesses should set clear goals on what they want to achieve. Having a clear view of your business needs makes the process easier. Below are some things to consider to improve data integration:
Streamline Development and Reuse Formats
When working on different data integration projects, you should consider using consistent formats. Maintaining consistency in formats, tools, and techniques used, not only saves time and effort but also establishes a standard that makes it easier for your team to collaborate and maintain the integration solutions in the future.
Effective Configuration Management
Configuration management is a method of keeping a detailed map of your data integration landscape. It entails maintaining a record of all settings, configurations, and decisions made during the data integration process. This level of organization enables you to better understand, control, and adapt your data integration procedures. Just like an ordered file cabinet guarantees that documents can be found when needed, good configuration management ensures that data flows seamlessly and reliably across platforms.
Maintain ContinousTesting and Debugging
Testing and debugging are critical components of data integration since they aid in fixing issues. Addressing errors in your data set immediately helps you increase your chances of detecting inconsistencies, problems, or performance bottlenecks early on, making it easier and less expensive to correct them.
Establish a Common Data Model
Consider a shared data model to be a universal language that everyone in your integration ecosystem understands. A shared data model, like speaking the same language, ensures that various systems interpret and process data consistently. You can eliminate ambiguity and confusion by creating a consistent manner of arranging and describing data with a common data model. This, in turn, promotes improved team collaboration and reduces the likelihood of misinterpretation or data conflicts.
Leverage Past Investments in Legacy Systems
Do not be too quick to do away with legacy systems due to the proliferation of new tools. The truth remains that a lot of legacy systems have their unique value and purpose and can still be leveraged. So, consider integrating them with a new data technology stack instead of discarding them when modernizing your data integration. This approach allows you to capitalize on your existing investments and as such, extends the lifespan and functionality of these systems.
Applications of Data Integration
Data integration should be one of the first steps to take toward transforming raw data into meaningful information. Highlighted below are some applications of data integration:
Consumer Relationship Management (CRM)
Delivering tailored experiences requires a comprehensive perspective of consumer interactions and preferences. Data integration enables firms to aggregate customer data from numerous touchpoints, such as sales, marketing, and customer care. This allows for a more in-depth understanding of individual consumer needs and improves ways businesses can engage with their customers.
Healthcare Data Management
Data integration is critical for Electronic Health Records (EHR) systems in the healthcare sector. Using data integration techniques, Healthcare professionals can improve patient care by integrating patient data from several sources.
Supply Chain Optimization
The supply chain is another industry where data integration can be applied. Integrating data from suppliers to distributors in this industry allows for real-time monitoring, demand forecasting, and inventory management. This connection improves supply chain visibility, lowers operational inefficiencies, and allows for more flexible actions against potential problems.
E-commerce
E-commerce platforms rely heavily on data from online transactions, inventory, and customer behavior to improve product recommendations, run their targeted ads, simplify order fulfillment, and improve customer experiences.
Business Intelligence and Analytics
The foundation of strong business intelligence (BI) and analytics solutions lies primarily in data integration. Without data integration, there will be no comprehensive data analytics. The application of data integration makes it possible for organizations to extract and combine data from different sources, such as sales, market trends and consumer behavior. Analyzing this integrated data helps in identifying patterns, trends, and possibilities that would otherwise go unnoticed.
Data Integration Tools and Technologies
Gathering data from different data stores and analyzing them can be very daunting and time-consuming when done manually. Data integration tools remove this pain point by automating the process. They bring efficiency to the table by automating the process of gathering data, processing it and loading it into a target system where organizations can use it to improve products and services.
There are hundreds of data integration tools in the market, and the best option for you is the one that offers the features and functionalities you need. When choosing a Data Integration tool, you should also consider other essential factors such as:
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Affordability - is the tool within your budget?
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Cloud compatibility - is the tool provisioned in the cloud or on-premise, or can it be deployed in both scenarios?
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Ease of use - does the tool have a low or high learning curve?
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A lot of connectors - does it integrate with the tools you currently use?
Answering the above will help you select the best tool for you.
Aside from the regular data integration tools you can buy or subscribe to, some vendors offer Integration Platform as a Service (IPaaS).
Integration Platform as a Service offers a cloud-based solution for data integration. This service allows organizations to connect applications and data sources regardless of their location. iPaaS solutions allow for easy management of data integration workflows, making the process more accessible to users without extensive technical expertise.
Best Practices for Successful Data Integration
The following are the best practices to follow to implement a successful data integration strategy.
Increase Data Security Measures
If you leave your data exposed, malicious actors can take advantage by encrypting it to deny you access and ask for money or other valuables in return for the release of your data – this form of attack is known as ransomware. So, it’s always important to encrypt both data at rest and in transit as it prevents malicious actors from accessing your data. You can also implement access control measures using access management systems to ensure that only users or applications with the right credentials can access a particular data.
Collaborate with Business Units and Departments
Data integration strategy should include inputs from all departments within the organization. Toeing this path can help you customize the strategy to suit the needs and use cases of all users who may need the data in the future.
Document Data Architecture and IT System
A proper record of the tools and technologies used for your data integration processes as well as the actions performed by each team member, should be documented for reference purposes. This will allow future team members to know what action was conducted in the past and what to do within company policies.
Ensure Data Quality Management and Consistency
Nothing ruins your data like inconsistencies. However, data integration helps to eliminate this through data cleansing and validation processes. So, it is a good practice to have a dedicated data management solution for performing complex analysis on data from disparate sources as they oversee the execution of practices, policies and procedures of data management within an enterprise.
Conclusion
When you lack quality data, you are prone to make bad decisions that will cost you money and make you lose customers and your reputation. Investing in a quality data integration solution is a leeway to ensuring the quality of your data and an avenue to making accurate decisions that will give you a competitive edge and improve your business performance.
Apart from deploying the best data integration solutions in the market, it is also important to apply the best practices listed above to improve your data integration process.