What Is Data Integration?
Data integration is the process of combining data from several different sources into a unified view, making it more actionable and valuable to those accessing it. Organizations across all professional fields establish data integration initiatives to analyze their data more effectively, which helps improve strategic decision making and increase the competitiveness of a business.
There is no universal approach to data integration. However, solutions generally involve a few common elements, including a network of data sources, a master server, and clients accessing data from the master server.
In a typical data integration process the client sends a request to the master server for data. The master server then intakes the needed data from internal and external sources. The data is extracted from the sources, then combined in a cohesive, unified form. This is served back to the client in a usable, cohesive form.
Why Data Integration Is Important
Even if a company is receiving all the data it needs, that data will most often reside in a number of separate data sources. Information from all of those different sources often needs to be pulled together for analysis, and that can be no small task for a professional trying to create a report or make an informed management decision.
Without unified data, a single report typically involves logging into multiple accounts, on multiple sites, accessing data within native apps, copying over the data, reformatting, and cleansing, all before analysis can happen. This is central to the importance of data integration, and it brings us to the major benefits of these efforts:
1. Data Integration Improves Collaboration and Unification of Systems
Employees in every department—and sometimes in disparate physical locations—increasingly need access to the company's data for shared and individual projects. IT needs a secure solution for delivering data via self-service access across all lines of business.
Additionally, employees in almost every department are generating and improving data that the rest of the business needs. Data integration needs to be collaborative and unified in order to improve collaboration and unification across the organization.
2. Data Integration Saves Time
When a company takes measures to integrate its data, it cuts down significantly on the time it takes to analyze that data. The automation of unified views cuts out the need for manually gathering data, and employees no longer need to build from scratch whenever they need to run a report.
Additionally, using the right tools, rather than hand-coding the integration, returns even more time (and resources overall) to the dev team.
All the time saved on these tasks can be put to other, better uses, with more hours earmarked for analysis and execution to make an organization more productive and competitive.
3. Data Integration Reduces Errors (and Rework)
There’s a lot to keep up with when it comes to a company’s data resources. To manually gather data, employees must know every location and account that they might need to explore—and have all necessary software installed before they begin—to ensure their data sets will be complete and accurate. If a data repository is added, and that employee is unaware, they will have an incomplete data set.
Additionally, without a data integration solution that synchronizes data, reporting must be periodically redone to account for any changes. With automated updates, however, reports can be run easily in real-time, whenever they’re needed.
4. Data Integration Delivers More Valuable Data
Data integration efforts actually improve the value of a business’ data over time. As data is integrated into a centralized system, quality issues are identified and necessary improvements are implemented, which ultimately results in more accurate data—the foundation for quality analysis.
Data Integration in Modern Business
Data integration isn’t a one-size-fits-all solution; the right formula can vary based on numerous business needs.
Leverage Big Data
Data lakes can be highly complex and massive in volume. Companies like Facebook and Google, for instance, process a non-stop influx of data from billions of users. This level of information consumption is commonly referred to as big data. As more big data enterprises crop up, more data becomes available for businesses to leverage. That means the need for sophisticated data integration efforts becomes central to operations for many organizations.
Create Data Warehouses
Data integration initiatives—particularly among large businesses—are often used to create data warehouses, which combine multiple data sources into a relational database. Data warehouses allow users to run queries, compile reports, generate analysis, and retrieve data in a consistent format.
Simplify Business Intelligence (BI)
By delivering a unified view of data from numerous sources, data integration simplifies the Business Intelligence (BI) processes of analysis. Organizations can easily view, and quickly comprehend, the available data sets in order to derive actionable information on the current state of the business. With data integration, analysts can compile more information for more accurate evaluation without being overwhelmed by high volumes.
Unlike business analytics, BI doesn’t use predictive analysis to make future projections; instead, it focuses on describing the present and past to aid in strategic decision-making. This use of data integration is well-suited to data warehousing, where high-level overview information in an easily consumable format aligns nicely.
ETL and Data Integration
Extract, Transform, Load, commonly known as ETL, is a process within data integration wherein data is taken from the source system and delivered into the warehouse. This is the ongoing process that data warehousing undertakes to transform multiple data sources into useful, consistent information for business intelligence and analytical efforts.
Challenges of Data Integration
Taking several data sources and turning them into a unified whole within a single structure is a technical challenge unto itself. As more business build out data integration solutions, they are tasked with creating pre-built processes for consistently moving data where it needs to go. While this provides time and cost savings in the short-term, implementation can be hindered by numerous obstacles.
Here are some common challenges that organizations face in building their integration systems:
- How to get to the finish line - Companies typically know what they want from data integration—the solution to a specific challenge. What they often don’t think about is the route it will take to get there. Anyone implementing data integration must understand what types of data need to be analyzed, where that data comes from, the systems that will use the data, what types of analysis will be conducted, and how frequently data and reports will need to be updated.
- Data from legacy systems - Integration efforts may need to include data stored in legacy systems. That data, however, is often missing markers such as times and dates for activities, which newer systems commonly include.
- External data - Data taken in from external sources may not be provided at the same level of detail as internal sources, making it difficult to examine with the same rigor. Also, contracts in place with external vendors may make it difficult to share data across the organization.
- Keeping up - Once an integration system is up and running, the task isn’t done. It becomes incumbent upon the data team to keep data integration efforts on par with best practices, as well as the latest demands from the organization and regulatory agencies.
Most of these challenges, however, are mitigated by the right data integration platform. There are free, open-source data integration solutions that will help get a business started.
How to Integrate Business Data
There are several ways to integrate data that depend on the size of the business, the need being fulfilled, and the resources available.
- Manual data integration is simply the process by which an individual user manually collects necessary data from various sources by accessing interfaces directly, then cleans it up as needed, and combines it into one warehouse. This is highly inefficient and inconsistent, and makes little sense for all but the smallest of organizations with minimal data resources.
- Middleware data integration is an integration approach where a middleware application acts as a mediator, helping to normalize data and bring it into the master data pool. (Think about adapters for old electronic equipment with outdated connection points). Legacy applications often don’t play well with others. Middleware comes into play when a data integration system is unable to access data from one of these applications on its own.
- Application-based integration is an approach to integration wherein software applications locate, retrieve, and integrate data. During integration, the software must make data from different systems compatible with one another so they can be transmitted from one source to another.
- Uniform access integration is a type of data integration that focuses on creating a front end that makes data appear consistent when accessed from different sources. The data, however, is left within the original source. Using this method, object-oriented database management systems can be used to create the appearance of uniformity between unlike databases.
- Common storage integration is the most frequently used approach to storage within data integration. A copy of data from the original source is kept in the integrated system and processed for a unified view. This is opposed to uniform access, which leaves data in the source. The common storage approach is the underlying principle behind the traditional data warehousing solution.
Getting Started With Data Integration
It is becoming ever more pressing for organizations to keep pace with the demands of modern business and the data onslaught it increasingly entails. Understanding the needs that data integration serves, the methods by which it’s accomplished, and the roadblocks that come up in implementation should provide an ample head start in discovering the best data integration option for any business or organization.
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