How to Integrate Data from Multiple Sources

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.

What Is Data Integration?

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 vs. Data Joining vs. Data Blending

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.

• Data integration: The systemic, comprehensive consolidation of multiple data sources using an established process that cleans and refines data, often into a standardized format. When this cleansing is complete, data is loaded into a repository such as a data lake or data warehouse. Transformations and integration are often handled by data curators, data scientists, or other IT staff.
• Data blending: The process of combining multiple data sets into a single data set for analysis. However, unlike data integration, blended data often combines native data—that is, data that has not been transformed or cleansed—from multiple sources. For example, an HR team will use blended data if they combine the current quarter’s internal hiring metrics with an open-source data set from the government on hiring trends. Unlike data integration, this example requires the user to clean and standardize the data after it’s blended.
• Data joining: Like data blending, data joining involves combining multiple data sets. The biggest difference from data blending is that data joining requires that data sets come from the same source, or at least have some overlap between columns and definitions. To expand on the HR example above, data joining occurs when the HR professional takes government-sourced national hiring metrics from the current quarter, then also downloads the quarterly data from four years ago for comparison. These data sets come from the same system and use the same format but need to be joined based on specific data ranges. This joined data can then be further blended into an internal HR data set, which then requires cleaning and standardizing.

Key Differences

The table below breaks down the differences among data integration, blending, and joining.

Key Takeaways

• Vet data sources with your goals in mind. While you can’t always control the quality of sources in a big data world, there are steps you can take to make integration easier.
• Automate as much of the process as possible. When data will be pulled from sources at high frequency, tools and scripts will greatly benefit your organization’s integration effort.
• To decide which data integration method works best for your organization, map out all of the variables involved—sources, hardware, volume.
• Continually refine your workflow and standards. Successful data integration requires continuous process improvement.

Integrating Data from Multiple Sources Explained

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.

1. A need for combined data is identified, either from an end user request or a decision made by the organization. This often includes parameters such as date ranges and other limiting factors.
2. Relevant sources are identified, along with the specific data needed from those sources.
3. Data is extracted from the sources in native format and combined into a data set.

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.

The Importance of Integrating Data from Multiple Data Sources

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.

Benefits and Challenges of Integrating Data

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.

Benefits

• Unified data. By bringing data together into a single repository, the overall data acquisition process is simplified and sped up. Instead of different groups working with disparate data sources, a single unified view creates better organizational alignment while reducing the resources involved in procuring and processing the data.
• Improved collaboration. Because of how data is traditionally stored, various groups may be working with outdated or slightly different versions of a data set. Use of different definitions or nomenclature can introduce confusion or lead to erroneous conclusions. Unifying data allows groups to all work with the same information.
• Streamlined operations. When data sharing happens only with manual requests and preparation, work slows down. Operations teams will benefit from streamlined processes, centralized data, and fewer manual steps.
• Saved time. In addition to streamlining operations, consolidating multiple sources removes the practical step of manual data transfer from group to group. Delays can happen when adjacent groups, such as sales and marketing, have overlapping data needs or when downstream participants in a workflow need to request data sets.
• Reduced manual errors. Removing manual steps from processes adds efficiency, but it also reduces overall risk. Fewer manual steps equal fewer opportunities for errors, such as sending the wrong data set or missing records when copying/pasting.
• Improved predictive analytics. The more data sources available to analytics platforms, the better. Data source consolidation widens analysis possibilities, empowering creativity and innovation. This creates the immediate benefit of more users taking control of business analytics and the long-term benefit of building a data-driven culture.

Challenges

• Data compatibility. Data coming in from different sources will almost certainly use different definitions and nomenclature. The process to clean that up is known as data transformation, and depending on the state of the original sources, can be unwieldy and complex unless systemic processes are in place.
• Data silos. Groups, including as sales, marketing, finance, and HR, all track data for their own internal needs. When data is siloed off like this, groups must make manual requests for access, and even when they receive it, nomenclature and definitions may vary, creating further hurdles to compatibility.
• Data quality. High-quality data makes possible a culture of accurate, data-driven insights. To get there, organizations need to establish standards and processes to ensure data quality. Accuracy, completeness, and update cadences (if using periodic refreshes) all need to be part of the discussion. Improving decision-making across departments requires a combination of IT infrastructure, group workflows, and individual buy-in to meet standards.
• Legacy systems. Data is generated by a wide range of systems, including legacy tools. Integrating these sources cleanly into a consolidated repository requires assessing the state of legacy system output, then figuring out how to make it compatible. Don’t skip this step; these older systems often contain nuggets of information unique to the organization.
• Unoptimized data. Data optimization refers to the process of making analysis operations as efficient and cost-effective as possible. Unoptimized data arrives natively from sources and will need to be propagated into appropriate values and records before use. An OLAP tool can automate this process.

Preintegration Checklist

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.

1. Get Stakeholder Buy-In

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.

2. Align Project with Business Goals

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.

3. Analyze Your Existing Data Processes

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.

5 Steps to Integrate Data from Multiple Sources

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.

