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What is data warehousing software?

Data warehousing software runs the databases that make up a company’s data warehouse. A Data warehouse software (DWH) will add data to the existing database and run queries that pull data sets for executive analysis.

A data warehouse works separately from the database that runs a company’s day to day work and is meant to hold historical data from lots of different sources that doesn’t get written over, whereas the transactional database that runs the daily work for a company may get written over with new information as it is gained. A data warehouse includes different types of data ported in from other types of software like a CRM tool, accounting software, and ERP software.

Because of the depth of storage in a data warehouse, the software that runs it must be highly sophisticated, able to handle large amounts of data, and able to distinguish and analyze data from widely different sources. As the single source of historical truth for the combined data of many different software tools from across the company, the data warehouse makes up the central data store for running a business intelligence software.

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Do I need data warehousing software?

Understanding when to move from several different databases to a DWH can be tricky. So how do you know when you need a data warehousing software? The simple answer is: if you currently have or are planning to implement a data warehouse or business intelligence software, you’ll need a data warehousing software to run that. Deciding whether or not you need a data warehouse in the first place can require more thought, however. Most companies will start looking at data warehouse solutions when they begin to think about implementing a BI tool.

Many companies begin to research data warehouse software when they realize that they have a problem maintaining their historical data in a way that then allows them to analyze it later. Once a company realizes the potential of the massive amounts of data that their tools collect, use, and lose every day, a data warehouse becomes one way to store data where it won’t change from day to day.

Data warehouses not only give companies a place to store data from different types of tools, it allows the data team to make the data searchable, standardized, and ready for analysis in the BI tool. Without a data warehouse as the singular storage location, individuals would have to pull reports, standardize each spreadsheet to ensure the correct mapping, and upload that spreadsheet into the BI tool. A data warehouse removes much of the manual sanitization and organization of data, freeing data analysts and stakeholders to spend time understanding the data rather than preparing it for use.

Benefits of data warehouse software

Non-volatile historical data

Storing all of your data within your separate types of software can feel like a great idea, until you try to change software. Changing software can involve a lengthy process of exporting historical data, sanitizing it and reformatting it, and then uploading data into a new system before any work can begin.

A data warehouse becomes the holding place for all of your historical data, and therefore is regularly updated with new data that software creates. While companies should pay due diligence to data for any software switch, a data warehouse gives companies the freedom to take their data with them, providing the basis for continued and comparative analysis.

Even better, the data in your warehouse rests in the same state that it was downloaded in. No matter what data gets added to the store, the historical data will be available for manipulation and analysis. This is the opposite of transactional systems where the process of gathering and manipulating new data writes over existing information, altering or losing it all together.


As any data analyst will tell you, preparing data for analysis isn’t as simple as just downloading a CSV and uploading it into a BI program. Preparing data for analysis involves data manipulation and sanitization, which is a nice way of saying cleaning up spreadsheets by looking for empty cells, mapping columns and rows, and checking and rechecking numbers and inputs to make sure the right information is classified in the right formats.

A data warehouse lets data analysts front load or automate all of this work. When data is transferred from software to the warehouse database, it is transformed into a consistent format ready for the analysis tools or BI software. So, instead of downloading and formatting data for every analysis query, the data analyst or executive can run their query directly on the data store. Frontloading data sanitization also lets teams query data from across several different systems that might otherwise not show comparable data.

Text and unstructured data analysis

An exciting outcome of data warehousing software is that it unleashes the potential power of text, images, and other unstructured data for analysis. Customer feedback, social media posts, and correspondence once required analysts to sift through information manually to process text for customer sentiment or buying trends because the information didn’t fit neatly into the rows and columns of a CSV.

Data warehousing software has becoming increasingly good at classifying unstructured data based on pattern recognition and machine learning and in some cases parsing the unstructured data itself. These innovations open new ways to understand business data that was previously inaccessible to analysts.

Tandem operation

A big draw of data warehousing tools is that they can run at the same time and separately from other business software. Executives don’t have to risk disrupting a business-critical IT process with their queries, giving all-hours access to analysis. Because the data warehouse is a separate database from the operational systems, the transactional processes that keep the business running aren’t delayed or disrupted with queries, saving time and speeding processes across departments.

What to expect from data warehouse software

Implementation time

While IT projects vary depending on industry and scope, you can expect a data warehousing implementation to take anywhere from 9-29 weeks before the DWH is operational from project planning to rollout. Of course projects can take longer than this, but teams should attempt to make time to implementation as short as possible (while ensuring data quality) to keep the data current and avoid getting caught in project creep that prevents any implementation.

Data mining

The structure of a data warehouse provides companies with subject-oriented data available for drill downs, roll-ups, and deep dives that would otherwise be impossible with data in an online transaction processing (OLTP) system that moves data from working memory into the archive. DWH are built for unexpected queries, and therefore classify the information differently, transforming it before storage to a highly manipulative form that anticipates unexpected queries.


Data warehouses are built with scale in mind. Companies who implement data warehousing software expect to store and manipulate large bodies of data, and continue to add to that store frequently. When choosing a data warehousing software, check that the manufacturer specifically addresses issues of scale.

Homogenization and Unforeseen Errors

While data warehouses provide centralized, standardized, and non-volatile storage for information from seemingly incompatible sources, the standardization of data for use in the DWH can cause issues with the data:

  • Loss of data that doesn’t fit into the standardized format
  • Errors that accumulate over time (null fields, incorrect translation of fields)
  • Inability to account for unplanned queries on subject matter considered extraneous during initial implementation

Standardizing data requires that some of the deviation will be ironed out of data before it is stored in the data warehouse, which in turn may trim fields not currently considered useful, and preventing the full picture of the information.


Cloud or On-Premise

As with any modern software, the company must decide between the relative support and stability of a cloud model, or the security of an on-premise solution. Here are pros and cons to consider before choosing either:

Pros of cloud DWH software

  • managed server uptime
  • monthly or annual subscription instead of one-time fee
  • support
  • disaster plans built-in with database replication

Pros of on-premise DWH software

  • company-run security measures
  • one-time purchase fee
  • all data held locally
  • server uptime managed internally

Cons of cloud DWH software

  • security handled by third party
  • possibly stored in a shared database

Cons of on-premise DWH software

  • company must make own disaster recovery plan
  • often little support after initial setup
  • server uptime managed internally


Data warehousing software, by design, is meant to connect as many pieces of software as your company needs to gain analytical insight, and so it’s built specifically to integrate with other pieces of software. Your data and IT teams may be required to build all or part of integrations that the data warehouse software doesn’t share an existing API connection with. This goes for connections to software that feeds into the data warehouse as well as the business intelligence or data visualization tool the company uses to analyze that data. Check carefully with potential data warehouse software providers, as custom integrations can delay initial implementation or add significant cost.


Different solutions offer different methods of access either from the data warehouse, your internal network, or even from the web. Most use online analytical process (OLAP) protocols. Depending on your needs, access to the data might be made available to data analysts, IT team members, and executives, and other stakeholders for analysis and oversight. Ensure that whatever method of access you provide doesn’t pose a barrier to these groups.

Different DWH solutions offer a variety of data latency options, from access to real-time data to significant update delays for stability. Take into account whether stakeholders want or need access to real-time data for analysis. Likewise, performance monitoring is important in determining whether you can perform an extract, transform, and load (ETL) job at the same time as a data mining procedure, or if you’ll need to plan your ETL pulls out of the way (such as 3AM on a Sunday morning) to avoid interfering with the performance of your data analysis.

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