Every business generates tons of data every day. A typical business uses several on-premise systems and hundreds of applications for storing enterprise data. Thus, data is one of the most crucial components using which today’s businesses are thriving.
Now, while businesses are producing huge amounts of data every second, that data is of no use if it is not adequately managed and integrated. Tons of data can get piled up and ultimately become unmanageable. And this is where data integration comes into play.
The process has emerged as one of the primary components in the overall data governance process. With the rising need to share existing data promptly, enterprises are employing data integration with increasing frequency.
Data integration processes typically involve a few common elements, including a master server, a network of data sources, and users accessing data from the master server. Working from this one data set allows businesses to make better decisions, drive better customer experiences, and align departments to work better together. Modern startups and enterprises rely on advanced solutions such as Enterprise data warehouse, data lakes and software app development to level up their data.
So, without further ado, let’s understand the concept of data integration and why it’s the next big thing for enterprises.
What is data integration?
Data integration is the process of combining data from different sources into a single and unified view. Integration begins with the ingestion process and uses the ETL approach that extracts, transforms, and loads data from different sources into a unified view. Data integration ultimately enables analytic tools to produce actionable, effective business intelligence.
For instance, customer data integration involves extracting information about each customer from disparate business systems such as sales, marketing, and accounts, which is then combined into a single view of the customer to be used for reporting, analysis, and customer service.
Data integration can consolidate all kinds of data – structured, unstructured, batch, and streaming – to do everything from basic querying of inventory databases to complex predictive analytics.
Now that we know what is data integration, let’s get into the benefits of using data integration in your business.
Why is data integration important?
The demand for big data integration is continually growing.
As reported by Global News Wire, the data integration market is estimated to reach USD 19.6 billion by 2026 from USD 11.6 billion in 2021, growing at a CAGR of 11%.
Whether an organization is looking to merge databases between partners or a government department is looking to eliminate data silos between departments, data integration systems can play an essential role in mitigating tedious data manipulation methods.
So, on that note, here are a few advantages of using data integration solutions for your business.
1. Boosts efficiency and saves time: When a company integrates techniques like data integration into their business processes, it significantly cuts down the time it takes to prepare and analyze that data. The automation of unified views also eliminates the need for gathering data manually.
The time saved on these tasks can be used for analysis and execution to make an organization more productive and competitive.
2. Reduces errors: There’s a lot to keep up with when it comes to a company’s data resources. To gather the data manually, employees must know every location and account they might want to explore. Plus, they must install all the necessary software before they even begin to ensure their datasets will be accurate and complete. In case, a data repository is added, and that employee is unaware, they will have an incomplete dataset.
When data integration is in place, your employees have access to the most accurate and real-time data whenever needed. It also reduces tedious manual work and data manipulation, thereby minimizing errors.
3. Helps in smarter business decisions: Data integration allows transparent business processes within the enterprise. Data integration systems give the flexibility to use data in different internal systems in whatever way the businesses want. This allows them to understand data clearly. Therefore, any decision regarding business processes can be made quickly and smartly.
4. Improves collaboration: Employees in every department – and sometimes in disparate physical locations – need access to the company’s data for business projects. Additionally, employees in almost every department are improving and generating data that the rest of the business needs.
By integrating data, everyone can access a unified view of the data from the entire organization. This improves collaboration and unification across the organization.
5. Delivers more valuable data: Data integration process improves the value of a business’ data over time. As data is integrated into a centralized repository system, quality issues are identified and improvements are implemented, resulting in more accurate data — the foundation for quality analysis.
Techniques used for data integration
The need for big data integration arises when data is coming in from external as well as internal sources. This is achieved using different types of data integration techniques, depending on the complexity, disparity, and volume of the data sources.
Let’s get into the different types of data integration techniques one by one and see how they can help improve business intelligence processes.
1. Data consolidation: Data consolidation combines data from different sources to create a centralized data repository. This centralized repository is then used for various purposes, such as data analysis and reporting.
A key factor that differentiates data consolidation from other data integration techniques is data latency. Data latency is defined as the total time taken to retrieve data from different sources to transfer it to the data store.
A shorter latency period means fresher data is available for business intelligence and analysis in the data store.
Depending on the technologies used for data integration and the needs of your business, this latency can range from a few seconds to hours, or even more. However, with advancements in data technologies, it is possible to consolidate data and transfer changes to the destination in near real-time or real-time.
2. Data propagation: Data propagation uses applications to copy data from one location to another on an event-driven basis. Enterprise data replication (EDR) and enterprise application integration (EAI) can be used for data propagation. While EDR is more frequently used to transfer data between two databases, EAI provides a link for purposes such as business transaction processing between two systems.
3. Data federation: This technique creates a virtual database that consolidates data from various sources. Users then use the virtual database as a single source of truth for all the data in the organization.
In this technique, data abstraction is done to create a uniform user interface for data retrieval and access. As a result, whenever an application or user queries the virtual database, the query is sent to the relevant underlying data source.
In other words, the data is served on an on-demand basis in the data federation.
4. Data warehousing: This type of data integration involves using a common storage area to cleanse, format, and store data. In this technique, data from all applications across an organization is copied to the data warehouse. From here, it can be queried by data analysts.
Querying data on the warehouse instead of the source applications means that analysts don’t have to worry about impacting the performance of the app or software.
Plus, data analysts can view all of the data in a central, single location, which means they can check for data’s accuracy and consistency.
5. Middleware data integration: Middleware data integration involves using a middlew
