Data can be messy — it isn’t always clean or straightforward. At times, it’s chaotic and can take time to decipher. Staring at a spreadsheet of metrics can feel like standing in the middle of a noisy, crowded room. It’s hard to see what’s going on until there’s order.

How exactly can companies organize and make sense of it all? Data scientists begin with data integration. It’s the process of pulling together data, and then sorting and transforming it into insight leaders can use. 

What Is Data Integration?

Data integration is the process of gathering, extracting and consolidating disparate data from various locations into one central location in order to enhance visibility and make it easier to map connections. Data integration can be performed by hand, or with the help of software and machine learning tools. 

Data integration centralizes all your data — but it’s also more than just a method of storage. 

“Data is the backbone of any data-led organization,” said Dr. Alvin Glay, VP of growth & analytics at Atlanta-based advertising company Response Media. “Proper data integration tools and protocol can improve data access as well as help with generating insights, influencing marketing activations and product innovations, and so much more.”

Data integration is the first major stepping stone between gathering metrics and putting them to good use. Here’s what it looks like, how to use it, and how it can bring order to chaos.

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What Is Data Integration?

Running any company means contending with a near constant stream of data. Leaders at an e-commerce company, for instance, need to take sales numbers, buyer demographics and site conversion metrics into account to make business direction decisions. But all these numbers come in from different locations, and that’s where data integration comes in. 

Data integration is a process for grouping together data from multiple different sources in order to get a more central and high-level view of a company’s operations. A company looking to get a unified view of their customer facing operations can use data integration to combine audience demographic data, new customer conversion numbers and ad engagement data into a customer 360-degree overview, according Amir Orad, CEO of New York-based software development company Sisense.

“Companies are able to make information more valuable and actionable by combining insights from multiple sources,” said Orad. “The information they access can help drive better ranging from something as simple as customer satisfaction to as complex as improving manufacturing processes.”

 

How Does Data Integration Work?

Exporting data from different locations and teams into a unified view is crucial for companies that want to make fully informed decisions about their business strategy. Data integration works by using data transformation and modeling technologies to clean up and organize data in one place. With the right technologies, data integration can be relatively hands-free.

“With data integration, organizations will be able to connect various departments or systems to gain new capabilities or insights they didn’t have before.”

“The technologies that make for effective integration are SQL, Python, R and semantic modeling,” said Orad. “SQL [can] pick the right data and create relationships between fact and dimension tables, [while] Python and R classify, cleanse and augment data with predictions or assessments. Semantic modeling technologies [can] make the integrated data available to less-technical end consumers.”

A robust data integration tool belt includes machine learning, plus warehousing and visualization tools to automate extraction and pull actionable insights from datasets. ETL tools like Azure Datafactory and AWS Glue can help users transform data and load it into new locations, said Maziar Adl, co-founder and CTO at Los Angeles-based IT company Gocious.

“With data integration, organizations will be able to connect various departments or systems to gain new capabilities or insights they didn’t have before,” he said. “For example, they can integrate IOT data being produced by assembly lines with maintenance systems to optimize machine maintenance, or can integrate product development statuses with customer and market data to steer new product launches.”

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Types of Data Integration

“There are several data integration approaches, and the method one chooses largely depends on the data type and accessibility,” said Glay. 

Smaller companies with less data to sort through may opt to go the manual data integration route — essentially coding, exporting and sorting data by hand. It’s the cheapest route, but also the most time consuming since there’s no additional technology at play to speed up the process. 

Another option is middleware data integration, which uses technologies that can move data from various source locations into a core data warehouse automatically. This can be more costly and requires maintenance by developers, but the upside is that middleware data integration can automate repetitive exporting processes. 

“Automated data integration reduces errors and risk in data movement,” said Ryan Francis, CEO of Chicago-based software company LaunchPad Lab. “Computers are much more reliable than humans when it comes to tedious data-oriented tasks. A business can have a greater degree of confidence in their data accuracy when systems are integrated.”

There’s also data virtualization, which integrates data from different sources into one real-time layer without requiring individuals to actually move data out of source locations; data propagation, which links and moves data between two or more locations without making any transformations to the data; and data federation, which condenses data into a central, virtual knowledge base that employees can turn to in order to get answers. Basically, there are tons of options out there for data scientists looking to make sense of their data, and each can help organizations achieve different types of goals with their data.

“Data storage has improved tremendously,” said Adl. “Relational databases in the cloud are now highly scalable. There are many options now for data integration.” 

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Benefits and Challenges of Data Integration

If done correctly, data integration can be a massive asset to companies looking to take full advantage of their incoming data. Scattered information can’t tell a story, but when linked together, it clues companies into performance and where to focus next.

“There are many different systems that capture and store data about topics such as accounting, inventory, supply, and production,” said Adl. “Data integration is the process of bringing these silos together to get new insights or manage cross functional activities that span beyond one system.”

“Once data integration is complete, analytics and visualization platforms can produce effective, actionable insights.... Then, the fun begins.”

But being able to enjoy these benefits means tackling obstacles along the way. Glay said that some of the most common challenges he’s seen have been issues with data quality, maintenance and infrastructure. With so much real time data to process, it can be easy for bad or low quality data to slip through the cracks and contaminate the system. The upkeep of data integration technologies can be taxing, and maintaining a high standard of data architecture and organization can also prove to be challenging. 

“Given these challenges, it’s critical to have solid data engineering and validation protocols to manage this process daily,” said Glay.

Any data scientist knows that the value of data isn’t what it says, but what you do with it. Numbers alone, especially if they’re siloed, can’t inform company strategy or help organizations improve. Being able to view data all in one place is the first step toward making use of it. 

“Once data integration is complete, analytics and visualization platforms can produce effective, actionable insights,” said Orad. “Then, the fun begins.”

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