Simplify your life with data consolidation. Find out what it is & how to do it.
Are you tired of sifting through mountains of data from different data sources before you find the data set you need to get your job done?
It's time to simplify your life with data consolidation.
In this article, you will learn:
Data consolidation is the process of combining different data sources (files like Excel, CRM contacts, SaaS apps, and others) into a single unified view of data. The data consolidation process is a critical step in data management, as it helps organizations eliminate duplicates, standardize data, and improve the data quality that underpins all business decision-making.
Keep in mind, the term data consolidation is often used interchangeably with “data integration”.
Let’s look at the different technological patterns we have to consolidate data.
Any data consolidation project will have three ingredients:
These 3 ingredients look simple. Yet you can mix them up into dozens of different architectures that turn raw data into insights.
Let’s look at the 3 most common data consolidation techniques, how they work, and when you would choose one over the other.
Data warehousing is the most common data consolidation technique.
You extract data from its raw data sources and ingest into the data warehouse via a process called ETL (extract, transform, and load). ETL data pipelines collect data from raw sources (extract), clean and aggregate the data (transform), and save the data into the data warehouse (load), where it can be accessed by business intelligence tools and data analysts.
Pros:
Challenges:
Recommended read: Check out our shortlist of best ETL tools currently available on the market that can help you streamline your data consolidation process.
Česká spořitelna - the biggest Czech retail bank with 4.5 million clients across 400 branches - used to self-manage their Oracle database alongside multiple 3rd Party integrations and data sources.
This setup caused a lot of technical overhead and intradepartmental friction. Employees created their own Excel reports without data quality validations. Causing disputes about metrics.
Until they decided to introduce Keboola. Keboola is used to automate all ETL data pipelines throughout the bank and integrate data into a data warehouse, where data is validated and can serve as a single source of truth for all reporting, keeping the various departments aligned.
The result?
The data lake design pattern is very similar to the data warehousing data consolidation technique. But instead of transforming the data before loading it into the data storage, you load the data first and transform it later. This data pipeline is called ELT (notice the Load before the Transform).
Pros:
Challenges:
Unlike data warehousing or data lakes, data virtualization doesn’t store data. Instead, you create links between your data consumers (business intelligence tools, data analytics software, …) and the data sources where the raw data resides. Data is referenced and not moved.
Data virtualization is a highly technical solution. It utilizes advanced data engineering techniques like federated compute, caching, push-down query optimizations, stream sampling, and many others.
Pros:
Challenges:
Why does your organization need data consolidation?
Let’s look at how easy it is to consolidate data with devoted data consolidation tools.
Keboola is a data platform as a service that helps you automate all your data processes. It helps you bring all business data under one roof and maximize data value through advanced business analytics.
Here’s how you can set up Keboola to consolidate data in three steps:
Make a quick list of data sources that you want to extract. Keboola offers more than 250 pre-made components that help you perform highly advanced data engineering feats with a couple of clicks.
You can use Keboola whether you're a data engineer who loves to code or a domain expert without a single CS class.
Keboola’s no-code features allow you to build a data pipeline in minutes by dragging-and-dropping components.
You simply select the data source you want to extract, click on the transform component, and save or load the data into your data warehouse.
You can schedule the pipeline to run automatically with a single click. No more manual-coding data pipelines, Keboola takes care of the heavy lifting.
Join the ranks of Mall Group, Rohlik, Česká spořitelna, and other companies who decided to automate their data consolidation process with Keboola and spend more time on creative work.
You can use Keboola whether you're a data engineer who loves to code or a domain expert without a single CS class.
When you consolidate data with Keboola, you manage all the data pipelines from a single user interface. Automate pipelines with one click and build advanced data products confidently with Keboola’s enterprise-grade data security.
These are the most common questions asked about the data consolidation process that we didn’t extensively dive into in the main article.
Centralizing all your data assets is the first and necessary step for data management and data quality. Only when all your data is centralized, can you start aligning your metrics across different data sources and departments.
Data consolidation and data integration are two terms that refer to the same process. They both refer to the process of extracting data from different sources into a single location.
Data consolidation is the name of a feature in Microsoft Excel that lets you consolidate data across different worksheets or workbooks. You can pull the data from multiple worksheets into a single target sheet by clicking the button Consolidate.