Data Consolidation - What It Is & How To Do It

What is data consolidation?

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.

Data consolidation techniques: the 3 design patterns

Any data consolidation project will have three ingredients:

1. Source data, where raw data resides.
2. The data consolidation mechanism that unifies data.
3. A consumer will take the consolidated data and turn it into insights or products.

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 consolidation technique #1: Data warehousing

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:

• Mature design pattern with a lot of tried-and-tested solutions that can help you deploy your data consolidation project fast.
• Data warehousing and ETL processes scale well with growing data and can keep up with your business growth. 

Challenges:

• If you decide to build your data warehousing and ETL solutions by hand-coding everything yourself, you can quickly discover many problems. Manual processes are time-consuming and prone to errors. And your data engineers will not be very grateful for the extra hours spent debugging data pipelines. A better approach is to use ETL tools that can streamline and automate your data consolidation processes.

Recommended read: Check out our shortlist of best ETL tools currently available on the market that can help you streamline your data consolidation process.

Data warehousing in practice: An industry case study

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?

• No more custom Excel reports of varying quality. 
• Faster time to insights. With automated data pipelines, the data and IT teams had more time to work on generating insights.
• Self-serve analytics. Keboola’s ease of use allowed non-technical personnel to build their own data products and innovate by creating new lead generation pipelines, data-driven and automated personalized marketing communications at scale, and automated credit risk scoring.

Read the full story for more details.

Data consolidation technique #2: Data lake 

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:

• Data lakes can handle more types of data. Data lakes are designed specifically for data storage, so they can handle structured data (think tables) and non-structured types of data like videos, images, long text files, etc.
• Data lakes can handle much larger volumes of data than data warehouses, so they are often used for big data and state-of-the-art data science initiatives. 

Challenges:

• Without data validations and data quality controls, data lakes can turn into data swamps. Pipelines that just dump vast quantities of unstructured data into the data lake until it becomes so crowded that it is unusable. 

Data consolidation technique #3: Data virtualization

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:

• Lower data storage and networking costs. Because you do not integrate the data into your own data storage, you spend less on storing and moving data around.
• It allows near real-time data access since it cuts the time of moving data and usually accesses a sample or aggregated metrics only.

Challenges:

• Hard to implement and maintain as a manual process. Hand-coding requires a lot of technical know-how.
• Data sources are not owned, so you can lose access to both your future and past data.
• Data virtualization tools are less common than ETL tools or other data consolidation tools. Harder to find a vendor or provider that suits your needs.
• Without an overall data management and data governance policy (that is in place for data warehousing and data lakes), your data operations quickly fragment into silos. As a result, each user builds their own data products without any synchronization or alignment with other business units. Hence, you face the same problems with which you started.

The importance and benefits of consolidating data 

Why does your organization need data consolidation? 

1. First and necessary step to data management. Without consolidating data, each department develops reports and consumes data in silos, causing misalignment and communication issues.

2. Data consolidation helps you build data products. Without bringing all the external data into a single location, you do not have all the ingredients to create data products like personalized marketing or the customer 360 view, which need data from multiple sources to truly work.

3. Increase data security. By managing your data from a single point, you lower the exposure to potentially harmful practices that could damage your business when data is in silos.

4. Automate data processes (ETL and ELT data pipelines) that bring the data together. Bringing data into a single location lowers costs and streamlines business processes surrounding data.

Let’s look at how easy it is to consolidate data with devoted data consolidation tools.

The 3 easy steps to consolidate data with Keboola

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:

Step 1: Identify your data sources

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.  

Step 2: Build an ETL/ELT data pipeline 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.

Step 3: Automate

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.

Choose Keboola for fast and easy data consolidation

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.

Schedule a free consultation 

Frequently asked questions about data consolidation

These are the most common questions asked about the data consolidation process that we didn’t extensively dive into in the main article.

Why consolidate data?

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 vs data integration: What’s the difference?

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.

What is data consolidation in Microsoft Excel?

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. 

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