What is data integration? Why is data integration important?
Businesses collect vast amounts of data. Take customer information, for example, which is often stored in a variety of separate places across the organization, from customer relationship management (CRM) systems and lead generation software to spreadsheets and financial applications. On each site, the information may be slightly different or formatted differently, so how can a company get a comprehensive picture of its customers? The answer is data integration.
In this article, we will learn about data integration closely.
What is data integration?
Data integration is defined as the process of integrating data from different sources into one central repository to facilitate data analysis. Data may come from enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, supply chain management (SCM) systems, partner companies, and vendors. And other sources.
Information integration is a key element of the overall data management process, and plays a critical role in a company's ability to perform analysis and answer queries. For example, by using data visualization tools that rely on integrated data, business leaders can get a complete and accurate picture of their business, which helps them To make a decision.
Data integration versus application integration
Information integration should not be confused with application integration. Many independent applications, each designed for a specific purpose, work together by exchanging data. Companies integrate applications through technologies such as application programming interfaces (APIs). Data integration works independently of the applications. , it captures and aggregates structured and unstructured data from multiple on-premises and cloud-based solutions into a central repository, where it is then analyzed.
Data integration is defined as a technical business process used to combine data from different sources into one accurate and comprehensive database.
Information integration helps provide a high-level overview of key business information to facilitate business analysis.
Although there is no single way to integrate data, there is a common technique that involves ETL: extracting the data, calculating any transformations, and loading the target database.
The target database is usually a data warehouse where historical and current data can be easily accessed to improve operations, identify trends, and forecast future revenues.st future revenues.
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DocSuite Data Integration System
DocSuite's data integration system integrates data from different sources into a central warehouse, which facilitates effective data analysis, and helps bring information from ERP, CRM, SCM, and other sources, providing a comprehensive and accurate picture of the company's business. It also relies on... Techniques like ETL to extract, transform and load data into the target database.
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What is the importance of data integration?
Without proper integration, it may be difficult to access necessary data when you need it. To create reports, for example, business users may need to manually collect data from multiple sources, which may require time-consuming actions such as logging into different accounts and importing data. Data to spreadsheets for reformatting and processing.
Furthermore, data can be inaccurate, incomplete, inconsistent, or include duplicates, leading to untrustworthy reporting. When data is consolidated into a central repository, companies can extract value sooner.
But the process of integrating information is more complex than simply merging multiple data sets. Because the separate data sets were developed independently, each may store some of the same information, such as customer name or product names, in different formats. In these cases, the data must be converted to One format. Other common problems include duplicate, missing, or incorrect data, meaning the data will need to be cleaned before it is moved to the warehouse.
Data integration typically brings together large amounts of data in one central repository, and fortunately, cloud storage can make data integration more feasible and affordable for small businesses, eliminating the need to purchase expensive hardware.
Why is data integration important for businesses?
Data integration is important because it eliminates information silos, enabling all of a company's business applications and processes to extract as much value as possible from the data, rather than each department just analyzing its own data. Data integration improves collaboration across the entire organization e.g. In addition to financial data, the accounting department can benefit from analyzing sales and supply chain data. Access to this relevant information can provide a more complete picture and reveal new insights.
Integrated data is more easily converted into accurate and meaningful information; Business intelligence tools allow managers to see the big picture through graphs and other visuals, and this gives the company an advantage over competitors who routinely deal with siled data.
Importance of data integration in Doc Suite
The importance of DocSuite's data integration system is evident in facilitating access to vital data when needed, as it reduces the effort required to create reliable reports, ensures the accuracy and completeness of data, improves collaboration between company departments, and enables effective use of data to make strategic decisions based on integrated insights.
DocSuite's data integration system enhances operational effectiveness and accuracy in decision-making, and reduces the tiring effort associated with manually collecting data from multiple sources, which saves time and reduces the chances of error, and contributes to improving the quality of reports and analyses, as the data is cleaned and standardized periodically, This ensures the accuracy of the results. More importantly, DocSuite's data integration system can promote effective interaction and exchange of information within an organization, enhancing efficiency and enabling more impactful decisions based on comprehensive, integrated analyses.
