An overview of data processing services,
past vs. present

Data processing services: what’s new?

Data is one of the most fundamental elements of your business’s operations. Without the correct data in a valid format, your teams will be unable to achieve their objectives. Accounts payable (AP) teams need the data in invoices to ensure they are appropriately paid. 

Healthcare professionals access and manage the data in patient reports, and manufacturing companies rely on the data in inventory documents. This data, in its various forms and sources, is essential to your organization. The real challenge is how to process the data. 

The problem is that most business data today is stored in unstructured formats. This is one way of saying that most professionals utilize PDF scans of paper documents to share data back and forth throughout an organization. 

PDFs store document data in an image format. This image merely displays the document but provides no way for computer systems to recognize and record the data. 

Solving this problem is the goal of manual data entry.

Manual data entry is one of the oldest and most established ways of data processing in business. In this method, employees simply read the documents and type the data into a structured format like an Excel spreadsheet. 

Although this way of data processing is well-established, it possesses several inherent drawbacks. It is expensive, error-prone, and tedious. Inevitably, paperwork piles up on your teams’ desks, causing demotivation and potentially driving some of your most talented employees out of your organization. 

One way to avoid the pitfalls of manual data processing is to utilize data processing services. 

These services allow you to outsource your data processing so that your team can spend more time on the tasks that can grow your business. There are various types of data processing services you can implement. 

One data processing service example is data annotation outsourcing. Data annotation is the process of labeling data to help artificial intelligence systems learn how to recognize data types and produce useful output. By outsourcing data annotation, you can relieve your employees of the burden of doing this time-consuming task. 

The importance of data processing in business cannot be understated. It’s vital to understand both the positives and negatives of outsourcing data processing.

Furthermore, the software might be a better solution for alleviating your employees, capturing data more accurately, and maintaining complete control over your data processing. 

Professional data entry services: BPOs vs. SSCs

When dealing with tedious and repetitive business tasks that can sidetrack operational initiatives, there are often two ways to address them. When looking at any data entry services list of data processing companies, you’ll usually find them offering BPO options. 

BPO is an acronym for business process outsourcing and refers to the notion we have already discussed – hiring a professional data entry services firm to process your data. Another method is to set up a shared services solution (SSC). 

A shared services center is an internal business unit responsible for handling data processing tasks (or any other specific process) for all departments within your business.

BPOs do have some advantages over SSCs. Because they often have more experience and resources than your organization, BPOs often offer more savings and faster implementation than an internal solution like an SSC. BPOs also give you flexibility in terms of the size of your financial commitment.

On the other hand, SSCs gives you complete control over your data. This is a huge advantage regarding business objective alignment and data security. With the right software tools, SSC can dramatically cut the costs of data processing and enable all of your various business functions to be streamlined. Although they may take longer to implement, SSCs more than compensate for that over time, thanks to the better results they can deliver.

Before the data processing cycle

Before outsourcing or building your internal business unit, it’s important to understand the data processing cycle fully. This cycle outlines the journey data takes as it moves through various stages in your business. First of all, the raw data is collected. The raw data can come from a variety of sources. 

As we have already mentioned, many of the sources will be in unstructured formats. The next step is preparation. In this phase, the raw data is filtered to remove duplicates and errors. Preparation could also be referred to as validation. The goal of this stage is to ensure that only the best quality data is used in business processes and decision-making. 

After that phase comes the input stage. Input is simply converting the raw data into a machine-readable format. Input can be done manually, it can be outsourced, or it can be automated using the software.

After that, the data is processed and then sent to an output system or stored, depending on its function. We’ve found that examples are often helpful when discussing these topics. There are several examples of data processing that we could look at, but accounts payable (AP) is one of the best. 

In AP, the raw data is collected in the form of invoices (often either in paper or PDF format). This data is prepared and then input into an accounting or ERP system. After that, it is processed, and the vendor or supplier is paid according to the collected data. Then, a new invoice arrives, and the cycle begins again. 

Types of data processing

There are, generally, three main types of data processing companies can choose to utilize:

1. Manual data processing
2. Outsourced data processing services
3. Automated data processing utilizing a software platform

These first two options can be very effective and affordable but require you to give up most of your control and can result in a lack of innovation regarding workflows. 

The third data processing method (automated data processing) is the most ideal because of its ability to process data rapidly and accurately without sacrificing data security and control. 

The right kind of software can result in huge savings of both time and money. It can simplify your data processing, leaving your teams more time to focus on the tasks and activities that matter to your organization.

Consider an example of data processing using this method:

An invoice comes to an employee on your AP team. They upload it to a system that automatically extracts the data and exports it directly to the destination system instantly. 

Furthermore, consider that the same employee could batch process hundreds of invoices in the same manner. This speed allows you to get near real-time data processing entirely automatically. Comparing the automated process vs. the manual input will give you valuable insights into your company’s workflow and efficiencies — or lack thereof.

Outsourcing data entry services: pros and cons

Even though utilizing outsourced data entry services may sound like an effective alternative to manual data entry, it does have some pretty severe downsides. First of all, outsourced data services do create third-party risks for your data security. 

It’s important to find a trusted partner to help you manage your data if you want to go this route. Another issue with outsourced data entry services is the lack of adaptability. 

The service provider will have fixed methods that may or may not suit your needs. Keeping your data processing in-house allows you to customize and improve your data processing workflows. Instead of relying on online data entry services, it frequently makes more sense to utilize effective automation software to maintain complete control over your data processing tasks. 

Data annotation services

One of the primary uses of outsourced data processing is data annotation services. The vast majority of artificial intelligence and machine learning systems process text data. To train these machines to be able to process this text accurately, data annotation is frequently required. The more labeled data these machines have to work with, the more accurate their processing becomes. 

However, manually labeling and categorizing all the data required for these machines can be an enormous challenge. That’s why many companies look to data labeling companies that have the experience and technology to complete these kinds of tasks quickly and accurately. 

Online data processing companies can be a very convenient solution for organizations looking for a quick and affordable way to annotate their data.

Image data entry services

Image data entry services are designed to help organizations extract and export data stored in images and PDF files so that it can be used in systems and stored in the correct destinations. However, relying on outsourced data processing for image data entry has all the pitfalls we have already mentioned. 

That’s why it makes more sense to rely on software-based data processing solutions. One great example of an intelligent data processing (IDP) platform is Rossum. 

Rossum makes it easy to collect and upload raw data from several channels. Then, with just a few clicks, you can automatically extract and export the data you need from one, or hundreds, of documents. 

Finally, with Rossum’s flexible API, it’s easy to build an integration with your destination applications and systems, allowing you to build completely paperless workflows.