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What is Data Consolidation?

Data consolidation 1200

Data consolidation is a relatively new discipline that has emerged because our data is becoming more spread out and difficult to manage. To learn more about how data consolidation can help your organization benefit from all your data, rather than being overwhelmed by it, read on.


Data consolidation definition


Every business has data. Human resources systems, product databases, customer relationship management (CRM) software, and hundreds of other business systems all contain data about your business and your customers. This data is valuable on its own, even when it’s locked in its own application silo, but it’s even more valuable when it’s combined with the data from the rest of your business.

Data consolidation is the process of taking all of your data from disparate sources throughout your organization, cleaning it up, and combining it in a single location, such as a cloud data warehouse or lakehouse environment. When your data is all in the same place, it’s a lot easier to get a 360-degree view of your business. It’s also much easier to transform that data to make it useful for reporting or analytics.


Data consolidation is similar to data integration, and in fact, the two terms are sometimes used interchangeably. Both are considered an essential part of any organization’s data management processes.


Why is data consolidation important?


Data consolidation is important for a number of reasons:


Analytics  Running analytics on an incomplete set of data will not yield the most accurate results. However, when you’ve consolidated all of your organization’s data into a single location, you can transform that data and combine it in different ways to gain a 360-degree view of customers and processes that can lead to valuable business insights and reliable business intelligence.


Planning  When all of your data is in a single location, it’s easier to plan your business processes and disaster recovery scenarios. It’s also easier to determine data capacity needs.


Data quality  One of the benefits of data consolidation is that because the data is transformed before it is consolidated, it is in a consistent format on the central data source. This transformation step can give data workers the chance to improve data quality and integrity as part of regular data operations.


Data consolidation techniques


Hand-coding. Using a manual process like hand-coding is certainly an option. And it might be feasible if you have a small, uncomplicated data collection. However, hand-coding is generally considered too time-consuming to support businesses with exploding volumes of data (that is, most of them).


ETL software. ETL software is often used to support data consolidation. ETL applications can pull data from multiple locations and multiple sources, transform it into the necessary format and then transfer it to the final data storage location.


ELT tools. As businesses move their data to the cloud, more of them are adopting a push-down, ELT approach to transforming and consolidating data. As opposed to ETL, with ELT, you first extract data from sources and load it into your cloud data warehouse or data lake. Then you use the power of the cloud to transform that data in a way that’s much faster, scalable, and more cost-effective.


6 data consolidation challenges


Limited time. Data consolidation projects aren’t quick. Or easy. But the rewards can be well worth the time invested. However, you’ll need to make sure you have enough time and resources allotted to the project.


Limited resources. Not everyone has dedicated team members to manage a complicated data consolidation project. It’s important to assess the skill sets and the size of existing staff and decide if you’ll need to bring in outside help, either consultants or a new hire.


Incompatible data types. Data from different sources will have different formats. As all of this data is consolidated in a single location, it must also be transformed so that can be used together in analytics. This transformation step can add complexity to your data consolidation project.


Complex data landscapes. The more data sources you have, the more difficult it can be to consolidate all of your data. This is particularly true when the data is distributed among numerous physical locations as well as different IT systems.


Latency. Data is most useful when it’s as up to date as possible. With data consolidation, there may be some latency involved because it can take time to retrieve the data from the source and transfer it to the central target. This latency period can be shortened by more frequent data transfers.


Security issues. It’s important to take measures to secure your company’s data. It’s even more important if you’re dealing with personally identifiable information (PII).


6 data consolidation best practices


Plan properly. As with any IT project, planning may be the most important phase of the project. Make sure to budget enough time and resources.


Get the right skill sets in place. Make sure you have the right expertise before you start your project. If you find you don’t have the right skills in-house, consider hiring a consultant.


Use an ETL product. ETL tools are designed to make it easy for you to extract, transform and load data. Instead of reinventing the wheel and coding it yourself, take advantage of a commercial ETL solution.


Use data consolidation tools that connect with numerous applications. If your data is coming from a broad range of sources, it’s important to use an ETL tool that will easily connect with all of them.


Establish consistent ongoing processes. Data consolidation isn’t a one and done proposition. It’s important to establish ongoing data consolidation processes and get your data pipelines flowing on a regular basis.


Consult with data security experts. To address any potential security issues, work with data security experts to ensure that you don’t introduce any security issues as you’re processing the data and that your data is stored securely.


Want to learn more about data consolidation?


Given the increasing volume and complexity of data, and the speed and scale needed to handle it, the only place you can compete effectively—and cost-effectively—is in the cloud. Only Matillion offers cloud-native, enterprise-ready ELT products for Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, and Delta Lake on Databricks. Trusted by companies of all sizes to meet their data integration and transformation needs, Matillion products are highly rated across the AWS, GCP, and Microsoft Azure Marketplaces.


Matillion Data Loader is a SaaS-based data integration tool that seamlessly extracts data and then loads it into your cloud data warehouse. With a code-free, wizard-based pipeline builder to common data sources like Salesforce, Google Analytics, and more, Matillion Data Loader can help make your first data migration (and every one after) quick and easy. Sign up today to try Matillion Data Loader for free and kickstart your data migration project.


Matillion ETL software is cloud-native, purpose-built to support leading cloud data warehouse and lakehouse environments, including Snowflake, Amazon Redshift, Google BigQuery, Microsoft Azure Synapse, and Delta Lake on Databricks. From extracting and loading your data to performing powerful data transformations, Matillion cloud ETL solutions offer cloud-native architecture and performance compared to legacy ETL tools. With no hardware or software requirements, Matillion leverages the speed and scale of the cloud, making complex data transformation fast, secure, and cost efficient.


See the power of Matillion for yourself. Request a demo to learn more about how you can unlock the potential of your data with Matillion’s cloud-based approach to data transformation.