What Is Data Integration?

Learn how data integration is the key to a mature analytics practice and more.
January 14, 2022

Data integration is the process of turning raw data into analytical models that can serve business purposes and help guide strategic decisions.  

As the quantity of data generated by business continues to grow exponentially, organizations need better ways to integrate this data in order to make sense of it. A more unified and coherent view of business activities enables more productive teamwork, makes companies more competitive, and helps analysts sleep better at night. 

With effective data integration, you can extract data from a variety of sources, including production databases, cloud-based applications, real-time data feeds and online data streams, and build a single source of truth. 

Why Is Data Integration Important?

The rise of the cloud means there is a huge volume, variety, and velocity of data constantly being produced. However, raw data is usually not directly useful. It must be understood, cleaned, and turned into models that humans can consume, interpret and use to drive business decisions. 

There are two essential uses for data integration: data analytics and data-driven automation. 

Data analytics consists of discovering insights and using patterns and trends to support decisions. Some examples include forecasting demand or recommending particular changes to product and sales strategies. Data analysis tools include data dashboards and data models. Their uses range from ad hoc reporting to using data as a product (see our Ultimate Guide to Data Integration for more details). At its most advanced, analytics can take the form of predictive modeling and artificial intelligence.

Robust, accurate data integration means goodbye to departmental data silos. It means mixing and matching data from across your enterprise. For instance, you can examine the total business impact of product and marketing changes, seeing trends that might not be obvious from simply looking at profit and loss statements. 

It also means goodbye to shadow IT analysts and headcount that departments might have otherwise hired to meet custom data programming needs. With effective data integration, people in every department should be able to produce reports, analyze data and identify trends without recruiting outside help.

Better data-driven business process automation includes instances where data feeds back into operational systems to automate common tasks, such as producing monthly reports or doing payroll. With data integration, this data can be easily produced and moved to where it’s most useful, without manual intervention — and this is what enables data to be used in near-real time.Eventually, robust data integration can enable a new generation of data-driven products through artificial intelligence and machine learning. Data can be used to train predictive models for advanced, automated decision support, as well as autonomous agents such as chatbots to respond to customer queries.

How to Integrate Data: ETL vs. ELT

Before the cloud, data integration was synonymous with the extract-transform-load (ETL) method. This extracted data from specific sources into a staging area, where the data was then transformed into a model that formed the basis for reports and primitive dashboards, eventually loading the data into a data warehouse. 

Unfortunately, with this approach, every data pipeline becomes a custom application, written in a variety of scripting languages that require specialized knowledge. This is very costly and time- and labor-intensive and sucks up loads of engineering resources. 

As the quantity of data explodes with the continued growth of the internet, ETL is unworkable. SaaS applications widen the volume, velocity and variety of data.

As a result, the modern era needs to turn the ETL notion on its head. The modern approach is to load data before transforming it, or ELT. Fortunately, with the virtually limitless storage and compute resources available in the cloud era, this is now feasible.

ELT, which stands for extract-load-transform, leverages the power of the cloud because storage is now much cheaper and more plentiful, and additional resources for computation can be provisioned in a few milliseconds.

Data Integration with the Modern Data Stack 

To enable and ensure the best possible data integration, you’ll need to have a modern data stack. We chose this term deliberately to contrast the modern data stack with older, less automated approaches to data integration. The modern data stack, in short, is a cloud-based data stack powered by automated data integration.

It is a suite of tools and technologies that includes the following:

  • Data pipelines that combine data connectors with standardized schemas and a wide variety of transformations. These pipelines should be automated and constructed with no or low-code processes.
  • Data warehouse that is secure, resilient and reliable and is based in the cloud so it can easily scale for additional compute and storage requirements. We benchmark the performance of four of the leading warehouse vendors here.
  • Data transformation tools to track data lineage, construct new data models, and leverage off-the-shelf data models for well-known metrics.
  • Business intelligence platforms that work well with the chosen data warehouse and offer automatic reporting, version control, collaboration, dashboards and data visualizations.

With the modern data stack and cloud-based ELT, analysts can leverage automated data pipelines, off-the-shelf transformations and cloud-based business intelligence, as well as scale up and down as data needs change. We can say farewell to complex data orchestration schemes when we want to combine different data sources, because they are automatically loaded in bulk to the data warehouse.

For example, businesses can continue to extract and load data concurrently while adjusting transformations. This means that transformations can be shifted downstream in the data integration workflow and can be performed by analysts using SQL and other generalized tools in the data warehouse environment. No specialized programming or scripting skills are needed. 

Making the move to a modern data stack isn’t time-consuming: in many cases, you can set up and start your testing in about an hour. You can begin reaping the benefits with data integration and better collaboration soon after.

Download The Essential Guide to Data Integration for an in-depth look at the various approaches to data integration and more. 

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