ETL Part 1: Your Data Extraction Is Useless Without Proper Integration

Without the right tools and approach, data extraction can be difficult and expensive enough on its own. Once you have everything you need to capture document data efficiently and cost-effectively, all that data is worthless until you integrate it into your business systems. What’s more, you’ll need to take data transformation into account. The entire process, commonly known as Extract, Transform, Load (ETL), is a vital part of your company’s data integration strategy. We’re going to take you through the basics to help you get a handle on this business-critical operation.

Generally speaking, ETL is the process of extracting data from at least one source then loading that data into a business system. For example, capturing invoice data to export into an accounting system. Between the extraction and loading steps, there is a transformation step that enables your accounting system to accept the extracted invoice data.

Good ETL Needs Great Data Extraction

Our invoice data example above is just the tip of the iceberg. On the surface, the whole process seems like it should be a piece of cake – swift, simple, inexpensive piece of cake. But the deeper you dive, the clearer the potential delays, complications, and costs become. If you get the first step – extraction – wrong, it can set off a costly chain reaction. 

Manual Data Entry is Not a Good Start

Companies that are still using manual data entry are crippling their ETL process right from the get-go. It’s slower than a dying turtle. It’s as expensive as (if not more expensive than) your company’s corporate box at your town’s major sport stadium. And it’s soul-crushing (though I suppose it beats removing hair from pork scratchings for a living, but not by much).

All these negatives create delays, excessive costs, and high employee turnover. And chances are, the data transformation step is going to suffer. Sticking with our invoice example, the data on the invoices your bored data entry clerks are entering into a spreadsheet or accounting system may not always be accurate.

Template-Based OCR is Not Much Better

Maybe your company has a legacy OCR system that can read documents then capture, transform, and export data into your business systems. Huzzah! Points gained for not subjecting fellow human beings to the tedium of manual data entry!

But, hold on a minute… Boo! Points lost for the high cost of implementing the solution – an investment that has probably yet to see a full return! More points lost for the time and resources spent creating templates and rules for new documents that your OCR system needs to read! Seriously: Every. Single. New. Document. Even more points lost every time you have to request new templates and rules from someone in IT who definitely has more important things to do!

Other Data Extraction Challenges

In addition to the approach you’re using to extract document data, you also need to take other potential challenges to the first step in the ETL process, including:

  • Continuously growing volumes of data
  • Documents with unstructured data
  • Highly variable document formats

These are just a few of the potential obstacles that you need to be aware of as you develop your company’s ETL process.

Once you’ve got a solid data extraction plan, you need to consider the manner in which you’ll transform it to ensure swift, accurate loading into your business systems.  

Data Transformation Complications

Once you’ve captured the data your company needs for its operations, you need to ensure that it’s in an accessible format and location. This means the data needs to be transformed. Otherwise, your company will not gain the business-critical insights it needs to improve efficiency, identify and capitalize on opportunities, and boost overall profitability.

Your Systems May Have a Problem With Polygamy

Dealing with relational polygamy means you’re trying to assemble data from a variety of formats and load them into one system. For instance, your enterprise resource planning (ERP) system may require data contained in images as well as data precisely laid out in a table. So you could be receiving some invoice scans in PNG format and others in a structured Word document.

That’s a simple example, as you can transform both the PNG and the Word document to PDF format, then extract the data – so basically switching from ETL to TEL. Looking at your ETL process as a whole, however, operations can get incredibly complex, so you need to be aware of how you’re not only extracting but also how you’re transforming your data.

Different Systems for Different Organizations

The fundamental issue with integrating document data from external organizations lies in the fact that every organization you’re receiving data from has its own systems and IT solutions. Also, their developers are obviously not your developers, each with their own experiences and skill sets. So when you need to apply your ETL process to integrate data from sources more complex than invoices, you and your team need to take great care when designing and developing this process.

Get Your Data Warehouse in Order

Your organization’s data warehouse stores data from a wide range of sources, including but definitely not limited to:

  • Invoices
  • Social media interactions
  • Sales figures
  • Product or service ratings and reviews
  • Leads
  • Employee performance

Again, this is tip-of-the-iceberg stuff – let’s not get started on dark data, or we’ll end up going down an infinite rabbit hole. Suffice to say, your business is probably receiving an overwhelming abundance of data every day. All of it in various formats, and all of it valuable in its own way. 

This is what makes transformation such a critical step: you need to standardize the data you’re loading from your warehouse, itself made up of various software solutions. This will help you maximize the benefits your vast stores of data have to offer.

Time to Get Loaded

With your extraction and transformation steps in order, your ETL process moves to the final step: data loading. You need to 

  • Map out what data is going to be loaded into which system
  • Define the loaded data’s purpose
  • Determine which format to present the data in after loading

If you’ve ever found yourself asking, “What am I supposed to do with this?” or “I can’t make heads or tails of this” after receiving the end result of an ETL process, you know these steps take considerable time and effort.

The Essence of ETL

As we have seen, there’s more to data extraction and integration than, say, copy-pasting values from one document into another. Nor is it a simple matter of automating a few data extraction processes. While automation plays an important role in ETL, it does not define the process. ETL is really all about communication between a complex network of systems. 

Overall, your ETL process must be:

  • Sustainable
  • Scalable
  • Capable of creating synergy among all your data.

With expert planning, careful implementation, and forward-thinking maintenance, your company’s ETL process can be game-changing. You’ll be able to work with data more creatively, elevating your competitive advantage in ways that you may have never even considered.

Coming Up Next

We hope you now have a better understanding of the importance of data extraction and integration, and how much of your company’s success is riding on the quality of its ETL process. In our next post, we’ll give you some valuable insights and advice on how to improve the way your company extracts, transforms, and loads data. 

Leave comment