What is Data Integration and Why Does It Matter to Your Company?
Every day, every firm generates a large amount of data. For storing enterprise data, a typical company uses many on-premise systems and hundreds of apps. As a result, data is one of the most important components of today's enterprises. While organisations generate massive amounts of data every second, that data is useless unless it is properly managed and integrated. A large amount of data can accumulate and become unmanageable. This is when data integration enters the picture. The procedure has become one of the most important aspects of the whole data governance procedure. With the growing requirement to communicate existing data quickly, businesses are increasingly turning to data integration. A master server, a network of data sources, and users accessing data from the master server are all common features in data integration procedures. Businesses may make smarter decisions, provide better customer experiences, and align divisions to work better together when they use this single data collection. To level up their data, modern startups and companies rely on advanced technologies like Enterprise data warehouses, data lakes, and software app development.
So, without further ado, let's learn about data integration and why it's the next big thing in business.
So, what exactly is data integration?
Integration of data
The process of merging data from various sources into a single, cohesive perspective is known as data integration. The intake procedure is used to start the integration process.
Customer data integration, for example, entails gathering information about each customer from several business systems such as sales, marketing, and accounts, and combining it into a single picture of the customer for reporting, analysis, and customer care.
Data integration can combine various types of data – structured, unstructured, batch, and streaming – to perform tasks ranging from simple inventory database queries to complicated predictive analytics.
Let's look at the advantages of implementing data integration in your organisation now that we know what it is.
What is the significance of data integration?
Importance of data integration
Big data integration is becoming increasingly popular.
The data integration market is expected to reach USD 19.6 billion by 2026, according to Global News Wire.
Whether a company wants to join databases with partners or a government department wants to reduce data silos between departments, data integration tools can help alleviate the time-consuming data manipulation process.
On that point, here are a few benefits of implementing data integration solutions in your company.
1. Increases efficiency and saves time: When a company incorporates data integration techniques into their business processes, the time it takes to prepare and analyse data is greatly reduced. The use of automated unified views also eliminates the necessity for human data collection.
The time saved on these duties can be utilised for analysis and execution, resulting in a more productive and competitive firm.
2. Reduces errors: There are a lot of them. When it comes to a company's data resources, there's a lot to keep track of. To manually collect data, personnel must be aware of every place and account they might want to investigate. Furthermore, they must first install all necessary software in order to verify that their datasets are accurate and full. If a data repository is added without the employee's knowledge, the dataset will be incomplete.
When data integration is in place, your staff will always have access to the most up-to-date and correct data. It also minimises errors by reducing tiresome human labour and data manipulation.
3. Facilitates better business decisions: Data integration enables for transparent internal business processes. Data integration systems allow you to use any data you choose.
4. Enhances collaboration: For business projects, employees from all departments – and often from different physical places – require access to the company's data. Employees in practically every department are also improving and producing data that the rest of the company requires.
Everyone may access a single view of data from throughout the enterprise by integrating data. This improves organisational collaboration and cohesion.
5. Provides more useful data: Over time, the data integration process increases the value of a company's data. Quality concerns are detected and solutions are addressed as data is merged into a centralised repository system, resulting in more accurate data – the foundation for quality analysis.
Techniques for integrating data
When data comes in from both external and internal sources, big data integration is required. Depending on the complexity, discrepancy, and number of the data sources, different types of data integration strategies are used.
Let's take a look at the many sorts of data integration approaches and see how they might help improve business intelligence processes one by one.
1. Data consolidation: Data consolidation is the process of combining data from several sources into a single repository. This consolidated repository is then used for data analysis and reporting, among other things.
Data latency is a crucial element that distinguishes data consolidation from other data integration strategies. The overall time it takes to retrieve data is known as data latency.
A lower latency interval means more recent data is available in the data repository for business intelligence and analysis.
This delay might range from a few seconds to hours or more, depending on the technology utilised for data integration and the needs of your organisation. With advances in data technologies, it is now possible to condense data and send changes to the destination in near real-time or real-time.
2. Data propagation: Data propagation uses event-driven applications to copy data from one area to another. Data propagation can be accomplished via enterprise data replication (EDR) and enterprise application integration (EAI). EAI provides a link for reasons such as business transaction processing, while EDR is more commonly used to transport data between two databases.
