Everything You Need To Know About Automated Data Processing

In centuries and decades past, businesses needed someone to manually compile and handle all data. As you might imagine, this process was very time-consuming and prone to errors that threatened the quality of the dataset and the Business Intelligence (BI) insights gained from it. 

Today, businesses have a better option: automated data processing. This guide will cover automated data processing and how your business can leverage it for cost and time-efficient data handling. 

What is automated data processing?

Automated Data Processing (ADP) refers to the interaction of the processes, procedures, methods, people, equipment, and tools used to perform operations on data. Some IT personnel refer to it as “Automatic Data Processing,” and there is no difference between the two terms. 

It’s vital to note that all data processing is not automated. Some data processing is manual, even if it’s performed on a computer. 

If a person is analyzing, organizing, storing, retrieving, or manipulating data themselves, it’s manual data processing. For example, a store manager who uses Microsoft Excel to calculate weekly sales averages is performing manual data processing. 

If a device is processing data without constant human oversight, it’s automated data processing. For example, it’s automated data processing when a BI system tallies weekly sales performance figures and displays them in graphs on a dashboard. 

Today, automated data processing usually uses computers, but it didn’t start this way. One of the first automated data processing systems was built by Herman Hollerith for the 1890 U.S. census. Hollerith’s system punched holes into data cards that mechanical card readers in the census office could interpret. These card readers then compiled and organized data. 

The automated data processing systems your business can use today have better functionality than Hollerith’s system. They can sort, contextualize, and interpret data, as well as derive insights by identifying trends and outliers. 

This functionality is crucial — organizations today collect large amounts of data on marketing efforts, financial performance, sales performance, human resources projects, their workforce, product research, etc. 

The quantity of data organizations collect is currently growing quickly. Data from Statista shows that 6.5 zettabytes of data worldwide were created, captured, copied, and consumed in 2012. Ten years later, Statista believes this figure has grown to 97 zettabytes.

Source : Statista

Considering organizations are swamped with data, automated data systems are vital for any organization looking to develop a BI strategy. These systems can help process large volumes of data (including big data) with minimal human interaction. 

Accurate data analysis is also key for sound decision-making. If you misinterpret or omit key data insights, you won’t have the knowledge you need to make an accurate, data-informed choice.  

In a real-life situation, poor data analysis could lead executives to pursue the wrong market for a new product, marketers to use the wrong incentives, and a business to miss a new industry trend that would have been very lucrative. 

Why use automated data processing software? 

By 2024, the U.S. data processing, handling, and hosting industry should bring in $197.8 billion in revenue. Data automation and processing systems have grown in popularity because they help organizations handle data smoothly. Specifically, they:

Improve efficiency 

Manual data analysis is extremely time-consuming and, thus, inefficient and expensive. When people need to dedicate hours to importing, cleaning, and sorting data, they can’t focus on other revenue-adding tasks. 

Some tools (like calculators and spreadsheets with formulas) speed up the data analysis process. But it’s still slow. Think of how long it takes you to create a pie chart with 100+ entries in heavy and complex BI tools like Power BI. Now imagine the success of your organization depends on you doing it hourly. 

Automated data processing systems are efficient because they perform repetitive tasks (like importing or tallying data) at scale. Thus, they can process large amounts of data very quickly. The insights you get from them are also up to date, as can they analyze data in real-time. 

Additionally, they simplify your organization’s data management process.

This is exactly what an end-to-end data platform like ClicData allows you to do. It takes you from this

without clicdata pic

… to this:

Avoid data silos

Data silos are collections of data that are unreachable to others in your organization because the data lives in an inaccessible location. 

For example, imagine that your marketing department uses an emailing software and analytics tool, your sales department uses a Customer Relationship Management (CRM) tool, and your finance department uses its own software. Strategic reporting requires your organization to use data from all three sources. 

But, because these systems are not connected, data from each department is inaccessible to other departments and management without hours of effort. If retrieving data will take that much time, it’s effectively impossible (time = money), and the data is stuck. 

