Data integration is the process of combining data from different sources into a single, unified view. Integration begins with the ingestion process, and includes steps such as cleansing, ETL mapping, and transformation. Data integration ultimately enables analytics tools to produce effective, actionable business intelligence.
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There is no universal approach to data integration. However, data integration solutions typically involve a few common elements, including a network of data sources, a master server, and clients accessing data from the master server.
In a typical data integration process, the client sends a request to the master server for data. The master server then intakes the needed data from internal and external sources. The data is extracted from the sources, then consolidated into a single, cohesive data set. This is served back to the client for use.
See how Talend helped Domino’s Pizza integrate data from more than 85,000 sources.
Integration helps businesses succeed
Even if a company is receiving all the data it needs, that data often resides in a number of separate data sources. For example, for a typical customer 360 view use case, the data that must be combined may include data from their CRM systems, web traffic, marketing operations software, customer — facing applications, sales and customer success systems, and even partner data, just to name a few. Information from all of those different sources often needs to be pulled together for analytical needs or operational actions, and that can be no small task for data engineers or developers to bring them all together.
Let’s take a look at a typical analytical use case. Without unified data, a single report typically involves logging into multiple accounts, on multiple sites, accessing data within native apps, copying over the data, reformatting, and cleansing, all before analysis can happen.
Conducting all these operations as efficiently as possible highlights the importance of data integration. It also showcases the major benefits of a well thought-out approach to data integration:
Improves collaboration and unification of systems
Employees in every department — and sometimes in disparate physical locations — increasingly need access to the company’s data for shared and individual projects. IT needs a secure solution for delivering data via self-service access across all lines of business.
Additionally, employees in almost every department are generating and improving data that the rest of the business needs. Data integration needs to be collaborative and unified in order to improve collaboration and unification across the organization.
Saves time and boosts efficiency
When a company takes measures to integrate its data properly, it cuts down significantly on the time it takes to prepare and analyze that data. The automation of unified views cuts out the need for manually gathering data, and employees no longer need to build connections from scratch whenever they need to run a report or build an application.
Additionally, using the right tools, rather than hand-coding the integration, returns even more time (and resources overall) to the dev team.
All the time saved on these tasks can be put to other, better uses, with more hours earmarked for analysis and execution to make an organization more productive and competitive.
Reduces errors (and rework)
There’s a lot to keep up with when it comes to a company’s data resources. To manually gather data, employees must know every location and account that they might need to explore — and have all necessary software installed before they begin — to ensure their data sets will be complete and accurate. If a data repository is added, and that employee is unaware, they will have an incomplete data set.
Additionally, without a data integration solution that synchronizes data, reporting must be periodically redone to account for any changes. With automated updates, however, reports can be run easily in real time, whenever they’re needed.
Delivers more valuable data
Data integration efforts actually improve the value of a business’ data over time. As data is integrated into a centralized system, quality issues are identified and necessary improvements are implemented, which ultimately results in more accurate data — the foundation for quality analysis.
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Data integration in modern business
Data integration isn’t a one-size-fits-all solution; the right formula can vary based on numerous business needs. Here are some common use cases for data integration tools:
Leveraging big data
Data lakes can be highly complex and massive in volume. Companies like Facebook and Google, for instance, process a non-stop influx of data from billions of users. This level of information consumption is commonly referred to as big data. As more big data enterprises crop up, more data becomes available for businesses to leverage. That means the need for sophisticated data integration efforts becomes central to operations for many organizations.
Creating data warehouses and data lakes
Data integration initiatives — particularly among large businesses — are often used to create data warehouses, which combine multiple data sources into a relational database. Data warehouses allow users to run queries, compile reports, generate analysis, and retrieve data in a consistent format. For example, many companies rely on data warehouses such as Microsoft Azure and AWS Redshift to generate business intelligence from their data.
Learn more about the differences between data lakes and data warehouses.
Simplifying business intelligence (BI)
By delivering a unified view of data from numerous sources, data integration simplifies the business intelligence (BI) processes of analysis. Organizations can easily view, and quickly comprehend, the available data sets in order to derive actionable information on the current state of the business. With data integration, analysts can compile more information for more accurate evaluation without being overwhelmed by high volumes.
