The constant flow of data to companies can be an enigma. It’s extremely valuable when utilized properly, however, it requires proper processing using automated techniques for data. When businesses automate data processing, they can eliminate the confusion from the equation and can maximize the value of data and information.
We’ll explore the examples of automated data processing, and how software for finance automation can be extremely useful.
What Is Automated Data Processing?
Automated data processing is the use of technology to manage, update, and process data. In order for this process to happen, computers and communications systems are required, which allow you to store, collect, transform, process, and distribute data.
Data processing, in general, includes:
- Aggregation: combining and collecting data from various sources
- Validation: Verifying that the data is true and clear
- Conversion: Conversion of data to another medium or language to be used
Automated data processing is essential for companies to keep an edge in the market. The data is available across all business departments, which is why it allows professionals all over the world to gather insights that will increase productivity, cut expenses, and increase efficiency.
Benefits of Automatic Data Processing?
When you automate data processing, your team can have the time and the resources to concentrate on actual analysis instead of dealing with the processing aspects of the process.
In addition to this major benefit, your company also could reap numerous benefits, such as:
1. Boost Efficiency
As you may imagine (or already have a good idea) manually processing data takes a lot of time. It takes days, hours, and even weeks to gather, process, and convert data to use.
With data automation even in Excel however, you’ll spend longer than you need managing a large amount of data. Automated data processing speeds up the process significantly, as do automation tools.
2. Reduce Silos
Another issue with the manual data processing can be the data siloing. The ideal scenario is for data to be accessible to all departments and other stakeholders who require access.
When you’re working on different desktops and systems, data frequently gets tangled across departments, and this can hinder the possibility of having a single outlook.
3. Increase Security
The most important aspect to be aware of when managing data is security, there’s no doubt! Whatever the level of care that humans are, there is always a possibility of mistakes.
Automated data processing eliminates the chance of mistakes, which can result in expensive and damaging results. Additionally, with top-of-the-line security, software for financial automation ensures data integrity and keeps up-to-date to secure the data.
Also read: 10 Ways of Encryption Key Management and Data Security
Limitations of Automated Data Processing
While it’s a great idea to automate data processing, it’s not going to solve each challenge you could confront. In particular, you’ll need to consider the following challenges to consider when automating data processing
1. Data Quality
The output of data automation is as good as its inputs, which means it’s up to you to gather accurate and reliable information.
2. Cost
Based on the software you select, the cost could be a major factor. But, there are solutions such as SolveXia which are not just affordable, but also can assist in automating numerous intricate tasks, like: reconciliation, expense analytics, rebate management, and much more.
3. Change Management
Similar to any other technology that is implemented, change management is an integral part of the picture. You must make sure that your employees understand the reason behind the tool and how it will benefit.
Top Automated Data Processing Techniques
There are various types of automated data processing techniques used by software solutions. The most popular of these methods are:
1. Batch Processing
Like the name suggests, this type of data processing automation involves processing massive quantities, or in batches, with a specific frequency. It doesn’t matter if it’s either weekly, daily, or monthly, data gets taken into action. This is how the majority of companies manage payroll every month.
2. Real-Time Processing
If there is a small amount of data that could be relevant immediately, real-time processing may help to gain insight. Real-time processing is extremely useful to determine cause-and-effect connections.
3. Multiprocessing
Multiprocessing happens whenever more than one processor is processing the same data in a single system. Because of the amount of power required, it speeds up data processing, which helps solve issues quickly.
4. Time-Sharing
If one processor is shared between users simultaneously, it’s referred to as time-sharing. In reality, the processor can work with data through the server using various “time slots.”
5. Distributed Processing
The last but not least, the distributed process splits the data between processors, allowing data to be moved efficiently across multiple servers.
Elements of Data Automation
To automate data processing, it runs through three major functions, referred to by the acronym ETL that stands for:
1. Extract
The process starts by taking (extracting) the data out of a single source or from multiple sources. For instance, SolveXia integrates with your existing technology stack to gather and store data seamlessly.
