How to Automate Data Processing? Experts Guide

The influx of data for businesses can be a double edged sword. It’s of great value when used properly, but it also requires adequate handling with data automation techniques. When companies automate data processing, they remove the chaos from the equation and are able to maximize the benefits of data and insights.

We’re going to take a look at automated data processing examples and how finance automation software can be of great use.

Coming Up

What Is Automated Data Processing?

What are the Benefits of Automatic Data Processing?

What are the Limitations of Automated Data Processing?

What are the Top Automated Data Processing Techniques?

What are the Elements of Data Automation?

How to Develop a Data Automation Strategy?

What are the Steps to Automate Data Processing?

What are the Types of Automated Data Processing?

What are Best Practices to Automate Data Processing?

Wrap Up

What Is Automated Data Processing?

Automated data processing is the use of technologies to update, handle, and process data. For this automation to take place, computers and communications systems are involved, making it possible to gather, store, transform, prepare and share data.

Data processing, in general, consists of:

1. Aggregation: collecting and combining data from multiple sources
2. Validation: ensuring that the data is accurate and clean
3. Conversion: translating data into a different language or medium for use

Automated data processing is crucial for businesses to maintain a competitive edge. Data exists across business departments, so it allows professionals across the board to gain insights to maximize their productivity, decrease costs, and boost efficiency.

What are the Benefits of Automatic Data Processing?

When you automate data processing, your team has the time and resources to focus on conducting actual analysis, rather than dealing with the processing aspect of things.

Along with this grand benefit, your business has the potential to gain various upsides, including:

1. Boost Efficiency

As you can imagine (or already know), manual data processing is insanely time-consuming. It takes hours, days, and weeks for people to collect, sort through, and transform data for use.

Even with data automation in Excel, you’re going to wind up spending more time than necessary to manage a high volume of data. Automated data processing expedites the process dramatically, and automation tools like SolveXia allow you to boost process time by 85x.

2. Reduce Silos

Another challenge of manual data processing is siloed data. The ideal goal is that data be accessible for all departments and stakeholders that need access to it.

However, when you’re working across disparate systems and desktops, data often gets held up across departments, thereby preventing the ability to have a unified outlook.

3. Increase Security

A key concern when managing data is security, no doubt! No matter how meticulous humans are, there’s always room for error.

Automated data processing removes the risk of typos that can lead to costly and negative outcomes. Furthermore, with top-notch security, finance automation software maintains data integrity and updates to keep data stored securely.

What are the Limitations of Automated Data Processing?

As grand as it is to automate data processing, it’s not going to solve every challenge that you may face. For example, you’ll still have the following considerations to overcome when you’re automating data processing:

1. Data Quality

Data automation’s output is only as strong as its input, so it’s still up to you to collect accurate and adequate data.

2. Cost

Depending on the solution you choose, cost can become a limiting factor. However, you can find solutions like SolveXia that are not only cost-effective, but can also be used to automate many complex functions, such as: reconciliation, rebate management, expense analytics, and more.

3. Change Management

As with any technology that you implement, change management is a part of the overall picture as you need to ensure your team understands the purpose for the tool and how they stand to benefit.

What are the Top Automated Data Processing Techniques?

There are different types of data automation and automated processing techniques deployed by software solutions. The most common of these techniques are:

1. Batch Processing

As the name implies, this form of data process automation entails processing in large quantities, or batches, at a defined frequency. Whether it be daily, weekly, or monthly, data is put into action together. For example, this is how most companies deal with payroll on a monthly basis.

2. Real-Time Processing

When there are small amounts of data that are of use right away, real-time processing can help to generate insights. Real-time processing is very useful for determining cause/effect relationships.

3. Multiprocessing

Multiprocessing occurs when more than one computer processor processes the same dataset simultaneously within a unified system. Given the amount of power involved, this expedites data processing to help solve problems rapidly.

4. Time-Sharing

If a single processor is shared amongst users at the same time, it’s considered time-sharing. In practice, the processor will work data through the server in different “time slots.”

5. Distributed Processing

Last but not least, a distributed process breaks up the data across processors to move data efficiently across the multiple servers.

