Companies accumulate vast amounts of data. Take customer information, for instance, which is often stored in a variety of discrete places across an organization, from customer relationship management (CRM) systems and lead generation software to spreadsheets and financial applications. But in each location, the information may be a little different or formatted differently. So how can the company get a comprehensive picture of its customers? The answer is data integration.
What Is Data Integration?
Data integration is the process of combining data from disparate sources into one central repository to facilitate data analysis. The data may come from enterprise resource planning (ERP) systems, CRM systems, supply chain management (SCM) systems, partner companies, vendors and other sources.
A major component of the overall data management process, data integration plays a crucial role in a business’s ability to conduct analysis and answer queries. For example, using data visualization tools that rely on integrated data, business leaders can get a complete and accurate picture of their business, which aids their decision-making.
Data Integration vs. Application Integration
Data integration is not to be confused with application integration, in which multiple independent applications, each designed for a specific purpose, work together by exchanging data. Businesses integrate applications through technologies like application programming interfaces (APIs). Data integration functions independently of applications. It captures and combines structured and unstructured data from multiple on-premises and cloud-based solutions in a central repository, where it is then analyzed.
- Data integration is a technical business process used to combine data from disparate sources into one accurate, comprehensive database.
- Data integration helps provide a high-level overview of key business information to facilitate business analysis.
- While there’s no one method to integrate data, a common technique involves ETL: Extract the data, compute any transformations and load the target database.
- The target database is usually a data warehouse where historical and current data can be easily accessed to improve operations, spot trends and predict future revenue.
Data Integration Explained
Without proper integration, it can be difficult to access the necessary data when it is needed. To create reports, for example, business users might need to manually gather data from multiple sources, which can require time-consuming actions such as logging into different accounts and importing data into spreadsheets for reformatting and manipulation. On top of that, data can be inaccurate, incomplete, inconsistent or include duplicates, leading to untrustworthy reports. When data is integrated into a central repository, businesses can extract value sooner.
But the process of data integration is more complicated than simply merging multiple datasets. Because discrete data sets have been 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 needs to be transformed into a single format. Other common problems include duplicate, missing or incorrect data. That means the data will need to be cleansed before moving it to the repository.
Data integration usually results in large amounts of data being compiled into one central repository. Fortunately, cloud storage can make data integration more feasible and affordable for smaller companies, eliminating the need to buy 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 out of the data as possible. Instead of each business department only analyzing its own data, data integration improves collaboration across the entire organization. For example, the accounting department can benefit from analyzing sales and supply chain data in addition to financial data. Access to this related information can provide a more complete picture and uncover new insights.
Integrated data is more readily transformed into meaningful, accurate information; business intelligence tools let managers see the big picture through charts and other visualizations. This gives a company an advantage over competitors that routinely deal with siloed data.
How Does Data Integration Work?
One approach to data integration, called data federation, is to leave all data where it originates. In response to queries, software gathers data from multiple sources to transform it in real time for immediate use. While the data is always current and doesn't require extra storage space, this method has disadvantages: Gathering data requires extra computer processing overhead, which may slow down transactions in business systems.
A second popular approach is to create a separate repository called a data warehouse, which centralizes data from a wide range of sources, including legacy databases, current operational systems like CRM or accounting software, websites and data lakes that store unstructured data. In order to produce an accurate, comprehensive data warehouse, it’s important to first clean the data, standardize formatting, remove duplicates and flag missing data.
Because organizations are constantly accumulating more and more data, data integration must be an ongoing process, not a one-and-done exercise.
What Problems Does Data Integration Solve?
By creating an accurate, central and easily accessible information center, data integration can solve a number of business problems:
- Information silos. Data silos reduce cooperation across departments 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 extremely large — and diverse — datasets, 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 cleansed to remove variations and duplicates.
- Data accessibility. Business units need to analyze their data with minimum effort, and decision-makers need easy and quick access to accurate data. Data integration makes this possible by providing a centralized hub where all up-to-date information is stored.
- Mergers and acquisitions. When two or more companies come together, it can be hard to share data because they may not share the same processes, methods and formatting. Data integration is a necessary step before the companies can move forward cohesively.
Benefits of Data Integration
In addition to solving certain business problems, data integration provides many benefits. Specifically, data integration can help save time and boost efficiency by making all of a company’s stored information more readily accessible and communicable between different business units. This includes both real-time and historical data. What’s more, establishing clean, accurate data is at the core of any data integration strategy. Combined, easy access and clean data enable decision-makers to make more informed decisions more readily. Spotting trends, making predictions and responding to market shifts becomes much easier.
Data integration can also help businesses find ways to lower costs and improve profitability. For example, having a high-level overview of easily consumable information fed into data dashboards can help shed light on when and where operations can be improved, even at a glance. These benefits can be specific to different business departments as well. Sales and marketing teams, for example, can create a single customer overview with engagement history. Such 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 precise and accessible.
Challenges of Data Integration
Although the benefits of data integration are clear, it can be challenging for organizations to develop a perfect repository of stored information. These challenges involve both people and data. For example:
- Implementation challenges. Successful data integration requires that businesses understand exactly what data needs to be collected, where it comes from, how it will be analyzed and what format it will be presented in, among other factors. This can be difficult, especially for organizations with large amounts of unstructured data or data being pulled in from external sources like vendors, which may not have the same level of detail as internal sources.
- Integrating with legacy systems. For companies that have data stored in legacy systems, extra steps may need to be taken to create a cohesive collection. For example, data stored on legacy systems might be missing key information automatically included on modern systems, such as time stamps.