Data that’s stuck inside silos is unhealthy data. Silos prevent people from using data, ultimately wasting resources, discouraging collaboration, and limiting the BI insights you can get from it. Additionally, data in different silos is often inconsistent and incompatible. Normally comparable data might have different data types, for example, or a data point in one silo may have an update that a data point in another doesn’t.

Data silos generally have one of three causes:

  1. Deliberate design. They are the result of old processes put into place when departments didn’t need to use each other’s data.
  2. Organizational culture. They result from each department using field-specific language and data management techniques. Alternatively, cross-department collaboration isn’t a part of your company culture.
  3. Technology. They result from departments using tools without data sharing options or an Application Programming Interface (API). 

Switching to an automated data processing system addresses all three issues. These systems automatically retrieve data from each department and centralize it in a data warehouse accessible to everyone. 

Improve integrity and security of data 

Humans are inherently flawed, and no one (no matter how qualified or educated) is above making a mistake. Data automation reduces the human intervention in data analysis — removing the chance for people to mistype, misread, or confuse data points. 

Additionally, BI requires a lot of data manipulation, so there are many points for potential errors. People could merge data incorrectly, for example, or integrate the wrong data points. Someone could even use the wrong data source in a visualization without realizing it. 

Some threats to data integrity are not accidental. Data that’s insecurely stored can be stolen with disastrous results. According to IBM’s Cost of a Data Breach Report 2021, the average data breach last year cost $4.24 million (up from an average of $3.86 million the year before). 

Automated data processing reduces the opportunity for error or data breaches by bad actors. Thus, it will help preserve your data’s integrity.

Automated data processing techniques you can use 

So far, we’ve covered automated data processing techniques quite broadly. Here are five automated data processing techniques and when you might use each of them:

  • Batch processing. Batch processing is when your system processes homogeneous data points in batches. There are three types: simultaneous (you process each batch at one time), sequential (you process each batch one by one), and concurrent (you process batches with an overlap). People typically use batch processing for financial data, Protected Health Information (PHI), or other highly confidential data.
  • Real-time processing. Real-time processing is when your system processes data so quickly it’s almost instant. People use real-time processing to handle data that they need insights from constantly, like sales performance data or location tracking data from field employees. 
  • Distributed Data Processing (DDP). DDP is when you break your datasets into sections to process them with different devices. People who want to analyze large datasets very quickly use DDP, as do those wanting to save money or reduce the risk of a natural disaster interrupting data processing by taking devices offline. 
  • Multi-processing. Multi-processing is when you use several processors from one system on one dataset simultaneously. People use multi-processing to handle very large datasets.

Time-sharing. Time-sharing is when many people interact with a single processor at once. The processor gives each user a “time slot,” and it processes each slot sequentially on a first-come-first-serve basis. People input a query, wait, and then get a response. Time-sharing is a very cost-effective processing technique, and people use it for queries that aren’t time-sensitive. 

If you have unlimited time and money, you can build your own automated data processing tool around the techniques that suit your business. Or, you can use a state-of-the-art platform like ClicData. 

How ClicData can help you with automated data processing 

ClicData can help you implement an automated data processing strategy into your organization quickly and easily. 

ClicData’s complete BI solution allows you to quickly collect, store, organize, retrieve, and manipulate data. From collecting raw data to building visualizations that make people say “wow,” ClicData offers all the features you need for your BI strategy. 

You can use ClicData for:

Connecting and up-dating data 

ClicData has over 250 data integration connectors. This includes connectors for business applications like Zero, QuickBooks, and Salesforce, file storage programs like Google Drive, OneDrive, and Dropbox, and databases like MySQL and Google Big Query. ClicData even integrates with social media platforms. 

Once the connection is done, all your data are automatically updated.

Storing data

ClicData has a powerful and secure data warehouse. ClicData users can bring all their organizational data into a single location to ensure data quality, consistency, and ease of reporting. 

With ClicData, you can centralize, combine, and clean data in a single place. 

Transforming data 

ClicData has a robust ETL tool that can help you clean, merge, combine, and transform data. With ClicData’s ETL, you can ensure your data is always in perfect shape for consumption. 

To see what ClicData can do for your organization, test ClicData with a 15-day free trial. The trial covers every ClicData feature and gives you a complete picture of the platform. 

Start your free trial here.

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