3. Data federation: This technology generates a virtual database that combines information from multiple sources. The virtual database is then used by users as a single source of truth for all data in the organisation.
Data abstraction is used in this technique to establish a unified user interface for data retrieval and access. As a result, whenever a user or programme requests the virtual database, the query is delivered to the appropriate underlying data source.
In other words, the data in the data federation is available on demand.
4. Data warehousing: This sort of data integration comprises cleansing, formatting, and storing data in a shared storage area. Data from all applications across a business is copied to the data warehouse using this method.
Analysts don't have to worry about affecting the performance of the app or software by querying data on the warehouse rather than the source apps.
Furthermore, data analysts can access all of the data in one central area, allowing them to check for accuracy and consistency.
5. Middleware data integration: Middleware data integration entails connecting source systems to a central data repository via a middleware application. Data is validated using middleware before being sent to a repository, which could be a cloud data warehouse or a database.
Real-life scenarios are used to integrate data.
logos of usage scenarios
Data integration is the first step toward realising its full value. When When businesses have all of their data in one location, it becomes easier to find the most accurate and important insights. A majority of businesses, including healthcare, telecommunications, and retail, now use data integration software. These industries are using data integration approaches to better their business operations, which offers them an advantage.
Here are some real-world examples of data integration:
that demonstrate how this strategy may benefit firms in a variety of industries.
1. Healthcare: Treating patients necessitates extreme caution and access to their medical records. Patient data was previously dispersed across multiple platforms, compromising care. Hospitals, on the other hand, have begun to integrate as much data as possible into a single comprehensive record through data integration.
2. Retail: Every day, retailers deal with massive amounts of data. As a result, having all important data in one location is critical to their performance tracking. Data integration enables retailers to efficiently monitor sales, inventory, and other critical indicators across their many stores and platforms.
3. Finance: The financial sector has begun to embrace data integration for fraud protection and detection, credit risk assessment, cross-sell/up-sell opportunities, and customer retention.
4. Marketing: Data integration has become increasingly crucial in marketing. It entails distributing messages to the appropriate audience at the appropriate time. Marketing initiatives can quickly devolve if they are not precisely tuned.
Information management Without suitable data integration routes and tools, managing information on hundreds, if not millions, of customers is impossible. It could result in underwhelming efforts and squandered marketing funds. The only way to keep data current and organised is to integrate it.
5. Telecoms: In telecommunications, excellent customer service is critical, and data integration is critical. Data from many sources is combined to create a 360-degree perspective of the organisation and consumer interactions. It is possible to identify and fix issues that lead to low customer satisfaction and increased customer service requests.
Data integration challenges
Unifying inconsistent data silos, keeping up with expanding data volumes, and resolving data quality issues are all common obstacles that data management teams face when it comes to data integration. In companies having international operations, Data volumes are rapidly increasing, and this trend will likely accelerate as IoT develops and the adoption of low-cost cloud object storage services grows.
Data integration software is essential for maximising the value of all that data, but organising and planning the necessary integration labour is a difficult task.
frequent issues that businesses face when developing integration systems:
1.Difficult to set up and use: Getting started with data integration can be daunting. This is especially true for large companies with scattered data silos and no one data source of truth. Enterprises that want to make data more accessible to the general public need a data integration strategy that ensures data governance and accessibility.
2.. Data from newer sources: New systems generate various sorts of data, such as real-time or unstructured data, from various sources, such as movies, sensors, and Internet of Things devices. It's vital for your organisation to figure out how to swiftly change its data integration infrastructure to meet the needs of integrating all of this data. The volume, pace, and new format of data, on the other hand, provide new obstacles.
3. Maintaining data integration: Once an integration system is up and running, your job isn't done. It is critical for the data team to keep data integration efforts up to date and in line with best practises, as well as the most recent requests from the organisation and regulatory agencies.
4. Learning new data types can be difficult: The number of data kinds generated is growing.
What role does Syoft play in data integration?
The benefits of data integration in the workplace are numerous. Data integration can improve your company's efficiency by providing clear insights into business trends and performance across all divisions, as well as boosting customer experiences.
Our highly-experienced team of professionals at Syoft will assist you in implementing automated data integration software for your organisation while also addressing all of your data difficulties.
Regardless of the kind, structure, origin, or location of the data, you can establish a modern data architecture for your business with our help.