What Is a Data Processing Agreement?

First, we need to define what is data processing. It is generally referred to as the collection and manipulation of items of data to process meaningful information. The process of conversion or manipulation is carried out using a predefined sequence of operations either automatically or manually. Data processing is basically synchronizing all the data entered into the software in order to filter out the most useful information out of it. This is a very important task for any company as it helps them in extracting the most relevant content for later use.

A data processing agreement refers to a legally-binding document to be entered into between the data controller and the data processor that covers how to handle the personal data gathered from the data subjects. This agreement can be done in writing or electronic form and serves to regulate the complexities involved in data processing such as the scope and purpose as well as the relationship between the controller and the processor.

Why Is a Data Processing Agreement Needed?

A data processing agreement is required for all businesses, large and small because it is practically impossible to run a business without processing personal data and exchanging it with other businesses. It could be website analytics software or cloud storage, and whether you are the controller, processor, sub-processor, or joint controller, you must create a legally binding data processing agreement with the party with whom you exchange personal information. Given the task’s complexity, it’s best to have a data processing agreement as a separate document.

Elements Of a Data Processing Agreement

A data processing agreement that conforms to the standards set by the GDPR (General Data Protection Regulation) has the following elements in place:

General information. This includes the activities involved in data processing, how personal data is used, who is responsible for ensuring GDPR compliance, and how long the processing will last. It also defines data subjects (customers or users), the types of data to be processed, how and where data is stored, and contract termination terms.Responsibilities of the data controller. When it comes to GDPR compliance, the controller is responsible for establishing a lawful data process and respecting data subjects’ rights. The controller is also in charge of issuing processing instructions and directing how data is handled by the processor.Responsibilities of the data processor. Processors are subject to plenty of obligations under GDPR. Maintaining information security, cooperating with authorities in the event of an investigation, reporting data breaches, providing opportunities for audits, record keeping, deletion or return of data at the end of the contract, and other responsibilities are among them.Technical and organizational requirements. This element discusses what methods will be used to encrypt, access, and test data. It also asks if both parties can ensure that processing systems and services maintain their confidentiality, integrity, availability, and resilience. GDPR requires controllers and processors to consider how cutting-edge technology, implementation costs, and variations in personal freedoms affect their ability to ensure ongoing data security.

Roles In a Data Processing Agreement

Here are the roles of the parties involved in a data processing agreement:

Types of Data Processing

Here are the most common types of data processing:

Transaction Processing. In mission-critical situations, transaction processing is used. These are situations that, if disrupted, will have a negative impact on business operations. As an example, as previously mentioned, processing stock exchange transactions. The most important factor in transaction processing is availability. Factors such as hardware and software can have an impact on availability.Distributed Processing. Often, datasets are too large to fit on a single machine. Distributed data processing divides and stores large datasets across multiple machines or servers. The fault tolerance of a distributed data processing system is high. If one of the network’s servers fails, the data processing tasks can be redistributed to other available servers. Distributed data processing can also save businesses a lot of money because they don’t have to build expensive mainframe computers and invest in their upkeep and maintenance.Real-time Processing. Real-time processing, like transaction processing, is used in situations where output is expected in real-time. However, they differ in how they handle data loss. In real-time processing, incoming data is computed as quickly as possible. If it encounters an error in the incoming data, it ignores it and moves on to the next chunk of data. The most common example of real-time data processing is GPS tracking applications. In comparison, consider transaction processing. Transaction processing aborts ongoing processing and reinitializes in the event of an error, such as a system failure. In cases where approximate answers suffice, real-time processing is preferred over transaction processing.Batch Processing. Batch processing, as the name implies, is the process by which chunks of data stored over time are analyzed together, or in batches. When a large volume of data needs to be analyzed for detailed insights, batch processing is required. For example, a company’s sales figures over time will typically be batch processed. Because there is a large amount of data to process, the system will take some time. It saves computational resources by processing data in batches. When accuracy is more important than speed, batch processing is preferred over real-time processing. Additionally, batch processing efficiency is measured in terms of throughput, which refers to the amount of data processed per unit time.Multiprocessing. Multiprocessing is a data processing method in which two or more processors work on the same dataset. It may sound identical to distributed processing, but there is a distinction. Different processors reside within the same system in multiprocessing. As a result, they are in the same geographical location. When a component fails, the system’s speed suffers. Distributed processing, on the other hand, employs servers that are autonomous of one another and can be found in various geographical locations. Multiprocessing is analogous to having a data processing system on-premise. Companies that handle highly sensitive information may prefer on-premise data processing over distributed processing. The most obvious disadvantage of this type of data processing is the high cost of building and maintaining in-house servers.

