Data Integration: Ultimate Guide for Your Business
Collecting large amounts of information is a pain point for today’s businesses. Industry leaders seek not only the solutions to gather massive data streams, but also tools to gain insight into the entire business system.
Data integration can be the solution to resolve multiple issues associated with the processing of large sets of data. In this article, we uncover the meaning of the suggested term, define the core types, tools, and strategies for its successful incorporation.
Since data integration (DI) can be interpreted quite differently, depending on the company’s size and enterprise-wide goals, we provide the most comprehensive specification along with useful takeaways to implement in real-life business.
What Is Data Integration And Why It’s Important For Modern Business?
The majority of definitions boil down to one: integration of data implies consolidating of datasets from various locations in a single one, turning data into a more accessible, structured, and beneficial for users format.
How Does It Work?
Typically, the integration tools need a few components for successful process accomplishment, including a set of data locations, central server, and users who obtain data from the sent to server queries.
Accordingly, the data integration (DI) process involves the following actions: 1) a user requests data from the server; 2) the server collects requested information from local sources; 3) then this information is merged by the app into a unique single format; 4) and finally, the users will get access to the required data.
Without integrating the data, it would be far more complicated to get the needed information in a speedy and structured manner. Mostly because massive quantities of data are usually stored in different hosts. Taking information from diverse sources means accessing each account, finding the data you need, copying, reformating, and analyzing it. The process turns out to be time-consuming and labor-intensive. This is why DI is very helpful.
Defining Popular Types Of Data Integration
To know what kind of DI works better for your company, consider the most common types of data integration:
Data consolidation is namely bringing data pieces together from multiple hosts, creating a combined version in single data storage. The main purpose of this approach is minimizing data storage systems. This process of unifying data can be realized by means of the ETL software, which means extracting, transforming, and loading the information. ETL pulls data out of several locations, reformats it, and then sends it to another data repository.
Data virtualization allows extracting and handling data without requesting technical details about this data and provides a common virtual view of the overall data. Unlike the traditional ETL process, the data remains in a place of origin and can be accessible by users in real-time.
Data propagation is the way to distribute data from one storage to another by copying it. The data is taken from the source data location and moved to one or several on-premise databases, following specific propagation guidance. The technologies of data replication and application integration aid in executing data propagation.
Data federation or federated database is another approach to integrating data. It uses a virtual database and creates a unified data model for heterogeneous information pulled from decentralized data sources. For this purpose, the enterprise information integration software is used.
Customer data integration (CDI) involves determining, unifying, and handling customer information via the company’s business departments to get an assembled and trusted version of customer data.
How Companies Benefit From Data Integration
A recent report from one reputing company demonstrates that data-driven organizations (those collecting and analyzing data on a massive scale) have more accurate decision-making. Namely, the businesses that exploit solutions for integration of data can boast about:
- Getting 93% more revenues
- Having 82% better sales rate
- Faster sales rate by 112%
In order to make data useful, businesses have to start their digital transformation. Here are some benefits that can be reaped from data integration:
1. DI improves cooperation and association of systems
Employees regularly need to access the company’s data to run their projects, being at offices in different cities or countries. DI allows for a high level of collaboration at a distance, enabling IT departments to securely transfer data across the enterprise channels. Plus, DI is helpful as employees not only use existing data but generate new information every day.
2. DI saves time
When DI tools are implemented successfully, organizations can save tons of time previously required for analyzing data from disparate sources. Data integration software allows creating reports with already structured information. In case you look for a dedicated team to execute your DI initiatives, Innovecs can help build efficient solutions suitable for your business needs.
3. DI helps to automate data governance
Manual collection of data is time-consuming, as an employee must know all accounts and enter each one separately to get the needed data. With solutions for integrating data, companies can automatically retrieve synchronized reports. Additionally, the DI tools constantly update these reports and always deliver the freshest information.
4. DI provides accurate business intelligence (BI)
DI demands much time and effort, but over time companies achieve the desired goals. As soon as data sets can be managed via a centralized platform, accountable managers can detect the corporate system’s errors and imperfections, improve them, and keep on running a smart business.
Defining Data Integration Techniques and Approaches
Although there are many pros of using data integration tools, very few organizations nowadays are making use of it. The recent report by Talend states that the majority of companies reveal that the prospects of using advanced technologies, such as AI, cloud, Internet of Things, and big data, have not satisfied their business needs.
Thus, to realize your data integration strategy successfully, we’ve picked some practical tips and recommendations on which DI techniques would better match your project’s goals and which approach is the most appropriate to adhere to.
Manual DI is the process of data collection by a person, who logins into multiple accounts, gathers necessary information, puts it in order, compiles, and moves for storing in a single repository. The procedure of manual data integration appears to be outdated, ineffective, and error-prone.
Middleware DI is a technique implying the participation of a third-party app that assists in structuring and moving data into the central data warehouse. Intermediary applications can be helpful in case the DI system cannot autonomously retrieve data from specific sources.
