Data consolidation is the corralling, combining, and storing of varied data in a single place. It lets users manipulate different types of data from one point of access and helps turn raw data into insights that drive better, faster decision-making. The term sometimes is used interchangeably with data integration.

Data consolidation enables businesses to streamline their data resources, discover patterns, and look for insights in multiple types of data. data consolidation

Data consolidation and ETL

Data consolidation usually involves four layers of technology: data sources, an ETL (extract, transform and load) data pipeline, a data warehouse destination, and business intelligence (BI) tools.

ETL stands for "extract, transform, and load" — the process a data pipeline uses to replicate data from a source to a traditional data warehouse. In a variant of this process, ELT, the transformation step happens after the data is loaded on the target system, because that's a better way to replicate data from a source system into a cloud data warehouse.

There are two ways to ETL:

  1. Hand coding is a manual process in which an engineer builds a script to consolidate data from predetermined sources. Although hand-coding is time-consuming and requires a data engineer, it can be useful for smaller jobs with just a couple of sources. It also may be necessary when a source or destination is not supported by other tools.
  2. ETL tools, both local and cloud-based, exist to expedite the data consolidation process. These tools automate the ETL process and can begin replicating data within minutes of implementation. Cloud-based ETL tools are tested, maintained, and updated constantly by the provider.

Replicate your data with Stitch today

Data consolidation best practices

Organizations should plan and execute data consolidation projects carefully. These best practices promote effective data consolidation:

  • Check to see whether data types in your source and target are compatible: If they're not, you'll have to transform data to address differences among data types.
  • Maintain copies of your data: Data lineage allows an organization to understand exactly what was done to the data — and how — during the consolidation process. You may need information to demonstrate regulatory compliance, or for retracing steps to understand the results of analytics and any business decisions based on them.
  • Standardize character set conversions: If you work with an application that allows you to store single-byte characters — such as Western languages — and double-byte characters — such as some Asian languages — in a database, the application can convert between these character types. However, when you move the data, the tools processing the data may be unaware that the data is stored in a different format. By standardizing character set conversions, you increase the likelihood of consolidating data for a reliable outcome.

Integrate data from more than 100 sources

Data consolidation challenges

There are challenges in the data consolidation process. The most common ones include:

  • Limited resources: Hand-coding consolidation techniques require data engineers who must write code and manage the process, and write more code every time a new data source comes online. The more sources and data types involved, the longer the process becomes.
  • Security issues (real and perceived): Security concerns include guarding data from breaches before and after consolidation, and developing backup and disaster recovery capabilities if data is compromised, corrupted, or deleted. Companies also must guard against "inside jobs" like exfiltration — the unauthorized copying, transfer, or retrieval of data from a computer or server.
  • Data spread across multiple locations: Today's decentralized data landscape can make data integration challenging. Having data in different locations — including in the cloud, on premises, and at remote locations — adds to the complexity of data consolidation. For instance, data stored in legacy systems may be missing times and dates for activities, which more modern systems commonly include; and data from external systems may not contain the same level of detail as internal sources.

Getting started with data consolidation

Are you planning a data consolidation project? Stitch is a cloud ETL service that provides connectors from more than 100 sources to the most popular data warehouse destinations. Our approach is simple, straightforward, and ready to go right out of the box. Give Stitch a try, on us — set up a free trial in minutes and consolidate your data today.

Give Stitch a try, on us

Stitch streams all of your data directly to your analytics warehouse.

Set up in minutesUnlimited data volume during trial