Michael Chen | Content Strategist | January 4, 2024
Data sources are everywhere in the digital business world—on Internet of Things (IoT) devices, an app’s userbase in an ERP system, and within customer service requests in CRM software. With so much data constantly flowing in, how do organizations tease out the information they need? Tools such as analytics platforms can derive insights, but only if data sets are integrated to allow for making connections. Otherwise, users are left to manually wrangle spreadsheets, expending time and effort while opening up the risk of being steered off course by dated, corrupt, or duplicate data.
Technical advancements have simplified the process of combining data, storing it, and making it accessible to business users. The first step: integrate data from multiple sources. That’s key to having a data-driven organization and opens a range of possibilities involving self-service analytics and business intelligence. Empowering users to create queries themselves can lead to amazing insights.
Getting to this stage, however, requires a data integration strategy, an infrastructure capable of effectively supporting links among multiple sources, and a data lake or data warehouse.
Data integration, data blending, and data joining all start at the same step: combining multiple sources of data. These techniques differ in the level of standardization in definitions and nomenclature and where in the process transformations occur. When deciding which method to use, ask questions such as, Is the extracted data set close to your internal standards, or does it require a lot of transformation? Does the source regularly produce duplicates or other issues that need data cleaning?
By understanding the quality of your data sources, your organization will be able to move closer to your data goals.
Data integration, data joining, and data blending are three terms often used in the modern IT vernacular. They’re also often confused because differences between them are subtle. A few factors that are universal: Whether data transforms happen before or after loading into a repository, it’s often the most cumbersome and effort-intensive step in the process, so automate what you can.
Here are basic definitions and how they compare with each other.
The table below breaks down the differences among data integration, blending, and joining.
|Combining multiple sources?
|Typically handled by IT or user?
|Clean data prior to output?
|Requires cleansing after output?
|Recommend using the same source?
|Extract/load/ transform or extract/transform/ load?
Tools evolve, data sources expand, and capabilities improve. That adds up to a constant flow of opportunities to refine data integration workflows and introduce more robust and efficient processes.
While every organization has its own specific needs, data integration generally follows a standard process.
At this stage, the combined data set can be made available for the requestor to manually cleanse and analyze, or the data may be normalized by a data scientist or data curator before it’s provided to the business. Regardless of how it gets there, data sets normally require additional processes to ensure consistent nomenclature among columns, removal of duplicate data, correction of inaccurate or erroneous data, fixing incomplete records, and other tasks.
When these tasks are complete, the data is ready to be loaded into analytics applications, business intelligence systems, or even simply Excel for the end user to analyze and manipulate for insights and visualizations.
A goal for IT departments should be to maximize the efficiency of this process. This requires planning to build automated transformations that minimize manual labor. How organizations get here, though, depends on many variables: where the data sources come from, whether those sources are vetted, which fields are prioritized, whether established data rules exist, and what types of workflows are in place.
Automating as much of the data cleansing process as possible may be the most important part when using multiple data sources as it enables a self-service environment that gets data into users’ hands faster.
If it seems like a lot of effort to build a data integration process, that’s because it is. From vetting sources to crafting and refining a data cleaning workflow, a smooth data integration process takes care and planning. However, the value quickly becomes apparent.
In business, time has always equaled money. However, in the era of big data, where real-time information flows in from suppliers and customers across the globe, the importance of that simple formula has grown exponentially. Circumstances change quickly, and the ups and downs of business can often be unpredictable. When data is in silos, lines of business looking to analyze new information or explore innovation opportunities can often feel like they’re several steps behind. In truth, it feels that way because they are. When business units must rely on other teams for data extracts and analytics reports, things slow down.
In the end, information is valuable only when it flows.
Integrating data from multiple sources removes many manual hurdles. In turn, it opens the door to a wider range of data sources to uncover hidden insights and make truly data-driven decisions. This increases both capabilities and efficiency for employees, which in turn drives innovation and opportunities for the organization. Ultimately, integrating multiple data sources allows organizations to find new ideas and solutions, pivot quickly, and stay ahead of the competition.
Successful data integration keeps organizations ahead of the competition, both now and in the future as data possibilities expand. Getting there, however, requires a combination of technical configuration and understanding from an organizational perspective. By addressing these challenges, organizations stand to change the way decisions are made in operations, sales, finance, manufacturing, and nearly every other department.
Here are some benefits—and hurdles—that need to be overcome for successful data integration.
Successful data integration requires groundwork in a number of areas, including technical support, business goals, and corporate culture. The following are the three most critical boxes to check before starting a data integration initiative.
To succeed, a data integration strategy requires technology to support it, teams to manage source data and data ingestion, business users to download and effectively use consolidated data, and executive leadership to approve budgets for the endeavor. Each of these stakeholders is crucial. Without organization-wide buy-in, strategies will be derailed or, sometimes, just whither on the vine.
Organizations must determine the “why” of their data integration projects. Is it to speed up processes, enhance data analytics, gain more data-driven insights, improve data accuracy, or a combination of these? Is it specific to one department or a broader initiative?
By identifying specific goals and parameters, organizations can develop a more focused and effective approach to achieving their data objectives.
