You already know that having ready access to data is critical for marketing, sales, and overall business success.
The issue: Today’s typical marketing and sales journey spans many steps and technological platforms and may include data from sources such as your website, customer relationship management (CRM) system, search engines, social media, point-of-sale systems, cloud warehouses, and more. Accessing it in a convenient and timely manner can be challenging when you want to evaluate progress toward marketing and sales success. Without a unified view, data is fragmented, inconsistent, and underutilized.
Marketers must rely on data from multiple sources to get a complete view of their marketing, sales, and customer service efforts to optimize campaigns. This guide shares Jarrah’s best practices for integrating and analyzing data from various marketing platforms for simpler and better data management.
What Is Multi-Source Data Integration?
Data integration is the process of combining data from multiple sources to produce a single, unified view. You must extract data from different places, transform it into a consistent format, and load it into a central repository, typically a data warehouse or cloud-based platform.
The primary benefit of data integration is breaking down data silos. Without integration, you’re forced to deal with fragmented insights, making it difficult to see the bigger picture of all that is going on across your organization. Having an accessible single source of data allows you to make better-informed business decisions.
Be aware: Data integration isn't merely about combining data. It is really about making data meaningful and accessible. Whether you're tracking customer behavior, sales performance, marketing results, or other things, integrated data provides the comprehensive insights you need to drive your business forward (the right way).
The multi-source data integration process typically involves:
- Identifying and connecting to source data systems
- Extracting raw form data
- Transforming and standardizing data
- Loading data into a central location
- Ensuring you maintain quality, consistency, and governance standards throughout the data integration process.
Depending on your data stack and latency needs, integration can take several forms, including:
Benefits of Data Integration
Combining all of your data from different sources can provide many benefits, including:
- Improved decision-making. Having all your data in a single place, formatted similarly, provides a comprehensive view of your operation. This allows you to make better-informed decisions, eliminating guesswork that can lead to bad, costly choices.
- Greater understanding of customers. Integrating customer data from all marketing, sales, service, and retention efforts provides a more complete and multidimensional understanding of your customers, allowing you to know what they need from your business before they do.
- Improved operational efficiency. Data integration eliminates the need to gather data manually and compile it into reports. This means less time spent on administrative work and more focus on core business activities. Also, having all your data in a single place makes it easier to identify inefficiencies and redundancies, helping you streamline your operations.
- Enhanced data accuracy. Pulling together data from different sources makes it easier to verify information and spot errors and discrepancies. This leads to more reliable information you can depend on when making business decisions.
- Enhanced collaboration. When everyone in an organization is referencing and working off the same set of data, they are more likely to work together to achieve company goals. A single data source helps break down silos that can sabotage organizational success.
- Lower costs. As mentioned, unified data can help you identify inefficiencies and opportunities to conduct business better.
- Real-time information. In today’s fast-changing business environment, it is critical to have real-time information at hand to make quick decisions. It’s the only way to keep up with the competition. Centralized, integrated data is readily available to help you make smart business decisions, anywhere, anytime.
- Business growth. The benefits of integrating your data make it likely your company will grow, something most business owners and managers strive for. (The good news is that most data integration platforms are highly scalable and able to absorb the additional data larger organizations generate.)
Common Data Integration Challenges
Here are common issues companies face when integrating data:
- Schema drift
- Different data formats and terminology
- Varying data reporting schedules
- Data duplication or inconsistency
- Incorrect or unreliable data
- Limited engineering resources to build and maintain an integrated system
- Difficulty maintaining visibility across complex pipelines.
The experts at Jarrah can help you navigate the challenges of integrating your data so you can enjoy the benefits of doing so.
How to Integrate Data from Different Sources
Integrating data from all the sources your business depends on can be challenging. However, the benefits it can bring to your organization are clearly worth it. Here are the steps you must take to integrate your data.
1. Define objectives
You must figure out what you want to achieve before launching a data integration project. You can’t achieve success if you don’t know what success is. Having goals will help you set clear objectives for the entire process, determine KPIs, and select the right methods and tools to integrate your data. Ensure your data integration goals align with your organization’s business goals.
You must figure out what you want to achieve before launching a data integration project. You can’t achieve success if you don’t know what success is. Having goals will help you set clear objectives for the entire process, determine KPIs, and select the right methods and tools to integrate your data. Ensure your data integration goals align with your organization’s business goals.
