What is Data Consolidation?

by Garrett Alley  
5 min read  • 3 Jan 2019

To the outside world, your business is a highly organized structure. But on the inside, it’s a cauldron of raw material collected from databases, documents, and a multitude of other sources. This material — a.k.a. data — has all the potential in the world to help your business transform and grow, so long as you properly corral it all through a process called data consolidation.

Data consolidation defined

Data is generated from many disparate sources and in many different formats. Data consolidation is the process that combines all of that data wherever it may live, removes any redundancies, and cleans up any errors before it gets stored in one location, like a data warehouse or data lake.

Data consolidation and database replication. There's another kind of data consolidation, one that has to do with how changes to rows in the data (updated, inserted, or deleted records) in a database are merged with a data warehouse. This "consolidation" — beyond the scope of this post — is often performed on a regular schedule and is used to incorporate changes to data being replicated in order to ensure the "latest" version of data is reflected in the data warehouse.

At a time when "information creation" is accelerating at exponential rates, data consolidation offers important benefits to organizations that are struggling to tackle today’s business challenges. The process helps to ensure greater data quality and accuracy, making it much easier to access, manipulate, and analyze when you’re ready. By eliminating the incongruencies that must first be addressed before operationalizing the data in any way, you can achieve enormous time savings, improve efficiency, and add value to your organization's data operations as a whole.

How to consolidate data

Data consolidation isn’t standard across industries or organizations and there are a few different tools or methods you can use to do it:

  • Hand-coding or scripting. This manual process custom builds scripting by data scientists to combine and consolidate data from a predetermined range of sources.
  • Open-source tools. Open-source software helps organizations combine and consolidate data with relatively little cost and more flexibility, but requires a higher degree of expertise in coding and usually more manpower.
  • Cloud-based tools. A modern approach to data consolidation, cloud-based tools automate many data consolidation tasks with speed, scalability, and security.

Challenges with data consolidation

Even though data consolidation is a critical stepping-stone on the path to greater business intelligence and faster, more precise decisions, it isn’t always realistic for organizations to do it themselves using existing teams and systems. On the upside, this more traditional approach may give the impression that your organization has full control of its data. On the downside, it can introduce a slew of other challenges that cancel out any control you may believe you have.

Here are four common roadblocks that can occur with traditional, on-site data consolidation:

Limited time. IT teams already have their hands full configuring, maintaining, and monitoring on-site hardware and other equipment, in addition to keeping up with the rest of their daily tasks. So spending the necessary hours to script, run, and manage error-free data consolidation may not always be feasible for your current team.

Limited resources. Any data integration process usually requires the help of skilled data scientists. Yet many organizations don’t have the budget or internal buy-in to staff up with the right resources to get the job done. The truth is, acquiring specialized knowledge is time-consuming, and hiring it is a hefty investment.

Scattered locations. Many businesses operate with remote or branch locations, which means that data isn’t available in a single physical place but has to be secured and managed in multiple locations. When you need to retrieve that outlying data and combine it with local data sources, it can take significantly more time (and a lot more bandwidth). Yet time is not a friend when quick decisions are on the line, since data can fast become outdated.

Security issues. Every place where data is stored opens up the potential for a hack or breach. And moving data to another place during the data consolidation process only increases that potential. As well, most businesses have to adhere to some level of regulatory standards. But patched equipment and having just one systems admin in charge of data management for the entire enterprise makes it much more difficult to maintain security and compliance to the degree necessary.

Modern cloud-based data consolidation

Where businesses can gain an advantage, however, is by using third-party, cloud-based data consolidation tools. Like other cloud solutions, these tools are built for speed, security, scalability, and flexibility — no matter where or in what form your data exists. In this way, you’re assured a complete and accurate data load you can then access whenever you want in your data repository of choice.

And because it’s all happening in the cloud, you don’t have to purchase or maintain expensive equipment to handle the data consolidation aspect, or add resources to your in-house IT team to oversee a manual process.

Alooma’s modern data consolidation solution ticks all the boxes your organization would need to kickstart a full data integration strategy. With security and compliance a top priority, Alooma’s cloud-based solution moves and combines all your data following the highest industry standards, thereby reducing risk.

Fast and efficient data pipelines cleanse your data and make it available to you in real time rather than in batches, if you choose. And because the data consolidation process is automated, your data operations run much smoother and more efficiently all around, saving upfront infrastructure costs and improving the speed and soundness of business decisions.

Ready to get started? Contact Alooma today to learn more about how modern data consolidation can create a faster, more accurate, and more efficient environment for data analysis.

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