What is data integration?

Data integration is the process of gathering data from multiple sources and combining it into one connected view. An organization can’t accurately analyze its data without first transforming it, cleaning it up, and joining disparate data sets together. The ultimate goal of data integration is to more easily identify and access valuable information.

Data integration plays an important role across all industries. Every organization benefits when siloed data sources are brought together.

Why is data integration important?

The average organization has data all over the place. Data may be stored in an on-premise solution or an outdated proprietary system that was created many years ago. There’s also data in spreadsheets. There’s data in cloud systems. There’s data in the platforms and applications that employees use each day to do their jobs. The challenge of data integration is getting that data into one place, making it work together, and using it to gain new insights, solve problems, and deliver better experiences.

When done successfully, data integration helps organizations make and achieve key performance indicators (KPIs) and streamline processes across departments. In short, data integration helps businesses succeed.

Access data more efficiently
Let’s say you are a product manager. You contact your analytics team and ask for information about how many customers are licensed for a particular product and logged in over the last 10 days. Unless your organization has invested in data integration, chances are your analytics team will respond like this: “We can get that information, but it will take some time. We need to pull numbers from several databases and our CRM (customer relationship management) system to create a report. We’ll get it to you in a couple of weeks.” But you need that information now, not several weeks from now.

Data integration makes it possible to deliver valuable data insights and analysis more quickly and easily than ever before. It saves time by eliminating the need to manually gather data and assemble reports. Employees across organizations can access information without navigating through other departments, and IT and analytics teams can deliver data securely across all lines of business. This improves collaboration and builds unity between teams.

Reduce the risk of human error
When individuals manually gather data, errors are inevitable. Analytics teams must know every possible location where data may be stored for a given project or data set and have all the systems and software in place to gather that data successfully. If they miss even one source, the data set will be incomplete. And, as new data is gathered and sources are added, manually created reports have to be redone periodically to remain current.

Data integration synchronizes information in real time so reports are always accurate and up to date.

Improve data quality
Over time, data integration efforts improve the value of an organization’s data. By pulling data into a centralized system, it is easier to identify quality issues or gaps in data sets. As businesses implement changes based on these insights, new data is more accurate and builds a solid foundation for analysis.

How does data integration work?

Most data integration takes advantage of extract, transform, load (or ETL) methodology.

The first step is to extract data from all the various sources and systems. An ETL tool uses pre-built connectors to sync with data sources or queries the source application programming interface (API).

Then, data is cleaned to correct errors or deficiencies and transformed into a standardized format. This could include standardizing values like time zones, units of measurement, and currencies; validating data to eliminate missing values and duplicates; and applying any specific organization rules or compliance requirements.

Finally, the data is loaded into either a single location, like a data warehouse, or a destination system like Domo.

Data integration can also follow an ELT — or extract, load, transform — approach. This is particularly useful when working with a cloud platform. The data is extracted and loaded into the cloud and then transformed.

No matter the approach, this data ingestion process is repeated frequently so that the central data source is updated with the latest information. Domo is designed to give users access to real-time data, constantly refreshing data sets and capturing streaming data.

Common data integration challenges and solutions.

Growing data volumes
Modern systems are producing more data than ever before, and many organizations are struggling to scale with the volume. When you collect data, you have to have a place to put it. Continually adding physical infrastructure isn’t practical or cost-effective. Cloud-based data warehouses are one solution that offers affordability and scalability.

Integrating new types of data
New data types are being created in every sector. Just take a look at the growth in the last decade of IoT devices. With this in mind, organizations can design a data integration strategy that is flexible enough to integrate emerging data types with ease.

Gathering data is time consuming
Without the right technology, integrating data from disparate sources into a central format and location is time consuming and can drain an organization’s resources. With a data integration solution that is built with native connections to popular data sources and the ability to support cloud data warehouses, it is quick and simple to create data pipelines. No more developers writing custom code and diverting resources.

