ELT is a three-step process that first extracts raw, structured, and unstructured data from source databases, applications, data stores, and other repositories. It then loads that data into a data lake and transforms it as needed by analysts.

Since it doesn't move the data to an intermediate staging area or transform it before loading, the extraction process is speedy. You don’t need to pick and choose what data loads into the data lake or wait for it to be processed. Analysts transform a small subset of the data in the data lake, rather than having all the data go through a transformation before loading.

While ELT is a fast and straightforward method for collecting all of your data in one place, its benefits ultimately turn into drawbacks because of the impact on data quality.

Integrate Your Data Today!

Try Xplenty free for 14 days. No credit card required.

Octopus

Table of Contents

  1. Why Does ELT Lead to Poor Data Quality
  2. The Consequences of Poor Data Quality for Analytics
  3. Using ETL to Improve Your Organization’s Data Quality with Xplenty

Why Does ELT Lead to Poor Data Quality?

These aspects of ELT solutions contribute to bad data quality and have far-reaching consequences for your organization:

No data filtering or screening: ELT processes typically ingest as much data from your sources as possible to capture everything that’s collected. You create a large data lake of all the data that you can collect in case it becomes relevant or valuable in the future. However, you need to manage all this data, and since it loads directly into your data store, you aren't able to stop bad data beforehand.

Massive data sets are hard to work with: Your data governance policies need to account for these massive, unfiltered data sets. This can lead to higher overhead, human error, poor productivity, and difficulty in locating the right data for analytics.

A one-size-fits-all approach to extraction and loading: You can't fine-tune your strategy for specific data sources and types. Everything goes through the same automated process, which may not be the best choice for some of your use cases.

Lack of sensitive data masking or removal: Regulated and sensitive data loads without masking or removal, and this information may be accessible to unauthorized parties.

Challenges with data access control: Different roles, teams, and individual employees require access to a subset of your data, but they may be able to see all of it in the data lake. When they look for data to run reports on, they might pull from sets that are not relevant to their use cases.

Enjoying This Article?

Receive great content weekly with the Xplenty Newsletter!

Octopus

The Consequences of Poor Data Quality for Analytics

Poor data quality affects everything it touches. Some consequences you may encounter due to this problem include:

Bad decision-making: Leaders can’t make good decisions when they’re burdened with bad data. For example, if they expect a certain cash flow level when they’re making plans for the next quarter and find that it’s much lower than expected, the organization may have to cut back on projects and investments.

Extended time to surface insights: The data may not have the right schema or format for the analysts’ tools, so they must wait for transformation before they can work with the information. Since there’s so much data loaded into a typical data lake, the analysts also spend more time hunting down the data sets they need.

Redundant work performed: ELT may duplicate data sets, and it can take time to sort out the right ones to work with. Different teams may work on varying versions of the same data set, which can also lead to additional work.

Fixing data quality slows productivity: Analysts have to sift through the data sets and fix data quality issues before they can get to their core work tasks. This administrative overhead impedes discovering what the data can tell your organization.

Long-term opportunity costs: You may not identify opportunities before they’ve passed due to lower productivity and a harder time digging through large data volumes. Without being able to take advantage of profitable opportunities as they knock, you could end up losing competitive advantages.

Reputation damage: What happens when your customers, partners, vendors, suppliers, and other stakeholders can’t trust your data? Your organization may develop a reputation for unreliability, which can be hard to shake.

Using ETL to Improve Your Organization’s Data Quality with Xplenty

Integrate Your Data Today!

Try Xplenty free for 14 days. No credit card required.

Octopus

ELT isn’t the only option you can use to get your data ready for analytics. Extract, transform, load (ETL) tools have a much better setup when data quality is your top priority. By transforming the data after it’s extracted and before it reaches your data store, you can eliminate poor quality long before an end-user accesses the information.

Xplenty offers a powerful and easy-to-use ETL tool that streamlines the process of fixing your data quality issues. With both no- and low-code functionality, end-users of all technical levels can set up the data pipelines they need to access high-quality data for analysis. Data cleansing during transformation eliminates duplicated data, sensitive data, and incomplete records through an automated process. Give Xplenty a try when you sign up for our 14-day demo.