Before starting a data integration project, it’s important to understand the existing systems and data you’re working with. In the best-case scenario, data can be easily exported, and there is already agreement and alignment across departments as to formats and standards. What happens if goals, processes, or native data formats vary significantly among departments? This is where executive sponsorship comes in.
The work of integrating data from multiple sources involves several steps. Throughout the process, however, it is important to keep data quality and integrity top of mind, along with relevant data security and privacy regulations. And, once data is integrated, ensure you have regular monitoring and maintenance in place to ensure data quality and integrity over time.
Data sources come in many different formats and reside in many locations. Every organization will have a unique combination of data sources, such as the following:
Regardless of format and other variables, the most important thing is to identify and select data sources that contribute to business goals, then examine the best way to integrate them.
Once you’ve identified your data sources, it’s time to see how their data sets are formatted and defined. There are two major preparation steps.
What works best depends on the state of the individual data sets and your organizational goals. But one universal truth is that cleaning and standardization work best when processes are automated. By using tools to assist in data preparation, the entire process can be hands-off. Now, IT staff can focus on flagged events rather than manual efforts to address every data set as it comes in. Low-code and no-code tools can drive simplified transformation, while custom scripting and coding can bring more flexibility to the process.
Your integration method will play a big part in defining your overall IT structure for data. This is why it’s critical to align your resources and your business goals with the chosen method, including whether you want to build a system with continuous integration or periodic refreshes set at intervals. The following are some of the most common data integration methods:
Implementing even a well-developed data integration plan can be an involved and complicated process, but with a methodical approach, the investment will pay long-term dividends while setting your company up for a scalable future.
The process starts by identifying your data elements and data sources, then mapping the relationships among them. What cleanly overlaps? Where are columns and definitions different? And what needs to be done to align them?
From here, you’ll build a model for data transformation. You might use custom scripts, prebuilt industry tools, or a combination, depending on your needs and available resources. The goal is to transform and merge data into a common format and resolve any conflicts between data sources, preferably in a systemic way to make the process repeatable and limit the work data consumers need to do.
During this process, a range of integration tools and technologies are available to data curators and data engineers. These include ETL tools that work in three main stages.
A range of ETL tools are available across formats and platforms. In addition to traditional ETL software applications, cloud-based ETL tools allow for flexible access because these tools can more easily connect disparate sources and repositories. Assuming you have the right IT expertise, open source ETL tools can provide robust features for a low upfront cost. However, they may not have the same level of feature development, security, or quality assurance as commercial products, and that can require a further investment of resources down the road. Custom ETL tools are available, though they often require a steep upfront investment.
How do you know which ETL tool is right for your organization? Factors to consider include the types of connectors supported, the level of customization available, performance and resource requirements, and the full costs including expertise and supporting infrastructure. Maybe most importantly, ETL tools should be assessed for automation capabilities, because automation is a critical part of the systemic data transformations that ultimately lead to self-service data analytics.
A data set’s quality refers to its completeness, accuracy, timeliness, and conformity with standards. It’s difficult to overstate the importance of data quality in integrated data. A high-quality data set requires far less effort to make it ready for integration. While this is important from a resource perspective, data quality also significantly impacts the output. For example, if an organization uses four significant digits in its calculations but an outside source provides data with only two significant digits, that data does not meet the expected quality level. If it’s used, the resulting analysis may contain flawed insights.
So high quality data is absolutely critical in integrated data to minimize transformation/cleaning efforts and ensure the accuracy of output.
How to measure and maintain data quality: A number of methods are helpful to ensure high data quality.
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The main benefit to integrating data from multiple sources, such as customer demographics, sales figures, and market trends, is that employees gain a more comprehensive understanding of any given business problem or opportunity. Done right, you will uncover valuable insights and patterns that may never have surfaced when analyzing each data source in isolation. The potential result: More informed decisions, more effective strategies, better data quality control, improved operational efficiency, and a competitive advantage in today's data-driven business landscape.
What factors should I consider when selecting data sources for integration?
The two most important factors involved with planning for data integration are: First, knowing what resources you have and will have at your disposal, and second, knowing what your business goals are. From there, you can identify data sources that will forward your strategy and determine if it’s realistic to access them.
What are some best practices for integrating data from multiple sources?
While many strategies for data integration are based on individual organizational needs, some broad-stroke best practices apply across the board such as the following:
What are some examples of use cases for multi-source data integration?
Here are two real-world use cases for multi-source data integration. First, consider a smartphone application that is constantly transmitting usage data to a cloud. This becomes cross referenced with two related data sets, an email marketing campaign and sales data. A unified view can uncover deeper insights into how usage, marketing, and purchases work together. Second, consider a medical IoT device that transmits records to a patient account. This is made available to a doctor immediately, who also has access to patient records to monitor for improvements or adjustments.
Why do we need to integrate multiple sources of data?
As the volume and variety of data sources increase over time, consolidating data sets has evolved from a “nice to have” to a necessity in business. These days, it’s rare that any operation won’t benefit from data integration. The trick, though, is executing a strategy that is appropriate to the organization.
What is combining data from several sources known as?
If the process of combining data sources occurs with systemic data preparation steps, it is known as data integration. If the data sources are combined without this transformation/cleaning, requiring those steps after the fact, it is known as data joining or data blending.