- Data cleansing. Many companies recognize that their data may be of poor quality. Before integration, this data must be cleansed to remove duplicates, flag missing data and correct inconsistencies.
- Lack of data professionals. Companies need data teams, including data leads who coordinate with various data owners, to ensure the integration process runs smoothly. Data professionals can help companies more effectively govern data, meet regulations and keep up with demands.
- Time and effort. Transforming data into a unified, valuable resource requires a substantial manual effort to define rules. For example, companies must consider which value or format takes precedence when merging multiple databases. What’s more, data integration requires upkeep to ensure all information is always accurate and up-to-date.
Data Integration Criteria
When undergoing an integration effort, the target database is often a data warehouse. Business analytics and reporting needs will generally dictate which data should be integrated into a data warehouse. Typically, only data that needs to be shared by multiple business units would be candidates for integration. For example, it’s possible certain business units might harbor some data that must remain confidential or is only relevant internally.
Otherwise, it’s important to develop a plan for how to integrate data. Integration criteria might include aspects like granularity and semantics. For example, consider the level of detail required by your organization or whether date formatting, names or certain phrases will be understood as synonymous. Will Joe Jones require a separate entry as Joseph Jones?
Data Integration Tools & Techniques
There is no one way to approach data integration. Instead, several different techniques and tools can be used in the process. They include:
- Extract, transform, load (ETL): The process of extracting, transforming and loading data from multiple sources into the target database, usually a data warehouse.
- Extract, load, transform (ELT): The process of extracting and loading data from locations into the target database, where it is then transformed — or cleansed — on an as-needed basis. In this case, the target database is usually a data lake, which, unlike a structured data warehouse, holds large amounts of both unstructured and structured data.
- Manual data integration: A technique in which individuals manually collect data from various sources, clean it as necessary and funnel it into 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 error-prone.
- Middleware data integration: A technique in which a company uses external software to help transfer and clean data into the target database. Middleware can be particularly useful when an organization needs to access data stored in a legacy system.
- Data federation: A data integration technique in which all data from various sources is accessed from a single point but the data is left in its original source, reducing the need to transfer and store data elsewhere. This provides a unified view of data that can readily be presented or analyzed, even though it’s not actually contained to a central hub.
And post-integration, business analytics tools provide a user interface to the integrated data repository. For example, business managers can make queries and drill down to discover patterns and trends. They also can create visualizations, charts and reports to communicate their findings to other business managers.
Data Integration Example
Let’s look at a company that wants to create a single, comprehensive view of each customer. The company uses a variety of tools to help run its business, including a CRM system, ERP system, analytics tools and email marketing tools. Each business tool retains data about the company and its customers. In order for the company to get a full, accurate view of each customer, all that data needs to be integrated and standardized.
One option is to create the customer view shown in the accompanying table. In this case, the customer name is drawn from the CRM record. The sales region is derived by a lookup using the city and state in the customer record. The website provides the date of the customer’s last visit. The total revenue from this customer is calculated from the accounts receivable table. Finally, the last direct mailing campaign and customer response are from the marketing mailing list.
ROI of Data Integration
It’s clear that data integration can save companies time and money simply by providing a central data warehouse that gives decision-makers a single, accurate overview of their businesses. But that's not the only way data integration provides ROI. For example, businesses can get more out of their data if they empower employees to utilize self-service analytics — which is only possible if there's a data warehouse in place. Data accessible to all enables all business users — regardless of data analytics expertise — to extract valuable insights at the click of a button. Not only does this put data in the hands of the people, it means data professionals and IT teams can focus on more value-added tasks rather than spending time responding to queries and ad-hoc requests. As a whole, this makes it possible to make better decisions faster, ultimately improving ROI.
History of Data Integration
In the early 1980s, computer scientists began to develop systems that enabled discrete databases to send and receive information in formats both could understand. One of the first notable solutions was designed at the University of Minnesota in 1991 to help make thousands of population databases interoperable.
Despite demonstrating the feasibility of data integration on a large scale, businesses wanting to integrate data generally needed to buy additional server hardware to support the increased disk space and network processing required. Capacity planning proved challenging as the volume of data surged, requiring even more storage and networking resources. This generally limited data integration to only big companies. Today, the widespread availability of affordable cloud computing solutions has enabled growing businesses to create their own data integration systems, as well.
Despite the fact it can be an initial challenge to perform data integration — not to mention a continuing effort to manage the integration — the benefits of data integration are undeniable. As long as an organization’s data is integrated accurately and with care, organizational information can be accessed quickly and easily to gain actionable insights that will support growth and success.
Data Integration FAQs
Q: What do you mean by data integration?
A: Data from multiple discrete sources is compiled into one comprehensive database where it can readily be transformed into meaningful, accurate insights. Data can then be fed into business intelligence tools that provide decision-makers with a complete and accurate picture of their business, which aids their decision-making.
Q: What is the purpose of data integration?
A: The purpose of data integration is to collect business data in one accurate database where it can be analyzed easily, quickly and accurately to provide actionable insights.
Q: What is data integration in a database?
A: Data integration is the combining of data from disparate sources into one coherent, central database to facilitate business analysis.
Q: What is ETL data integration?
A: ETL stands for extract, transform and load. In practice, data is extracted from its sources, such as legacy databases, current operational systems (CRM, accounting, etc.), websites and data lakes that store unstructured big data. The data is then transformed — or cleansed — to remove duplicates and flag missing data. In this phase, data is merged. Finally, data is loaded into the target database, usually a data warehouse.