by Carol Stigum | December 13, 2024

Data Consolidation: How to Merge Multiple Data Sources Seamlessly

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Data comes from every direction—websites, apps, social media, smart devices, and more. And the data keeps growing. Unless you find a way to bring it all together, you are missing the big picture. This is where data consolidation comes in. Data consolidation has become essential for modern businesses in that it breaks down data silos and creates a single source of truth in which to uncover insights into your business.

This article explains how effective data consolidation provides the clear data insights you need to support your data flow, improve productivity, and grow your business.

What is data consolidation?

Data consolidation is the process of combining data from multiple sources into a unified system or repository, such as a data lake or data warehouse. This data becomes a consistent, single source of truth for your organization. There are many ways to perform data consolidation, depending on the amount of data and where the data resides.

10 crucial benefits of data consolidation

Improved data quality: Data is validated and corrected during the consolidation process, resulting in improved data quality. Consolidation also makes it easier to enforce data governance, resulting in data that complies with the necessary industry standards.

Enhanced data accessibility: Data contained in disparate sources is not always easily accessible. You may have to obtain technical support or have special permissions to access the data. Consolidated data adheres to consistent access policies for authorized users.

Reduced data redundancy: During the data consolidation process, data is checked not only for accuracy but also for redundancies. Duplicate records are identified and removed, improving storage and performance.

Increased data consistency and accuracy: Data consolidation standardizes data formats, allowing for uniformity and better data consistency. Since the data is in one system, you can ensure that the data is the latest available.

Improved decision-making: A huge benefit of data consolidation is improved decision-making throughout your organization. You can view data across departments and functions—making decisions based on all your available data rather than piecing together disparate data.

Simplified data management: By consolidating data into a single repository, you need to manage only one source. You can have tighter control over data access and compliance with a single data source.

Reduced costs: Consolidating data optimizes hardware and software resources, resulting in a decreased need for IT staff to support these resources.

Enhanced data analysis and reporting: Data consolidation eliminates data silos and provides a single point for data analysis. The more data you have immediately available, the better your analysis will be. You can then create comprehensive reports for your consolidated data rather than multiple reports that don’t represent your entire business.

Increased time savings: Analysts save time by having the ability to easily access information from a single resource, not to mention the IT staff, who save time on systems management.

Improved data integration: Data consolidation simplifies how different systems and datasets work together, allowing for better scalability, accessibility, and data governance.

Techniques for data consolidation

As mentioned earlier, there is no one correct way to perform data consolidation. The best technique for your business depends on the kind and amount of data you have and the way you plan to use the data.

Extract, transform, load (ETL): ETL is a well-known method of data consolidation you may already be familiar with. Simply put, you extract the data from various sources, transform the data into the desired format, and then load the data into your target system.

Extract, load, transform (ELT): ELT is a more updated approach to the ETL method because it loads the data into the target system first and then transforms the data. ELT aligns well with increasing volumes of semi-structured and unstructured data because you can store the data first and then transform as needed.

Data warehousing: A data warehouse stores data after it is collected from various sources and then processed and organized into a consistent structure. Data is loaded into the data warehouse during the ETL process.

Data lakes: A data lake is a centralized repository that stores large volumes of raw, unprocessed data from various sources in its original format. Data lakes offer low-cost storage. You only process the data on an as-needed basis.

Data marts: A data mart is a focused, specific subset of a larger data repository, such as a data warehouse. Data marts usually focus on a specific business function, such as marketing or finance. If you know you need data for a specific function or department, data marts make searching for data more efficient in a consolidated data warehouse.

Manual data consolidation coding: It may not be possible to automate all of your data consolidation. Your organization may lack data consolidation tools, or you may have needs that require a custom solution. Manual data consolidation coding is the process of writing custom scripts to integrate, verify, and consolidate data from multiple sources. Manual coding is time-consuming and prone to errors. It may also be difficult to adapt the manual coding to your future needs.

Data virtualization: Data virtualization is another strategy for managing data from multiple sources, but data virtualization approaches the problem differently. Data consolidation often involves data replication and data migration, while data virtualization refers to a data virtualization layer that manages data without physically moving it. Refer to this article to determine whether you should use data virtualization for consolidating data.

7 steps of an effective data consolidation process

Effective data consolidation takes planning and a systematic approach. Follow these key steps for the best results.

Data source identification: You must first identify the data sources to consolidate, including databases, APIs, and non-structured data. You need to understand the data structure of each source for effective data mapping.

Data mapping: Your consolidation software needs to set up mapping relationships and transformation rules for the metadata so that there is one consistent schema for the target system (such as a data warehouse). For example, your date formats and units of measurement should be consistent.

Data extraction and transformation: In this step, you use ETL or ELT pipelines to extract data from the desired sources. In ETL, you would then transform the data by standardizing it and removing duplicates. You may also use tools to enrich the data. Note that data consolidation is just one of the goals of a data pipeline. Data pipelines can also accomplish other goals that drive data-driven decision-making.

Data loading: The data is now ready to be loaded into the target system, such as a data warehouse or data lake. Depending on your pipeline (ETL or ELT), the data is either already transformed or will be transformed when needed. You need to take steps to ensure that the data migration goes smoothly, with minimal downtime.

Data merging and integration: The next step is the actual merging of the data. Doing the work of mapping the data and setting up transformation rules makes this step easier. You may still need to join tables or eliminate duplicate records.

Data verification and validation: Your tools then verify that the consolidated data meets quality standards and business requirements. At this stage, you would perform testing to ensure efficient queries and operations.

Unified data storage: The final step of data consolidation is to store the data in a centralized repository. Choose a storage solution based on your business and performance needs, including the volume and type of data, number of users, data retention needs, query needs, and your budget.

5 data consolidation tools and technologies

Consider one of the following 5 popular data consolidation tools to meet your business’s needs.

CData Sync lets you build and deploy ETL or ELT data pipelines in minutes. It can connect to any cloud or on-premises source, so you can migrate your on-premises databases to the cloud or consolidate customer data from your CRM systems into one data source.

Airbyte allows you to extract data from its over 400 built-in connector sources and create a data pipeline to a data target, such as a data warehouse. It also offers an AI assistant to help you build connectors.

Fivetran is a data consolidation tool that offers automated ELT pipelines. You can deploy Fivetran to the cloud, on-premises, or a hybrid of both.

Rivery is a unified solution that performs all data consolidation functions, such as ingestion, transformation, and data activation. It contains over 200 built-in connectors and the ability to create custom connectors.

Stitch is a cloud-based ETL /ELT service that provides data consolidation to most popular data warehouses and data lakes. It has a user-friendly interface that non-technical users can set up.

Consolidate your data with CData Sync

CData Sync is a powerful ETL/ELT tool that simplifies data consolidation by allowing the seamless extraction, transformation, and loading of both on-premises and cloud-based data into a centralized storage system such as a database, data warehouse, or data lake. With over 250 data sources, you can consolidate data from a wide range of data sources with no coding required. Try CData Sync today.

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