Data integration brings together data of any type, from one or more sources, into a single destination; often used to produce useful business information or to enable new business processes.
Businesses generate a lot of data. And it’s become increasingly distributed. Data sources are no longer limited to mainframe and applications but extend beyond the enterprise IT landscape.
Data integration is the process that lets you connect the dots between all your different structured and unstructured data sources, whether it’s social media platform data, app information, payment tools, CRM, ERP reports, or others, and plug that data into analytics and insight solutions to create actionable intelligence for better business decisions or to achieve a business objective.
As a process, it’s comprised of the many different architectural techniques, practices, and solutions companies use to achieve their data goals. Since every business has different data goals, there’s no one size fits all method. Still, data integration tools and solutions are usually comprised of a few standard building blocks: users who need data, a primary server, and a disparate or interconnected system of external and internal data sources plugged into the primary server. Users request data from the main server. The main server then intakes, aggregates, enhances, and combines the requested data from the various data sources into a unified set of data that’s delivered to the user.
Overall, data integration helps to transfer and sync different data types and formats between systems and applications. It’s not a one-and-done event, but a continuous process that keeps evolving as business requirements, technologies, and frameworks change. As organizations generate more and more data, it provides an opportunity for better business insights. Your data integration strategy determines how much value you can get out of your data, and which data integration type will work best for your use cases and initiatives.
One way of categorizing data integration is based on frequency and latency of dataflow. There are two main ways to do this: batch data integration and real-time data integration. Your data integration strategy should help guide you to selecting the best way to integrate your data. Regardless of which type of data integration you use, cost-effectiveness and speed will play into your decision.
Just as the name implies, it involves processing data in batches. A group of data are collected and stored until a specified amount is gathered, then that data is processed all at once as a batch. Batch data integration is the traditional way to integrate data and for years was the standard approach; older technologies had an easier time processing data in batches as opposed to in real-time. Batches helped save bandwidth by cutting the number of input & output events. Batch data integration is still widely used today when companies don’t have a need to gather and analyze data in real-time.
Batching is an efficient way to process data. It lets you schedule data integration at regular intervals, optimize resource allocation, and improve performance for high-volume data transformation and transfer.
A newer way to integrate data, real-time integration is triggered every time new data is available, bringing the latency to almost zero.
Companies are relying on real-time data integration and data processing more than ever to capitalize on up-to-date insights, analytics, and to help serve customers better and faster. For example, say you’re a global car rental company like Avis Budget Group. You want to cut costs, drive cost efficiencies, and increase revenue by connecting your fleet of over 650,000 vehicles worldwide with a complete global view of your business. You’d need to be able to ingest and integrate data from thousands of vehicles in real-time and quickly onboard new Internet of Things (IoT) devices, formats, and data fields as the technology shifts and changes. Many companies like Avis rely on streaming analytics platforms to achieve real-time data integration and data processing.
See “Streaming Analytics: What It Is and How it Benefits Your Business” for an in-depth overview.
There are three basic operational steps to data integration: extract, transform, and load. It starts with gathering business requirements and locating data sources. Data catalogs and data publication subscription models can help you figure out the right data. Once the sources are identified, you then need to extract or ingest the data. Typically, data is then enriched to match the requirements of the target location. Transformation can also be done after loading the data in the target application.
Two common architectural patterns of data integration are ETL and ELT.
ETL (extract, transform, and load) is the most common pattern and has been practiced for a while now. However, ELT (extract, load, and transform) is relatively new and is gaining momentum as pushdown techniques get smarter.
In a heterogenous IT landscape that spans multiple clouds and on-premises environments with several data sources and targets, it might make sense to process data locally with ETL and then send the transformed data to downstream applications and datastores.
ELT, on the other hand, is efficient if you have your data source and target in the same ecosystem. For example, for transformations within a single cloud data warehouse, ELT can be effective from both a cost and performance standpoint.
While data integration tools have evolved over time, the basic feature set remains the same. All quality data integration tools should be able to:
With cloud coming into the picture, data integration solutions can be deployed in several different ways where the IT team can offload the IT infrastructure maintenance part in steps.
Cloud data integration gives you the benefit of the cloud:
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With cloud computing in the picture, data integration solutions can be deployed in cloud environments and IT teams can offload IT infrastructure maintenance in steps.
Cloud data integration gives you the benefit of the cloud:
Serverless cloud integration takes cloud data integration a step further. IT teams do not have to manage any servers, virtual machines, or containers. They don’t have to pay anything when an application sits idle. Serverless integration also enables auto-tuning and auto-scaling for effortless data pipeline processing.
On the surface, data integration and app integration may look the same. They’re both cloud-based and give you all the benefits of cloud computing. They both take different data sources, translate, and convert them into a new data set. But the big difference is when and how they’re used.
Application integration is preferred when business processes are automated and operational data is shared between applications in real time.
Data integration is mostly used for consolidating data for analytical purposes. Generally, data integration is taken into consideration when the normalization, transformation, and reusability of data sets is required.
Check out “Application Integration: An Intelligent Way to Modernize your Data & Applications” for a deeper look.
Application programming interface (API) acts as a window to enable interaction and sharing of data among applications, systems, or services. With growing cloud and web-based products and applications, API integration has gained momentum. There is greater control over APIs when it comes to security, monitoring, and limiting access. You can custom-build private or public APIs; open your data for innovation and monetization opportunities. Companies now opt for data integration using API technology.
Companies are constantly challenged by the four Vs of big data: velocity, volume, variety, and veracity. A data integration platform helps to standardize, automate, and scale data connectivity as these variables grow or change.
Team productivity improves when you automate data workflows and reuse frameworks and templates to accommodate new data types and use cases.