How does data integration work?
One method of data integration, called data federation, is to leave all data where it originates. In response to queries, software collects data from multiple sources to transform it in real time for immediate use. Although the data is always up to date and does not require additional storage space, this The method has disadvantages: data collection requires additional computer processing overhead, which can slow down transactions in business systems.
The second common method is to create a separate repository called a data warehouse, which aggregates data from a wide range of sources, including legacy databases, existing operating systems such as customer relationship management or accounting software, websites, and data lakes that store unstructured data, in order to produce For an accurate and comprehensive data warehouse, it is first important to clean the data, standardize the format, remove duplicates, and flag missing data.
As organizations continually collect more and more data, data integration should be an ongoing process, not a one-off exercise.
What problems does data integration solve?
By creating an accurate, centralized, and easily accessible information center, data integration can solve a number of business problems:
- Information silos
Data silos reduce cross-departmental collaboration and create operational inefficiencies Data integration eliminates silos by linking an organization's data sources together.
- Working with big data
Data integration makes it easier to work with very large and diverse data sets, especially when there are multiple types of data that express the same information but are formatted differently. As part of the data integration process, data is cleaned to remove differences and duplicates.
- Data accessibility
Business units need to analyze their data with minimal effort, and decision makers need easy and quick access to accurate data. Data integration makes this possible by providing a central hub where all up-to-date information is stored.
- Mergers and acquisitions
When two or more companies come together, it can be difficult to share data because they may not share the same processes, methods, and coordination. Data integration is a necessary step before companies can move forward cohesively.
- Benefits of data integration
In addition to solving some business problems, data integration provides many benefits. Specifically, data integration can help save time and enhance efficiency by making all of a company's stored information more accessible and communicable between different business units. This includes both From real-time data and historical data, moreover, creating clean and accurate data is the core of any integration strategy. Easy access and clean data allows decision makers to make more informed decisions more easily. Spotting trends, forecasting, and responding to market shifts has become much easier.
Integration can also help companies find ways to reduce costs and improve profitability. For example, having a high-level overview of easily consumable information fed into data dashboards can help highlight when and where processes can be improved, even in... At a glance, these benefits can be specific to different business departments as well. For example, sales and marketing teams can create a single overview of the customer with engagement history. This customer data can help create a better customer experience.
Finally, data integration can help organizations save time when preparing for audits and better comply with regulations because all data is accurate and accessible.
Business integration tools and techniques
There is no single way to approach business and data integration. Instead, many different techniques and tools can be used in this process, which include:
Extract, Transform and Load (ETL)
The process of extracting, transforming, and loading data from multiple sources into a target database, usually a data warehouse.
Extract, Load and Transform (ELT)
The process of extracting and loading data from websites into the target database, where it is then transformed – or cleaned – on an as-needed basis. In this case, the database is usually a data lake, which, unlike a structured data warehouse, contains large amounts of data. Unstructured data and structured data.
Manual data integration
A method in which individuals manually collect data from various sources, clean it when necessary and route it to the target database. In general, manual integration is only recommended for small organizations with a low volume of data because the process can be highly inefficient and prone to errors.
Middleware data integration
A method in which a company uses external software to help move and clean data in a target database. Middleware can be especially useful when an organization needs to access data stored in a legacy system.
Data Federation
An integration technique where all data from different sources is accessed from one point but the data is left in its original source, reducing the need to move and store data elsewhere. This provides a unified view of data that can be easily viewed or analysed, even though it does not contain Actually on a central position.
After integration, business analytics tools provide a user interface to the integrated data warehouse. For example, business managers can perform queries and drill down to discover patterns and trends, and they can also create visualizations, charts, and reports to communicate their findings to other business managers.
Despite the fact that implementing an integration can be an initial challenge – not to mention the ongoing effort of managing the integration – the benefits of data integration are undeniable, and as long as enterprise data is integrated accurately and carefully, organizational information can be quickly and easily accessed to gain actionable insights from It will support growth and success.
Companies benefit from DocSuite's data integration system tools, as, in general, they contribute to improving the efficiency of data management, and facilitate query and analysis processes, allowing companies to make informed and accurate data-based decisions.