Steps Or Stages Of the Data Processing Cycle

As stated earlier, a data processing agreement defines clear roles and obligations for data controllers and data processors. With that being said, here are the steps or stages that a piece of information or data goes through as it is being processed:

  • 1. Collection Stage

    The first stage of the cycle is data collection, which is critical because the quality of the data that is being collected has a large impact on the output. The data collection process must ensure that the data gathered is both defined and accurate in order for subsequent decisions based on the findings to be valid. This stage provides both a baseline against which to measure and a target for what needs to be improved.

  • 2. Preparation Stage

    The manipulation of data into a form suitable for Further Analysis and processing is referred to as preparation. Raw data cannot be processed and must be validated. Data preparation is the process of creating a data set from one or more data sources for further exploration and processing. Analyzing data that has not been thoroughly screened for errors can result in highly misleading results that are heavily dependent on the quality of the data prepared.

  • 3. Input Stage

    Input is the task of coding or converting verified data into a machine-readable form so that it can be processed by an application. Data entry is performed using a keyboard, a scanner, or data entry from an existing source. This time-consuming process necessitates speed and precision. Because a large amount of processing power is required to break down the complex data at this stage, most data must adhere to formal and strict syntax. Because of the associated costs, many businesses are opting to outsource this stage.

  • 4. Processing Stage

    When data is processed, it is subjected to a variety of powerful technical manipulations using machine learning and artificial intelligence algorithms to generate an output or interpretation of the data. Depending on the type of data, the process may consist of multiple threads of execution that execute instructions at the same time. When you know where to look, you can find a variety of Data Processing Software. The use of software allows for the processing of large amounts of heterogeneous data in very short periods of time.

  • 5. Output Stage

    The stage of output and interpretation is where processed information is transmitted and displayed to the user. Users are presented with output in a variety of report formats, such as graphical reports, audio, video, or document viewers. The output must be interpreted in order to provide meaningful information that will guide the company’s future decisions.

  • 6. Storage Stage

    Data storage is the final stage of the data processing cycle, in which data and metadata (information about data) are stored for future use. The significance of this stage of the data processing cycle is that it allows for quick access and retrieval of processed information, allowing it to be passed directly to the next stage when required.


What are the things I need to check when signing a data processing agreement?

Check whether your data processors provide adequate guarantees for the protection of the data transferred to them before signing a DPA. According to the GDPR, if there is a data breach, even if it is on the part of the processor, you, as the controller, may be held liable. As a result, it is critical to select processors who take adequate precautions to reduce the risk of a data breach. Furthermore, processors must take adequate precautions to mitigate the impact of a breach and notify you in a timely manner.

Data processors should not be able to process your data for any purpose other than the one specified in your DPA and in the outsourcing agreement. As a result, you should investigate how the processor intends to use the data you provide to it; whether it is in accordance with your contract, or whether the processor intends to use the data for its own purposes. You must also ensure that the scope of the processor’s DPA does not exceed the original legal basis for processing the personal data.

What is the GDPR?

The General Data Protection Regulation (GDPR) is a European Union (EU) law regulation on data protection and privacy in the European Union and the European Economic Area (EEA). The primary goal of the GDPR is to improve individuals’ control and rights over their personal data while also simplifying the regulatory environment for international business. If the data controller, processor, or the data subject is based in the EU, the regulation applies. Under certain conditions, the regulation also applies to organizations based outside of the EU that collect or process personal data from individuals residing within the EU. The regulation does not apply to a person processing data for purely personal or household purposes, with no connection to a professional or commercial activity.

Who is required to have a Data Processing Agreement?

When using data processors, data controllers must have a GDPR Data Processing Agreement (DPA) in place. This is due to the fact that data controllers will be sharing legally protected personal information with data processors during the course of this relationship, and a DPA will help ensure that the data processor agrees to handle the data appropriately.

In conclusion, whenever a business collects or processes personal data, an effective data processing agreement should be in place. It enables them to set out a relationship between the parties and sets the terms on how the said data should be processed. It is also a legal requirement and having one in place reduces the opportunity for any form of legal dispute to unfold. Creating a data processing agreement can prove to be a very complex process and to add to that, there is no universal approach in making this document as every business can be unique in its own respect. In this article, examples of ready-made data processing agreements are available for you to download and personally use as a reference in case you are experiencing difficulties in making one.