What are the Elements of Data Automation?

To automate data processing, it goes through three main functions known as ETL, which stands for:

1. Extract

The process begins with pulling (extracting) data from a single system or multiple sources. For example, SolveXia integrates with your existing tech stack to collect and centralize data seamlessly.

2. Transform

Transformation is the adaptation of data into whatever structure is required, such as a CSV file. In order to transform data to be used by a desired system, you may need to change data formats so they can be read properly. As an example, this could be as simple as changing state abbreviations to written-out state names.

3. Load

Last but not least, you’ll move data into the desired output system.

How to Develop a Data Automation Strategy?

As a company that is on the road to automate data processing, the journey may look slightly different for every business, but it is helpful to follow a roadmap of sorts. These steps can be helpful in setting you up for success:

1. Identify Pain Points

Start by assessing your current data situation. What departments can benefit most from deploying data process automation? You can figure this out quickly by being honest about how much of your team’s time is currently being spent manipulating data manually. Hint: Accounting and finance teams are often the most bogged down by data.

2. Classify Data

Categorize data based on its usability, importance, and accessibility.

3. Prioritize Operations

List where automated data processing would be the most beneficial and impactful.

4. Define Requirements

List what’s required to convert data into the target size and output.

5. Execute Operations

Naturally, execution tends to be the hardest part, but if you can plan how to set up reporting, pipelines, and machine-learning procedures, you’re a step ahead. Plus, when you choose a system that can handle all your needs, such as SolveXia, the deployment becomes a breeze.

What are the Steps to Automate Data Processing?

With your data processing automation strategy in your mind, it’s time to put the plan into action! Here are the steps required to automate data processing:

1. Identify Data

Define business goals and choose what datasets are necessary to accomplish said goals. Figure out where your source data lies and the systems involved.

2. Set Data Access

How will data be obtained? This depends on what format the data is in. It will also involve the stakeholders who currently oversee the data to help define the situation.

3. Select Tools

At this stage, you’ll want to find the data processing software that’s right for your business. It helps to select a solution that is easy-to-use, collaborative, scalable, and secure. SolveXia hits all of these checkmarks, and more.

4. Define Operations

Define the format the data is currently in and what output it will need to be transformed into for use.

5. Develop the Process

Test out the software to ensure that the ETL process runs smoothly.

6. Schedule and Review

Schedule your automation processes, and define the frequency at which the processing will occur. Analyze and review how the system is working to ensure it’s producing your desired results.

What are the Types of Automated Data Processing?

Data automation techniques exist across the board, including:

1. Data Integration

Data integration involves viewing data in a unified manner. This means that data is collected from various data and transformed to be stored in a target repository.

2. Data Transformation

This involves formatting data into a usable format for analysis, which tends to involve data wrangling. Automated data transformation saves a ton of time, allowing your team to spend more time on the analysis aspect of the procedure.

3. Data Loading

Data loading is much like loading pallets into a container. Once everything is neatly packed and stored in the right format, the data can be loaded into the data warehouse, where it can be stored securely and accessed for use.

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For a deeper dive into the technicalities of data processing and the steps involved, check out this guide.

What are Best Practices to Automate Data Processing?

The goal to automate data processing is a big and important one, which requires adequate planning and a clear outlook.

To be successful in your endeavor, we’re going to share some expert tips:

• Be clear about your goals and objectives from the get-go
• Identify the data sources ripe for automation
• Take time to research software that can help and be intentional about your chosen solution
• Involve stakeholders so everyone is aligned
• Implement solutions slowly and carefully - test as you go
• Document processes clearly so everyone is clear about their responsibilities
• Continuously monitor the outputs, and be open to continuous improvement

Wrap Up

When you automate data processing, you’re able to save time, boost productivity, reduce errors, and most notably, trust your data and insights with utmost confidence.

With this amount of data integrity and accuracy at your fingertips, you can make informed decisions at any given moment in time, maintaining a competitive edge and bettering both the employee and customer experience along the way.

Interested in seeing how SolveXia works in action? Request a demo today to see how having access to process automation, real-time analytics and customizable dashboards can help you see your business more clearly.