ETL – The First Step in Data Analysis

Data is universal – the global community generates as many as 2.5 quintillion bytes of data per day. For a company, data represents its fundamental reality. Corporate data reflects all the elements of manufacturing, production, sales, workforce, revenues, losses, and every other aspect that flows from developing, selling, and delivering goods and services.

 
The biggest data challenge presented to businesses today is harnessing its’ mass into a productive, informative enterprise asset. The difficulty arises because a wide variety of disparate sources generate all this information, most of which use different types and styles of data to report and reflect their activities. As a collection, each unique data bit contributes to the comprehensive picture of each transaction, and all are necessary to complete a full understanding of their common result. However, gathering all those relevant bits, making sense of them, and then structuring a report to measure their aggregated value is an incredibly tricky project.

Datavail’s Data Management experts use cutting-edge “Extract Transform Load’ (ETL) practices to ensure their clients have access to and can effectively use all their data, regardless of its origin.

From One Flows Many

To understand the complexity of the ETL process, for example, consider that every purchase transaction anywhere in the world triggers numerous other data-developing transactions all related to that one activity:

• Item or product data: The item purchased carries with it information related to multiple data sources, including but not limited to production lines, supply chains, product management teams, inventory, and storage. Each purchase represents the transfer of that item from the manufacturer to its purchaser by way of any number of transportation systems; each step and element in that delivery process represents an individual data-generating transaction related to that purchase.
• Purchaser data: Information relating to the purchaser of an item is also critical. They have selected this particular product from a variety of possible options. They may also be looking for accompanying products to go with their purchase. They’ve used some form of financial process to make the purchase (credit or debit card, or one of several online payment portals). The product requires delivery to the purchaser, which entails a physical address and an appropriate logistics system to transport and deliver it in a timely manner. Each step related to this purchase by this purchaser is data that is relevant to the product.
• Manufacturer or producer data: Creating any product requires aggregating the resources of required materials, production facilities, production equipment, logistics systems, warehousing, advertising, etc.

Making Data Useful

Each element and movement of a product and the aggregated resources it contains or consumes are individual data bits that impact its cost, quality, availability, and affordability. Each of those data bits enters corporate data stores in its own unique format and type, and those types are usually inconsistent or incompatible with other data types related to the same transaction.

The Extract Transform Load process is the process that extracts and evaluates all this raw data, transforms it into formats that are compatible with other types of data, then loads it into corporate data analysis programming for use by its owner. Datavail follows industry best practices in its ETL activities, such as:

• Beginning with source analysis. Corporate data comes from many sources, such as the manufacturing floor, supply chain vendors, materials suppliers, market availability, etcetera. Some sources generate structured data, which comes in the form of tables with columns and rows. Structured data from one source is compatible for analysis with structured data from other sources. Unstructured data, on the other hand, often comes in the form of audio files, images, and other nonstructured formats. Information contained in nonstructured data types must be extracted and transformed into a format compatible with structured data.
• Recognizing the source and type of data is one step; solving issues found within it is another. Not surprisingly, incoming information is rarely perfect in its form. It may be incomplete – parts of a particular record may not have been transmitted with the rest of the document. It may also be erroneous and not appropriate for use within the organization. Data managers evaluate the information they’re looking at using sophisticated filters and software programs to identify predictable errors, anomalies, and other invalidating concerns that often appear in the database and within the data itself. Many data repair programs are automated, so even the experts aren’t required to peruse each piece to cleanse a batch.
• Auditing the process as it continues. An audit program oversees the ETL project to ensure that it’s going according to strategy and stays on task to achieve its desired goal. Auditing can capture anomalies or abnormalities within the aggregate data set even when errors aren’t in evidence.
• ETL logging is also a critical task within the ETL process. Logging requires making a record of each step taken within the ETL process, including recoding any events that occurred before the ETL process began, all the steps and activities that happened during the process, and the conclusion of the process after depositing the revised data set into its appropriate data store.

 
Additionally, the process of moving large amounts of data from one format to another and from one storage type to another can generate errors in itself. Datavail’s ETL experts set checkpoints throughout the process from which they can recover should an error cause the process to fail. Using checkpoints allows programmers to stop and recover or repair a mistake and then continue on without having to start the project again from the beginning.

The fundamental purpose of the ETL process is to release all data values to be available for analysis by corporate data analysts. Consequently, the organizational requirements that the data will be informing will also structure the extraction, transformation, and loading process to ensure that the final loaded database has both the information and the software needed to pursue all corporate goals.

Learn how a Data Expert Improved its Data Integration, Storage and Reporting by modernizing their data capture, conversion, storage, management, and retrieval system.