Data isn’t optimized for analytics
Most of the data organizations collect isn’t optimized initially for quick analytics. In order to use the data to get any real insights, it has to be transformed — cleaned and standardized — into formats that are easier to use and interpret when teams create analytics reports. The right data integration tool simplifies this process and makes sure that all data is useful data.

Modern data integration approaches and definitions.

Data fabric
A data fabric is an environment made up of a unified architecture and all the services and technologies that run on that architecture. It helps organizations manage their data more effectively by maximizing the value of their data and speeding up the process of digital transformation by integrating data management across cloud and on premises systems.

Data virtualization
Data virtualization connects and combines data to give organizations a holistic view of information. Users can access data through reports, portals, dashboards, and mobile and web applications. It makes it simple for stakeholders to access data sets with high speeds at lower costs than many traditional data integration processes.

Federated connections vs. native integration
Federated connections and native integrations are two approaches to retrieving and using data. Federated connections means that a tool like Domo connects to cloud data warehouses via federated queries, which means organizations can leave their data in their current warehouse and the BI tool can retrieve it to use in visualizations. Native integration means that a BI tool can do just that, integrate natively, within a cloud data warehouse to optimize BI.

iPaaS
The number of organizations adopting an integration platform as a service (iPaaS) is growing. An iPaaS is a platform that standardizes how an organization integrates applications. In doing so, an iPaaS makes it easier for businesses to automate their processes and share data across applications.

How different industries use data integration.

Business
Modern businesses have data coming from many sources and in many different formats:

• Social media and internet ads
• User analytics from websites and mobile apps
• Customer service databases
• Accounting and financial applications
• Sales databases
• Marketing leads
• Content management systems

Data integration helps businesses manage this data and get a complete view of everything from customers to manufacturing and supply chain operations to regulatory compliance efforts. This data informs KPIs and can even help decision makers analyze financial risks. It also improves collaboration with organizations outside of the corporation like suppliers and governmental oversight agencies.

Healthcare
Data is absolutely critical in the healthcare industry. With correct data, healthcare professionals can better care for patients, diagnose medical conditions, and advance medical research. Organizing patient data from different medical records and systems into a unified view ensures no information is lost and that the information a doctor is seeing is up to date. Effective data integration also improves claims processing for medical insurers by updating patient records and contact information in real time.

Government
From the smallest municipal government to the federal level, departments and agencies handle massive volumes of critical data. Citizen information, budgets, research and statistics, infrastructure, and public health all come into play. But the government sector is notoriously siloed, with individual departments or agencies using their own software and data systems. Plus, the average government employee isn’t a data analyst, and without the proper tools wouldn’t even know where to start to find the data sets they need. This creates a perfect storm for unused, valuable data.

Data integration between areas of government breaks down silos for more effective interdepartmental communication and productivity. It reduces costs by accelerating data processes, and with the right system, even the most non-technical government employee can easily access and share data.

What tools are needed for data integration?

Historically, data integration was a manual process that required expert analysts and many man hours. Thankfully, those days are gone.

Today, data integration tools like Domo provide a single source of truth that businesses, data teams, and IT managers can use to make disparate data assets accessible and available for business analysis.

Domo dynamically integrates with cloud systems using over 1,000 pre-built connectors and connects to on-premise and proprietary data systems.

How will data integration evolve in the future?

As more people work remotely, the need to consolidate data and easily access it will increase. More organizations will invest in cloud data warehouses, such as Snowflake, Redshift, and BigQuery, as part of their digital transformation strategy. With that investment, the need for seamless and user-friendly data integration will increase as well.

Data integration systems will adapt to interact with these cloud data warehouses in more effortless ways. Domo’s multi-cloud data fabric is the perfect example. This innovation allows Domo to integrate more tightly with data warehouses and augment what they do by connecting more sources and outputting data from across multiple cloud platforms into a single interface. It will make it easier for customers to achieve modern business intelligence for all and accelerate the speed of business transformation.