Application integration (AI). This approach can be interpreted as automated data integration because it enables accessing data once and tying it to as many apps as you have in place.
The benefit of the AI approach is high connectivity level, meaning that each time when new data is added to any of your apps, it will be automatically shared with other bound apps. This removes the need for manual work and thus eliminates employees’ errors. Plus, AIs ensure data consistency throughout the enterprise system.
Virtual integration aims to create a consolidated view of data that is requested from various separate sources. This approach represents data uniformly while keeping the data in its source location.
Common storage integration (data warehousing) works similarly to a virtual DI access approach, except for creating and storing a duplicate of the information in a data warehouse. This results in more flexibility in data management.
Taking into account approaches for data integration, have a look at the recommendations below aiding in choosing the appropriate DI technique for your project:
Choosing The Most Efficient Data Integration Strategy
In addition to the mentioned advantages and methods, we provide some more useful applications of data integration:
Handling ETL Procedures. Since ETL (extract, transform, load) involves pulling datasets from the original locations, transforming into a unified format, and transporting to a designated place. However, the traditional ETL procedure slowly shifts towards extract, load, and transform (ELT) processes.
Storing data in warehouses. Data integration efforts in big companies are often used to build large data storage locally or in the cloud, merging various data sources into a central database where information units have defined relationships for easy access. Data warehousing is a convenient way to manipulate data: create queries, analyze required data and summarize it into a consistent report.
Facilitating business intelligence (BI). A common view of data from disparate sources enables easier BI procedures. A company’s executives can comfortably get the required data bulks to check the status of a particular project or process. DI initiatives allow business analysts to get precise and trusted information for making their reports.
Using data lakes. On a large scale, the term data lakes is used to define huge repositories for both structured and raw data. These storages can be sophisticated and contain massive data bulks. More and more data is added to the company’s system every day, necessitating the use and processing of these massive datasets. Accordingly, this provokes the growing need for integration efforts to simplify these efforts and get the most out of it.
There are multiple aspects to count when initiating a data integration strategy. The following tips may be helpful when developing the right plan for you:
- Develop a data management strategy. Explore the internal processes to realize the quality of the data, what you need to analyze it, and ensure the management strategy matches the company’s goals.
- Find the best cloud provider for you. There are hundreds of vendors on the market, that’s why it’s crucial to pick the most suitable one for your current business needs and future objectives.
- Look for a DI provider thoroughly. To find a proper partner for data integration, consider the tools a provider offers, the cases it demonstrates, and client reviews.
- Choose systems to start with. Updating the corporate system as a whole is excellent, but that’s costly. It would help if you choose which ones need improvements first.
The Future Of Data Integration
The companies that keep pace with technological progress, have a significant advantage over the competitors. The modern tech solutions unveil plenty of data management opportunities to succeed in the era of digital transformation. Here are three technologies influencing data governance:
Cloud computing adopters have gained enormous profits, including improved performance and huge cost reductions. Since the cloud is a distributed storage and makes it possible to exploit as much capacity as the customer needs, it allows to minimize the time for maintenance of IT infrastructure, cut costs for the workforce, and gives customers real-time access to their data from any place.
The modern cloud providers enable companies of any scale to manipulate their data without massive investments unlike on-premise IT architecture.
Containers are a form of operating system (OS) virtualization. Nowadays they are rapidly replacing virtual machines, that require massive overhead investments, time-intensive servicing of entire hardware, and OS.
In contrast to virtual machines, containers are movable, with several portable components and minimized governance. A company using containers purchases only a part of the OS and hardware where the containers are stored, enabling data users limitless operating prospects affordably.
Serverless сomputing is the cloud computing model allowing companies to assign the maintenance of their servers on third-party cloud providers. This technology improves the effectiveness and agility of customer’s workloads, reduces entry expenses for novices, and speeds up the growth of the organizations’ IT infrastructure.
Serverless architecture is about running the code without maintaining any app’s infrastructure. Plus, serverless computing can scale data up or down according to business demand. The serverless is promising in terms of exporting your data to the utmost.
Conclusion: Seamless Data Integration With Innovecs
Businesses are treating data integration as an objective of a high priority, especially to improve data analytics and simplify business intelligence (BI) efforts company-wide. The main driver for DI initiatives is data latency hindering the collection of insights throughout all enterprise systems.
Thus, 80% of business executives say data integration is vital to their success. DI has many benefits, including:
- Improved collaboration
- Consolidation of the company’s systems
- Time and manual work saving
- Automation of data management
- Accurate running of business intelligence
To start data integration processes, company leaders have to consider the available approaches and techniques (manual, middleware, API, virtual integration, or common storage integration), assess the data needed to be integrated, choose an appropriate strategy, and find a reliable cloud provider.
Innovecs is a reputable custom software development company, offering profound expertise in many fields and delivering top-notch outsourcing services to its clients. If you need to launch and maintain complex BI and big data integration projects, our professionals are ready to help.