Start by defining what you want to achieve through integrating your data. Consider not just why you want to do it but also what you hope to achieve. Review the benefits we covered earlier and determine which your organization would like to enjoy. (Most companies we work with aspire to all of them.)
Get started by:
- Identifying business goals. Define business outcomes you want to achieve, such as improved decision-making, better knowledge about customers, or increased operational efficiency.
- Determine metrics. It’s not enough to broadly define business goals. It’s also critical to back them with key performance indicators (KPIs) to measure the success of your integration efforts.
2. Identify data sources
Figure out the types of data you need to integrate based on your goals. Some examples include metrics from social media advertising, emails, a customer relationship management (CRM) system, and Google Ads. Typically, data exists in silos, so you need to know which sources are critical to your mission and where they are housed.
Check the quality and reliability of the data coming from different sources. Introducing bad or unreliable data into your mix could have a very negative impact on your business, as you could make poor decisions based on it.
In short, only include high-quality, reliable data from trusted sources in your mix.
You can’t integrate your data until you identify all the valuable sources. Do this by:
- Conducting an inventory. List all the systems, applications, and databases that hold the data you use to make business decisions.
- Evaluating data quality. Check the currency and accuracy of the data from each source. Identify issues such as missing values, duplicates, or inconsistent formats that may need to be addressed during the integration process.
- Determining data formats. Document the format of data from each source, whether it's structured (e.g., SQL databases) or unstructured (e.g., emails, social media posts).
3. Choose the correct integration tools
Doing your due diligence before selecting a tool to integrate your data is critical. It’s the only way to ensure your data integration process benefits your firm rather than complicates things. It will make it easier for you to implement your data integration, access your data, and manipulate it to support your business decision-making.
Choose an integration tool that is easy to understand, compatible with your existing IT infrastructure, and scalable to meet the growing data demands of your firm. Other essential features to consider include;
- Security. The tool you choose will process a significant amount of personal and business data. You must make certain it is safe and secure.
- Features and functionality. It’s critical to ensure your data integration system performs as you intend and does what you want it to.
- Performance. Check that the tool performs effectively, providing you with the information and reporting you need when you need them.
- Automation. Make sure the tool automates all aspects of the data integration process. Anything you have to do manually just adds time to the effort and introduces the possibility of errors.
- Processing capabilities. Ensure your tool can handle your data volume and processing needs. Also, check that it can grow with your business.
- ETL versus ELT. Begin by deciding whether you need an ETL (Extract, Transform, Load) tool, which transforms data into usable formats before loading it to the target destination, or an ELT (Extract, Load, Transform) tool, which loads data first and then transforms it. (More on that later in this article.)
- Tool features and functionality. Look for things like pre-built connectors, real-time processing capabilities, and support for a wide array of data formats.
- Flexibility. If you have unique or complex data integration needs, ensure any tool you select is flexible enough to meet them.
4. Extract the data
Some things to consider as you begin the data extraction process include:
- Automate data extraction. Use your integration tool to automate the data extraction process. It will help save time and reduce the risk of errors.
- Schedule extractions. Develop an extraction schedule to keep your integrated data up to date.
- Track data flows. Regularly monitor your data extraction process to identify and address issues quickly.
5. Clean and transform data
Transforming and cleaning your data makes it usable and dependable. Here’s how to go about it:
- Data cleansing. Remove duplicate data, add in missing values, and standardize formats.
- Transform data. Apply business rules and logic to transform the data into a consistent format that suits your business analysis needs, including doing things like converting currencies, standardizing dates, or normalizing data structures.
- Leverage transformation tools. Use the features available through your integration tool to automate and speed the cleaning and transformation process.
6. Load data into a central repository
Next, load your data into a central repository for analysis.
- Choose a data repository. Select an appropriate repository, such as a cloud data warehouse or a data lake. (More on that later.)
- Load data. Use your integration tool to upload the transformed data into your repository.
- Validate data. Always make it a point to check data to ensure it is accurate and complete. Make certain the data in the repository meets your expectations.
7. Establish data governance
Develop data governance processes and procedures to maintain data quality and security. Here are key things to consider.
- Data ownership. Assign roles to people in your organization to guarantee accountability for data quality and management. Make accountability a part of your annual review process.
- Implement controls. Put in place access controls to protect sensitive data and ensure compliance with applicable regulations.
- Data-related policies. Create and enforce standards to maintain consistency, reliability, and quality.
8. Monitor data
Data integration isn’t once-and-done. It is an ongoing activity that requires continuous monitoring and maintenance. Here are some things you should do.