1. Identify Which Data Sources to Integrate

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:

• Relational databases: Data integration processes can connect directly to relational databases, which have standard tabular row/column setups.
• Flat files: Most databases export data sets in flat files, which format as two-dimensional tables that provide standalone context without any needed reference to other tables. Popular export formats include CSV and delimited and are generally easy to transform as needed.
• XML and JSON: XML and JSON are common standards for modern data transmission, particularly for the web and web-based applications. In technical terms, JSON is a data format, while XML is a language. These differences bring their own specific considerations such as how XML is structured while JSON parses data faster. For the purposes of data integration, the most important thing to know is that you'll likely encounter both if you're taking in data from websites or web-based applications.
• APIs: Application programming interfaces (APIs) connect different systems and retrieve data from multiple sources. APIs allow for real-time data integration and can be customized to meet specialized integration requirements.
• Cloud-based data sources: Some data sets are openly available and updated via the cloud. These types of metrics often come from governmental, educational, or research sources, where data is made available for researchers to examine further downstream.
• Internet of Things (IoT) devices: IoT devices are constantly collecting information, sometimes thousands of data points daily. Examples of IoT devices include medical devices continuously transmitting patient data, smart appliances in your home, and industrial IoT (IioT) devices that control factories and smart cities. Data from IoT devices is often uploaded to the cloud to be used by other systems.

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.

2. Prepare Data for Integration

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.

• Data cleaning: Data sets may contain incomplete or duplicate records, corrupt sections, or other issues. Data cleaning is the process of scrubbing the data set to gain a complete set of workable records.
• Standardization: While data cleaning removes problematic records, it does not address the issue of standardization. When integrating data, the process goes smoothest and results are best when standards rules—including date formatting, taxonomy, and metadata fields—are defined and applied. Before proceeding with integration, have records conform to standards as much as possible. That will minimize downstream work while increasing timeliness and accuracy.
• Transformation techniques: You can use a range of techniques and practices for data transformation. These include data smoothing, algorithmically reducing noise within a data set; data normalization, scaling data within a workable range; data generalization, establishing a hierarchy among fields; and data manipulation, identifying patterns to create workable formats.

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.

3. Choose a Data Integration Method

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:

• Manual: Manual data integration does not mean someone is physically clicking through every data field. However, it does require someone to write code to handle each step of the process. While it’s cumbersome and time-consuming, there are some situations where manual integration is the most viable option due to the quality of sources or organizational resource realities.
• Extract/transform/load (ETL): ETL processes handle transformation before loading data into a repository. ETL is most effective when systemic transformation standards are in place and able to process prior to ingesting data sets into a data lake or data warehouse.
• Extract/load/transform (ELT): ELT processes handle data transformation after loading into a repository. Which is why data sets using ELT are often in their native formats and not standardized. ELT is used when systemic transformation is not available, such as when a user finds a new source.
• Change data capture (CDC): CDC is a process that reduces resource use while keeping data sets updated. CDC ingests changes to a record and makes updates in near real time rather than refreshing the entire data set at periodic intervals. Because updates occur individually and in small, quick bursts, CDC does not impact database uptime or result in resource usage spikes.
• Data replication: Data replication keeps an original version of the data at its source and creates a copy (replica) for use by groups. This copy may be only a fraction of the record, such as select columns or some other subset used for manipulation. Replication can bog down resources if too many versions need to be kept over the long run.
• Data virtualization: With data virtualization, all data sets remain in their original databases. Transformation and manipulation occur in a virtual layer using federation to point to individual records without actually pulling them into a new file.
• Stream data integration (SDI): SDI works as a real-time version of ELT processing. Streams of data are sent from sources and transformed in real time before being sent to a repository. This has two major benefits. First, by continuously updating records, data sets are always kept up to date. Second, this removes the need for refreshing data sets at scale, stabilizing resource usage. However, SDI also creates an infrastructure challenge to functionally support the process and qualify data as it comes in.

4. Implement the Integration Plan

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.

• Extract data from data sources, such as smartphone apps, databases, web applications, and software applications.
• Transform data from data sources to meet internal standards for definitions, nomenclature, and taxonomy.
• Load transformed data into a data warehouse, data lake, or other repository accessible by tools such as business intelligence or self-service analytics.

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.

5. Ensure Data Quality

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.

• Data profiling: High-level analysis of source data to examine quality, completeness, accuracy, and other elements to create summaries.
• Data standardization: The process of creating standards for format, definitions, nomenclature, and other elements to ensure that data is fully compatible with other data sets within an organization. If data does not arrive meeting standards, it will need to be transformed to do so.
• Data cleansing: Cleaning a data set to correct and remove duplicate, empty, inaccurate, or corrupt entries so data sets are ready for processing.
• Data matching: This involves matching records across different data sets to verify they reflect the same subject while also flagging duplicate records for removal.
• Data validation: Verifying the accuracy and quality of data by checking that it works within given rules through a series of checks and parameters.
• Data governance: The process of monitoring data to ensure that storage, security, acquisition, and other tasks meet standards and principles set by the organization as well as any regulations that might apply.
• Continual monitoring: The use of various tools to continuously check the health of data sets based on internal standards and governance criteria.

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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.

Multi-Source Data Integration FAQs

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:

• Understand the state of your data sources regarding data quality
• Plan with your business goals in mind
• Know what your IT resources and budgets are
• Prioritize which departments can benefit most from data integration
• Consider long-term expansion and scalability

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.