Business leaders often need to realize the time and energy necessary to create enterprise-wide business intelligence. They assume it’s as simple as pulling information from multiple sources, putting it all together using a spreadsheet and then feeding it into the BI tools, or perhaps working with a data integration analyst who creates intelligence from data. When the project is completed, they will be able to gain astonishing insights into company performance and market potential, as well as forecasts of revenue over the coming decade.
The process of BI is challenging, and the primary element to its success is frequently ignored – Data Integration. To ensure smooth data operations at every company, it has to be accessible at the appropriate time, in the correct place at the right moment, and in the proper form. The dispersed nature of data, which is stored in silos, is the primary reason for the inconsistency, inefficiency, and inaccurateness of your BI initiatives and other operations involving data.
In this blog, you will learn what data integration means and the process involved. Let’s begin.
What Is Data Integration?
It’s defined as the method of creating a single overview of the data residing on various applications, systems, cloud platforms, and sources for analytical and business processes. The best part is that there is no risk of duplicate, error, fragmentation, or various data types. Although an enterprise may have all the necessary information, the information likely exists in various data storage units.
Suppose you want to use a traditional customer 360-view use scenario. In that case, it is necessary to integrate the data. It could comprise data from cloud-based contact center platforms, online data, activity, marketing, sales software, customer marketing apps, customer satisfaction systems, and information from various other stakeholders. The data from all these sources must be consolidated for analysis functions or other operational tasks that could be difficult for developers or data scientists.
Defining
Data integration refers to the process of combining data from multiple sources to create a single, coherent perspective. The process begins with data input and includes cleansing, ETL, data analysis, and transformation. Data integration allows analytical tools to provide real-time, measurable business information.
ETL is an abbreviation that means remove, transform, and load. This refers to data extraction through various sources, its transformation into an entirely new form or framework, and data transfer to another final destination. ETL encompasses both data and application integration. Data integration is tightly connected to application integration. It involves the exchange of data between applications to keep them in sync. Generally, every application releases and accepts only one type of data transferred in lesser amounts. Integration with applications is ideally suited to support operational usage cases.
There needs to be a standard approach to implementing data integration. It is, however, generally composed of a set of information sources, a central or master server, and a client that accesses the data through the master or central server. There are two primary methods of data integration, one being the strict coupling method and another being a loose coupling technique. A data warehouse is the basis for the retrieval of data. By using its ETL (Extraction, Transformation, and Load) method, information from various sources is combined to form a single physical space that is tightly coupled.
An interstitial layer receives the user’s query, transforms it into a format that the source database can understand, and then sends it directly to the original database, triggering the response. When data integration is done using loose coupling, data is stored in only the database that was used to create it.
Benefits Of Data Integration
Data integration offers a variety of advantages that allow businesses to make better-informed decisions, reduce costs, and improve their competitiveness. Data integration’s benefits are:
Reducing Data Silos
Data integration combines information that comes from different systems and sources, giving a complete view. By breaking down silos of data, companies can reduce any inconsistencies or redundancies that may arise from data sources that are not linked.
Improved Data Quality
Data integration can enhance data quality by transforming it and cleaning processes. By identifying and correcting mistakes, errors, and inconsistencies, accurate, reliable data instills confidence in decision-makers.
Improved Effectiveness
Data integration helps speed up processes by making data entry less manual and eliminating the need to perform repetitive jobs. This analysis reduces errors and ensures data consistency across a company.
Rapid Time To Insight
Data integration facilitates faster access to information for analysis. This speed is vital to making quick decisions and responding to customers’ demands and emerging opportunities.
Business Intelligence Has Been Improved
Integration of data is an essential element of any enterprise intelligence project. Tools for BI rely on integrating data to produce valuable Data Visualization and analyses that help drive strategic decisions.
Data-Driven Innovation
Integrated data may reveal patterns, trends, and potential that aren’t evident when enterprise data is scattered across different platforms. As a result, organizations can innovate and develop innovative products and services.
The Process Of Data Integration
Whatever the data integration type, the processes are identical for everyone because the aim is to integrate and connect data. In this section, we will go over an overall enterprise-wide data integration framework that can be used to implement every data integration method.
Gathering Of Requirements
The initial step of data integration is to collect and evaluate the technical and business requirements. This can help you create, plan, and build a framework that will produce the desired results.
Profiling Of Data
The next step in data integration is to create a report on the data profile or assess the type of data to be incorporated. This can help you evaluate the data’s status and reveal some hidden details about its content and structure. The report on data profiling identifies empty values, field data types, repeating patterns, and other descriptive statistics that reveal potential cleansing and transformation opportunities.
Reviewing Profiles About Specifications
When you complete integration specifications and assessment reports, it’s time to identify any gaps in the reports. Many functions are required in the requirement stage that aren’t valid or do not match the profiled report of the existing data. Comparing both will allow you to develop an integration strategy that meets as many of the specifications as possible.
Design
It is this phase of the process where you must design fundamental concepts for the integration of data, for example:
- Architectural design, which shows how data moves across systems
- The trigger conditions determine what time the integration takes place or trigger triggers it.
- The updated, improved model of data and columns maps that form the foundation of the process of consolidation
- Data cleansing, standardization, matching, and quality assurance requirements have to be arranged to ensure error-free integration.
- Technology that is employed to create, confirm monitoring, and then repeat the process of integration.
Implement
After the integration procedure is designed and implemented, execution comes next. Execution can occur slowly, integrating smaller amounts of data from non-conflicting sources. Then, you iteratively increase the numbers and incorporate additional sources. This is useful in catching any issues that could arise. When the integration process for your existing data is complete, you can concentrate on integrating any new stream of information.
Verify, Validate, And Keep Track Of
During the verification stage, it is necessary to verify the effectiveness and precision of the process for data integration. A profile of the destination source is an effective method to spot any errors in the process and validate the integration process.
Data Integration Use Cases
Data integration can be used across various situations and industries to meet business issues and needs. The most common applications include:
Data Warehouse
Data integration is a method of developing a data warehouse. It is used for centralized data storage for analytics and the basic report.
Data Lake Development
The big data environment typically combines structured, semi-structured, and unstructured data. Transferring this data away from siloed platforms on-premises into data lakes can simplify gaining data value by conducting Advanced Analytics using it. These include AI (AI) and machine learning (ML).
Customer 360 Degree View
The fusion of customer information from various sources, such as CRM systems, customer relation management (CRM) systems, marketing databases, support, and platforms, allows organizations to build a single view of every customer. Well-integrated data from customer relationships helps companies better target their marketing strategies, pinpoint opportunities for cross-sells and upsells, and provide superior service to customers.
Business Intelligence And Reports
Data is crucial in creating a complete BI Architecture Design for reports and dashboards. That offers insight into various aspects of an organization’s performance, such as finances, marketing, sales, and operations.
Processing IoT Data
The integration of data from IoT devices allows businesses to monitor and control connected devices, analyze data from sensors, and automate operations based on the latest insights.
Conclusion
Data has become a necessity for the majority of modern business applications. Data refers to the process that combines data from several diverse sources to form a data warehouse’s final location. It’s an essential element in making raw data insights that drive faster, better-making decisions.
Instead of viewing information by itself, data lets you consider different types of data to see the bigger picture. In other words, instead of only looking at the client’s place of residence, Data integration integrates the customer’s demographics, social media activities, internet browsing histories, and more to construct a comprehensive personal profile of the client. This is just one of the many uses for data integration, which companies can investigate and profit from.