- Conduct a performance review. Regularly check the performance of your data integration processes to identify and fix issues.
- Check data quality. Conduct regular checks of your data, ensuring integrity and accuracy.
- Enhance integrations. As your business grows and changes, you must update your integration processes to accommodate new data sources, higher data volume, and new requirements.
Now that you understand the process of data integration, let's look at some ways to do it.
Data Integration Methods and Techniques
Integrating data from different sources requires a range of methods and techniques to consolidate the data effectively. Here are some common ones.
- ETL (Extract, Transform, Load). This is a basic method for extracting data from many sources, transforming it into a usable format, and then loading it into a central repository.
- Extract. Gather data from different sources, including databases, applications, or external systems.
- Transform. Apply business rules to clean, format, and enrich data, including removing duplicates, standardizing formats, and converting data into different types.
- Load. Move the transformed data into a data warehouse or data lake where it can be accessed for analysis.
- ELT (Extract, Load, Transform) is a more modern approach to data consolidation that leverages the power of cloud-based data warehouses to handle transformation.
- Extract. Similar to ETL, data is accessed from different sources.
- Load. In this method, raw data is loaded directly into a cloud data warehouse.
- Transform. In ELT, the cloud's computational capabilities are used to transform data in the data warehouse.
ELT is often preferred over ETL because it is typically faster and more scalable.
- Data warehousing. This involves collecting and managing data from different sources in a single place. Here’s how it works:
- Data from various sources is collected in a data warehouse
- Before it is stored, data is cleaned and formatted
- Data warehouses typically store historical data, making it easier to analyze and report on it.
- Data visualization. This tactic allows users to access and query data in real time without moving it from its original location. Some benefits of data visualization include:
- It provides a single interface for accessing data from multiple sources.
- Data remains in its original source location, but it can be accessed and integrated on demand.
- It is fast and flexible, which is beneficial for real-time analytics and reporting.
- Data federation. This form of data integration combines data from different sources without consolidating it into a single place. Features include:
- Data stays in its source systems but is virtually combined and presented so you can see it in a single place.
- You can retrieve data from multiple sources all at the same time.
- Data integration is relatively easy to implement because it doesn't require moving data from its original source or storing it in a different location.
- API-based integration. Application Programming Interfaces (API) facilitate data exchange among different systems and applications. Benefits of this form of data integration include:
- APIs allow systems to communicate directly, sending and receiving real-time data.
- They are highly flexible and can integrate with most applications and platforms.
- They provide real-time data synchronization and updates.
- Middleware integration. Middleware is an intermediary that facilitates data exchange among different systems. Some things middleware can do include:
- Format and validate data before transmitting it to an end system.
- Delivering a seamless connection among different systems.
- Handling complex integration scenarios with differently formatted data.
- Batch processing. Batch processing involves collecting and processing data in batches at prescheduled intervals. Some features of batch processing include:
- Data is processed in batches during off-peak hours to reduce system load.
- It is a solid way to process large volumes of data that doesn’t require real-time analysis.
- It can be used in financial reporting, payroll processing, and other periodic data tasks.
Ensure Data Accuracy and Consistency
Consistency and accuracy are key when integrating data from different sources. Information collected from a range of platforms, such as social media, point-of-sale systems, and Google, may be structured differently, called different things, or updated at different times. To eliminate this issue, you must use consistent formats and definitions for data from all sources entering your unified system.
This typically requires a mapping exercise to identify commonalities and differences in data from a range of sources and to assign definitions to them. You also need to address the timing of data updates to ensure you are using datasets that reflect common time periods. For instance, combining older social media data with of-the-moment point-of-sale metrics in the same model could lead to incorrect associations between marketing and sales activity, leading to poor decisions about continuing marketing and sales efforts.
It is also critical to ensure that data is correct or to take steps to clean it up before it enters your system.
When data is consistent, analysis based on it will be credible, and the occurrence of misleading conclusions will be reduced significantly.
Data Integration: The Final Word
Integrating data from multiple sources can have a big positive impact on businesses. Having a clear, real-time view of what’s happening across your organization can help you make better business decisions and eliminate waste.
Still, implementing a data integration program can be challenging and takes a long-term commitment. Leverage the tips in this guide to get started and to keep your data integration program running smoothly.
Also, feel free to contact the experts at Jarrah for answers to your data integration questions and to get support launching an initiative.