Unlike business analytics, BI doesn’t use predictive analysis to make future projections; instead, it focuses on describing the present and past to aid in strategic decision-making. This use of data integration is well-suited to data warehousing, where high-level overview information in an easily consumable format aligns nicely.
ETL and data integration
Extract, Transform, Load, commonly known as ETL, is a process within data integration wherein data is taken from the source system and delivered into the warehouse. This is the ongoing process that data warehousing undertakes to transform multiple data sources into useful, consistent information for business intelligence and analytical efforts.
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Challenges to data integration
Taking several data sources and turning them into a unified whole within a single structure is a technical challenge unto itself. As more business build out data integration solutions, they are tasked with creating pre-built processes for consistently moving data where it needs to go. While this provides time and cost savings in the short-term, implementation can be hindered by numerous obstacles.
Here are some common challenges that organizations face in building their integration systems:
- How to get to the finish line — Companies typically know what they want from data integration — the solution to a specific challenge. What they often don’t think about is the route it will take to get there. Anyone implementing data integration must understand what types of data need to be collected and analyzed, where that data comes from, the systems that will use the data, what types of analysis will be conducted, and how frequently data and reports will need to be updated.
- Data from legacy systems — Integration efforts may need to include data stored in legacy systems. That data, however, is often missing markers such as times and dates for activities, which more modern systems commonly include.
- Data from newer business demands — New systems today are generating different types of data (such as unstructured or real-time) from all sorts of sources such as videos, IoT devices, sensors, and cloud. Figuring out how to quickly adapt your data integration infrastructure to meet the demands of integrating all these data becomes critical for your business to win, yet extremely difficult as the volume, the speed, the new format of data all pose new challenges.
- External data — Data taken in from external sources may not be provided at the same level of detail as internal sources, making it difficult to examine with the same rigor. Also, contracts in place with external vendors may make it difficult to share data across the organization.
- Keeping up — Once an integration system is up and running, the task isn’t done. It becomes incumbent upon the data team to keep data integration efforts on par with best practices, as well as the latest demands from the organization and regulatory agencies.
Integration strategies for business
There are several ways to integrate data that depend on the size of the business, the need being fulfilled, and the resources available.
- Manual data integration is simply the process by which an individual user manually collects necessary data from various sources by accessing interfaces directly, then cleans it up as needed, and combines it into one warehouse. This is highly inefficient and inconsistent, and makes little sense for all but the smallest of organizations with minimal data resources.
- Middleware data integration is an integration approach where a middleware application acts as a mediator, helping to normalize data and bring it into the master data pool. (Think about adapters for old electronic equipment with outdated connection points). Legacy applications often don’t play well with others. Middleware comes into play when a data integration system is unable to access data from one of these applications on its own.
- Application-based integration is an approach to integration wherein software applications locate, retrieve, and integrate data. During integration, the software must make data from different systems compatible with one another so they can be transmitted from one source to another.
- Uniform access integration is a type of data integration that focuses on creating a front end that makes data appear consistent when accessed from different sources. The data, however, is left within the original source. Using this method, object-oriented database management systems can be used to create the appearance of uniformity between unlike databases.
- Common storage integration is the most frequently used approach to storage within data integration. A copy of data from the original source is kept in the integrated system and processed for a unified view. This is opposed to uniform access, which leaves data in the source. The common storage approach is the underlying principle behind the traditional data warehousing solution.
Data integration tools
Data integration tools have the potential to simplify this process a great deal. The features you should look for in a data integration tool are:
- A lot of connectors. There are many systems and applications in the world; the more pre-built connectors your Data Integration tool has, the more time your team will save.
- Open source. Open source architectures typically provide more flexibility while helping to avoid vendor lock-in.
- Portability. It’s important, as companies increasingly move to hybrid cloud models, to be able to build your data integrations once and run them anywhere.
- Ease of use. Data integration tools should be easy to learn and easy to use with a GUI interface to make visualizing your data pipelines simpler.
- A transparent price model. Your data integration tool provider should not ding you for increasing the number of connectors or data volumes.
- Cloud compatibility. Your data integration tool should work natively in a single cloud, multi-cloud, or hybrid cloud environment.
The key to achieving full data potential
Business intelligence, analytics, and competitive edges are all at stake when it comes to data integration. That’s why its critical for your company to have full access to every data set from every source. Talend Cloud Integration Platform helps businesses consolidate data from virtually any source and prepare it for analysis with any data warehouse.
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