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The Aspects and Values of ETL in Data Management

Data serves as a brain for businesses. A main source of information, it is the root of campaigns, processes, goals, and directives. Executives gain great internal and forward-facing knowledge through analyzing data. From streamlining processes, to creating an effective workforce, to consolidating resources, the key players in businesses benefit from having a full stream of information at their fingertips.  

Data itself, however, can be tricky to manipulate. Because it originates from various sources and enters a database at varying times, it is important that the information a company keeps is properly prepared and organized. ETL stands for Extract, Transform, and Load and is a process that gives data a thorough housekeeping. The results of the ETL process provide businesses with organized, well-groomed information that can be used to truly make a difference in marketing initiatives, sales directives, and overall business efforts. 

The Purpose of ETL

Retrieved from various databases and platforms, files, spreadsheets, CRM material, and other data are essential to daily business processes. In order to use data, its integrity and consistency must be established and verified. The ETL process efficiently facilitates the merging of information from multiple sources by gathering, cleaning, blending, and consolidating it into a useable format. It even effectively migrates datasets that span several sources, allowing businesses to combine their resources. This collaboration contributes to business strategies, decisions, and development from the ground floor to the executive suite. 

Although simplified by initials, the ETL process is one that involves much more than three tidy steps. Having to overlap and adapt the stages, techniques, and technologies adds intricacy to the undertaking. 

Putting ETL to Work - An Example

The technical aspects of ETL are complex and detailed, however the organizational benefits are achieved through time and money efficiencies. Here is an example of the flow of the ETL process and how it might help resolve a current issue, from the executive (versus technical) point of view: 

The USPS reports that businesses spend over 20 billion dollars per year on mail and packages that are undeliverable. Your business has a goal to curtail unnecessary spending on postage. You need to clean and improve your CRM which is currently an accumulation of multiple prospect lists from various sources. Each list is in a different layout and format, and duplicates may exist.  

The tools within the ETL process will prepare your CRM for cleaning through a standard set of rules that applies to all incoming data. With focus on the name and address fields, it is likely unknown in what format this information exists. The ETL process allows for specifics such as first and last names within one field to be put into two separate fields. Also, if the original fields have last name first, the rules can parse this, transferring the fields to first name before last name. 

Information relating to USA-based addresses can be standardized. For example, here are several variations of the word ‘Drive’ that might be inputted into a database record: 

  • Dr. 
  • dr. 
  • dr 
  • drive 
  • Dr 
  • Drive 

Standardization allows for sorting multiple versions of the same information into a preferred, standard format - such as the word ‘Drive’ to represent the above variations. This can be applied to other data points in the address. Internationally, rules can be created per country to follow their local postal code policies. 

Records that don’t meet the requirements of the data-matching parameters at this point, are returned for visual inspection and a decision is made regarding which record is the most accurate for retention. 

This process produces a clean database of active, up-to-date addresses that provides a better experience and results going forward. 

Step One: Extract

To merge a large volume of unrelated information, it must first be collected and assembled from its original place and format. Some of the sources of data include:

  • Current and legacy storage systems 
  • CRM platforms 
  • Mobile devices 
  • Storage locations: in-house, remote, or cloud 
  • Sales and Marketing systems 
  • Analytics programs 

Extraction is the most important yet complicated piece in the ETL process. It determines the current location of the selected data as well as its appropriateness. Extraction has two methods: logical and physical. 

Logical extraction is the more common method and works by gathering information it has been asked to retrieve. It uses a set of rules to identify certain criteria within a dataset, and then combs for information that qualifies within those parameters. Logical extraction has two methods: 

  1. Full Extraction – The entire database is extracted collectively and simultaneously each time. 
  2. Incremental Extraction – Data is pulled only if it is new as of the last successful extraction. It determines this based on timestamps implemented by the ETL tool and documentation during the previous extraction. 

Physical extraction is a second, more extensive and time-consuming approach, often used when data is in an outdated or restricted storage system. Using a bit-by-bit approach to extract data, it also has two methods: 

  1. Online Extraction – Transfers the information straight from one source to the other. It creates a direct connection between the current and new locations. 
  2. Offline Extraction – Rather than extracting the data directly from the source, it is instead held outside of its origin and stored in a flat file, dump file, or within transaction logs. 

The ETL tools of today have improved the extraction process, requiring fewer manual tools and instead utilizing automation practices. This creates increased efficiency and provides comprehensive results by addressing multiple data sources at once. 

Step Two: Transform

At the time of transforming the data, a business gears up for customization. Preferences and goals for how the data is presented are determined during this step. The transform process within ETL involves several undertakings to achieve data standards, readiness, and availability. Rules and requirements are instilled at this phase, duplications are removed, and integrity is improved.  

The transform process involves cleaning and standardizing the data, removing redundancies and duplications, and verifying and sorting good information, purging that which is unusable. 

In essence, businesses set their criteria at this phase of the ETL process, determining how the data is to be compiled for use going forward. 

Step Three: Load

The last step of the ETL process is loading the cleaned and sorted data into the selected data warehouse where it is prepared for end users. Within this process, there are two ways of merging data: full and incremental. Full loading involves bringing the entire collection of data into the new repository at once. Managing this amount of data simultaneously has its benefits and challenges. Although it is useful for data to be available as needed, the amount of fully loaded data, and the growth it experiences in day-to-day business can make bulk upkeep taxing. 

As the name suggests, incremental loading involves loading data only when new and unique information pertaining to a specific record is found. A financially feasible and time saving approach for smaller businesses, incremental loading is also a frugal angle for larger ones. With less data being pulled, less time is required to review, validate, and adjust the information. The incremental design of loading data allows for conservative or smaller business entities to have the organization of ETL without being overwhelmed in time, money, or resources at once. 

Once the load stage is complete, it is time for the business to decide how best to use their organized, cleaned, and stored data.  

ETL is Full of Advantages

The decision to implement an ETL process within your business data is something worth considering. Delivering valuable benefits, ETL is a powerful tool to drive businesses and their workforce toward more efficient and effective practices.  

Some of the benefits of ETL include: 

  1. Scalability: ETL offers the ability to ramp up or down, per the needs of your company and your strategies.  
  2. Simplification: ETL makes data easier to understand and manage by maneuvering it based on the needs of the end-user: from administrative assistant to sales manager to stakeholders, data is made manageable.  
  3. Savings: Rather than using employee hours to process data, ETL offers an efficient process that saves time and money.  
  4. Handling: ETL far outweighs manual data processing in its coordination, accuracy, and ability to manage large datasets from multiple sources.  
  5. Customization: Data is formatted the way your business requires it. 
  6. Historical and Current: ETL combines archived data with current, therefore producing helpful views containing both historical and recent information. The old and new can be viewed alongside one another. 
  7. Reliability: With ETL, there is considerably less risk of human error, assuring accurate and reliable information.  

Bringing it Together

Through data, businesses gain insight, prompting ideas and initiatives for strategies that streamline internal processes, improve products and services, and enrich customer experiences. Utilizing information to its fullest is pivotal. ETL is a way to bring past and present data together, organizing it in a way that provides forward knowledge, a competitive edge, and improved efficiency. 

Whether your business enlists in-house developers or a partner in data management services, the addition of ETL to your data process promotes future initiatives by providing the needed hindsight to promote essential, productive foresight.  


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