Organisations spend a lot of time and effort collating their data before analysing it to gain insight. Although the result they are looking to achieve is the same, their approaches will vary significantly. In this blog, we compare two different methods that are currently used: ETL and Data Preparation. These are the two most common methods used for collation.
- Extract-Transform-Load (ETL) is the traditional Data Integration method.
- Data Preparation – also known as Data Wrangling – is a technique that sits within the areas of Data Quality and Self-Service Analytics.
Despite being based in two different disciplines, both methods share a lot of the same concepts and both solve the same problem. They transform and shape raw data into a form that can be used to enable business information analysis and reporting. But it’s not immediately obvious which one is the best option to apply to your business problem.
To gain a deeper understanding, let’s consider some of the differences:
1. Solution owners and users
ETL technologies, which are managed by IT experts, can benefit from tighter governance, change control and a formal workflow. This is often a suitable choice for your business-critical processes and those which are often repeated or scheduled. The downside is that ETL processes may be expensive and time-consuming to amend, even for small changes.
Data Preparation sits with the business users, the people who interact with the data daily and know its intricacies. Data Dashboard and Preparation tools are visual in nature (not dissimilar to an Excel spreadsheet) which allows non-technical users to investigate, prepare and correct data quality issues. This enables fast and dynamic wrangling as the Data Preparation effort is distributed across departments, rather than being controlled by a small group of highly technical IT professionals.
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2. Use cases
ETL is ideally-suited to business-critical processes, and it’s still the recommended method for loading raw data into centralised Data Warehouses for downstream reporting and analysis. ETL should also be considered for tasks that require complicated transformations or intensive processing. ETL is the go-to solution when you need reliable production of data of the highest quality.
Data Preparation solutions excel at carrying out an ad hoc, investigative and exploratory task with new data sources. They also provide an effective way to support data quality and data scrubbing activities, enhancing the value of data in an agile and flexible way. Data Preparation shines when you want to evaluate a set of scenarios by assessing various data combinations quickly.
Data Preparation and ETL as complimentary solutions
Businesses can deploy both ETL and Data Preparation solutions within their platform to maximise value from their data. As a part of data strategy solutions framework, IT experts can implement ETL solutions, which integrate data into the business, making it available to business users who can then self-serve, explore and generate value from the data using data preparation tools. Data Preparation can also inform ETL design from the discoveries made wrangling the data, creating a feedback loop between the two.