A common data integration use case is consolidating data from multiple sources to a data warehouse or data lake. Data is standardized and its quality is ensured before it’s made available to any consuming applications, systems, or services.
Data integration enables a holistic view of business. The opportunity to combine the right sets of data irrespective of the sources empowers stakeholders to make fast business decisions while keeping multiple perspectives in mind. As organizations become more data-driven, analytics take precedence in decision making. The success of data science and advanced analytics projects depends on how much employees can trust and access the data they need. Timely access to valuable business insights can give a company the competitive advantage they need to stay ahead of the curve.
With the right data integration platform, it’s easier to govern the data flow. Data integration solutions ensure a secure and easy data flow between systems. The visibility of end-to-end data lineage helps ensure data governance and maintain compliance according to the corporate policies.
Data integration is core to any company’s modernization journey. The success of digital transformation initiatives depends on how well connected the IT landscape is and how accessible the data is. And now, with architectural patterns changing from monolith to service-oriented architecture to microservices, data integration among these various cross-functional components is crucial.
With a heterogeneous IT environment, data resides in siloed and fragmented locations. These could be a legacy on-premises system, SaaS solution, or IoT device. A data integration platform serves as a backbone in this fluid, ever-changing, and ever-growing environment.
Finding the ideal data integration tool for your business goals depends on identifying which product features are must-have, should-have, and nice-to-have. Here are some advanced data integration tool features you should look for:
The data available to companies is growing larger and more complex every day. From data volume to data formats and sources, data load parallelization, third-party app invocations, and more, your tools must be able to ingest and process this data quickly and accurately. Look for solutions that give you easy scalability and high-performance processing.
A must-have feature to look for in a data integration tool is pushdown optimization for the ELT operations. This feature can help you save costs by using data warehouse and ecosystem resources more effectively.
These two features are a lifesaver! Automating data integration tasks gives you a faster and easier way to extract data for insights and analytics. Job scheduling lets you easily schedule any of your data integration tasks.
Sometimes data pipelines break. This can be for a variety of reasons ranging from conditional logic errors to corrupt data. Find a tool that ensures data availability, consistency, and accuracy by easily and efficiently managing errors.
No matter what data integration solution you buy, cost is going to be a critical factor. Look for tools that can leverage artificial intelligence (AI) and machine learning (ML) to recommend the most cost-effective option for your workload.
Some data integration initiatives are common across industries, like digital transformation analytics and business intelligence projects. There are other aspects that are unique to an industry or a segment. Data integration plays a big role in how an industry innovates and addresses its data-driven use cases. There are data integration frameworks specifically tailored for an industry. Let’s take a quick look at data integration use cases across various industries.
As the healthcare system embarks on the digital transformation journey, there is an increased focus on data privacy and protection. The right data integration strategy will ensure that today’s medical systems provide valuable insights about each patient while keeping data safe and confidential.
With patient data at healthcare staff’s fingertips, it is possible for the medical systems to take a predictive approach to healthcare rather than rely on reactive methods. Data integration plays a critical role in combining real-time patient data from IoT or mobile apps with historical medical data to provide personalized care and mitigate risk.
Fighting fraud, ensuring compliance, and running complex analytics are some of the priorities that financial services institutions need to consider when choosing data integration solutions. Data governance, industry regulations and privacy issues must be taken into account before data is made available for consumption. Financial services companies are slowly and cautiously shifting to cloud with tried-and-tested cloud data integration solutions.
Government organizations are modernizing their data infrastructure to achieve mission-critical outcomes while complying with regulatory mandates. Integrating trusted data would help agencies gain real-time insights and improve decision-making. A modern data integration platform paves the way for public sector to transform and stay more connected to the people they serve.
Manufacturing companies are going through a series of automations fueled by the intelligence derived from the data they generate. Sensor data integration helps with real-time monitoring of the equipment in plants, boosting performance and ensuring production quality.
Automation and integration with the whole supplier ecosystem enables transparent transactions. Inventory and warehouse management gets easier with data integration. The orchestration of orders and deliveries helps optimize resource allocation and remove inefficiencies.
Customer experience is a big brand differentiator in the retail industry. Not only are traditional retailers setting up online stores, but they’re also going above and beyond to provide a seamless digital experience.
With the right data integration framework, retailers can get a 360-view of their customers using data from their online behavior, social media interactions, preferences, purchase history, or other data sources.
Japan for UNHCR is a nonprofit organization determined to raise awareness, help the world’s refugees, and ease the plight of displaced people. By moving from time-intensive manual data flows to the Informatica data integration platform, they Increased developer productivity, making new fundraising tools available in weeks instead of months.
Anaplan connects people, data, and plans to enable real-time planning and decision-making in rapidly changing business environments. Intelligent Data Management Cloud extracts data from more than 25 data sources and imports it into a Google Cloud data warehouse, delivering the right data, to the right people, at the right time, enabling better, faster decisions. In just four months, their teams used Informatica Cloud Data Integration to build 90 complex ETL jobs spanning 17 source systems.
The Department of Culture and Tourism - Abu Dhabi regulates, develops, and promotes the emirate of Abu Dhabi as an extraordinary global destination. Their goal was to build a cloud data warehouse with data ingestion from hundreds of integration points. Within the first two months they achieved 760 integration processes using a unified integration platform to automate application & data integration coupled with business partner process automation.
If you’re new to data integration or simply want to refresh the foundational concepts, join our "Back to Basics – Data Integration webinar series.”
Cloud data integration solutions by Informatica provide the critical capabilities key to centralizing data in your cloud data warehouse and cloud data lake:
Find out more about cloud data integration as part of our industry-leading, metadata-driven cloud lakehouse data management solution, which includes metadata management and data quality in a cloud data management platform.