2. Transform
Transformation is the process of converting data into the structure required, like a CSV file. To convert data for use by a system you want to use You may have to change the data format so that the data can be read correctly. In one instance, this can be as easy as changing abbreviations of state names to written-out names for states.
3. Load
Then the data will be transferred to the output system you want to use.
Also read: What is the Data Analysis Process? Types and Tools
How to Build a Data Automation Strategy?
As a company moving towards automated data processing, your process might be different for every company, however, it’s helpful to have a plan that is a kind of. These steps can assist you to set you up to be successful:
1. Identify Pain Points
Begin by looking at the current state of your data. What departments would benefit most from automated data processing? You can determine this quickly by revealing the amount of your team’s time currently spent modifying data manually. The accounting and finance teams are usually the ones that are most engrossed by data.
2. Classify Data
Sort data according to its importance, usability, and accessibility.
3. Prioritize Operations
The list below highlights areas where automated data processing is most effective and beneficial.
4. Define Requirements
Make a list of the things needed to convert data into the desired dimension and format.
5. Execute Operations
Naturally, the execution can be the toughest part of the process, however, if you determine how to set up pipelines, reporting, and machine-learning processes, then you’re already a step ahead. Furthermore, when you select an application that can meet every need, deployment is easy.
6 Steps to Automate Data Processing?
With your automated data processing strategy in mind, it’s time to put it into practice! Below are some steps you must take to automate data processing
1. Identify Data
Set business goals and decide the necessary data sources in order to meet those goals. Find out where your source data is located and what systems are involved.
2. Set Data Access
What is the method of obtaining data? It depends on the format the data is. The process will also involve people who are currently responsible for the data in order to define the data’s situation.
3. Select Tools
In this phase, you’ll need to choose the right software for data processing that is suitable for your company. It’s helpful to select an option that is simple to use, flexible, collaborative, scalable, and safe.
4. Define Operations
Determine the format in which the data is stored in, and the output it needs to be converted into to use.
5. Develop the Process
Try the software out to make sure you are sure that the ETL process is smooth.
6. Schedule and Review
Automate your processes and determine the frequency that the process will take place. Review and analyze how the system is performing to make sure it is delivering your desired outcomes.
Also read: Top 10 Data Processing Tools and Software
Types of Automated Data Processing?
The use of data automation is widespread all over the world, and includes:
1. Data Integration
Data integration is the process of looking at data in a unifying way. It means data gets gathered from a variety of sources and then transformed so that it can be stored in an appropriate repository.
2. Data Transformation
The process involves transforming data into a format that is suitable for analysis, which can be a process of some sort of data manipulation. Automated data transformation can save lots of time and let your team focus more time on the analysis part of the process.
3. Data Loading
Data loading is like loading pallets into containers. Once everything is properly wrapped and arranged correctly, the data can be transferred to the data warehouse, from where it will be securely stored and then accessed to use.
Best Practices to Automate Data Processing
The goal of automating data processing is a major and crucial one, and requires a well-planned and well-defined vision.
To ensure success in your endeavor to be successful, we’ll share some of the most effective tips for success:
- Make sure you are certain of your goals and intentions at the outset
- Recognize the data sources ripe for automation
- Spend time researching tools that can assist you and make sure you are discerning about your choice of solution
- Engage stakeholders to ensure everyone is on the same page
- Apply solutions slowly and cautiously, and test as you go
- Document processes are clearly defined so everyone knows their roles
- Always monitor your outputs and remain open to constant improvements
Summary UP
If you automate data processing, you’re capable of reducing time, increasing productivity, minimizing errors, and most importantly, you can trust your data and insights with absolute confidence.
With this much data integrity and accuracy available, it is possible to make informed choices at any moment in the present, while maintaining your competitive edge, while improving the customer and employee experience in the process.
