Make your work easier with automated data processing (ADP)

Stuck manually updating data for that Excel report? There is a better way to spend your time at the office. 

Automated data processing can do the heavy-lifting for you and liberate your schedule for more productive and creative work.

What is automated data processing (ADP)?

Automatic data processing (ADP) refers to any use of technology that automatically performs data tasks with no to minimal human interaction.

ADP can take care of:

1. Data collection. Extract data from raw data sources without you needing to lift a finger.
2. Movement of data. Send data from one storage to another. Email clients with a CSV report automatically attached. Send data to be deleted once it is not needed anymore.
3. Data transformation. Clean your data automatically. Remove outliers, reshape the data format, beautify the format of data for reporting, construct metrics, and any other transformation.
4. Data organization. Organize and structure your data where it’s saved. This can be anything from automatically saving a report in the right folder, to properly integrating data into your data warehouse or other database management system so the data organization is compliant with your data storage schema.
5. Data analysis. Automatically collect, transform, and load data into business intelligence (BI) tools to construct metrics and dashboards without any manual processes.
6. Decision-making. Use automated machine learning to make decisions without a human in the loop. From fraud detection algorithms that sound the alarm when someone is trying to trick your business to recommender systems that upsell your customers without you needing to pick up the phone.

But do you need automated data processing?

How do you know if you need automated data processing (ADP)?

To understand if you need specialized data automation tools, take an inventory of all your business processes. 

Time for a mini-challenge!

For each business process, identify what data workloads form part of the process. Then ask yourself:

1. How do we collect data for that workflow? (Data collection)
2. How do we transform the data sets before they are ready to use? (Data transformation)
3. What do we do with data once the business process is over? (Data loading)

Let’s look at a complete example. Let’s say you are preparing your end-of-quarter sales report.

1. Data collection - you open the CRM, pick the view showing client emails by acquisition date, and use manual data entry to copy and paste all the new clients’ emails into an Excel spreadsheet.
2. Data transformation - you remove the client emails that belong to companies that signed a contract before the current quarter. 
3. Data loading - you save the Excel report and send it as an attachment to your boss. Then you move the Excel file into a folder on your desktop called “Quarterly Reports”.

All of the steps above take time and are not fun to make. This process (and others that you have identified with this mini-challenge) can easily be automated with ADP. 

But why would you automate business processes with ADP?

What are the benefits of automated data processing (ADP)?

There are 5 main advantages to using ADP for your company.

ADP benefit #1: Save time

Data automation saves a lot of time. 

Manual data entry and data processing eat away hours of your most valuable resource: time. 

But manual processing is not just time-consuming. It also comes at an opportunity cost. Every minute spent on repetitive, mind-numbing, and soul-crushing tasks eats away from the time your workers could be working on productive, creative, and revenue-generating tasks.

Automatic data processing systems win back the wasted time and give you and your workers time to focus on what matters: growing your business. 

ADP benefit #2: Decrease human errors

To err is human. And when working with data, we tend to make errors. Sometimes we are tired, sometimes we are distracted, and sometimes our head is just not into it.

Machines make fewer errors. 

That is not to say, they never make a mistake. But the mistake is a predictable consequence of wrong programming and can be quickly detected (more on this below), and amended, so fewer expensive mistakes happen in the future. 

ADP benefit #3: Validate data quality

ADP can be used to automate the evaluation of your data integrity and quality. If there is something wrong with data, you can either send an automated message to a human so they can check it, or decide to discard data because it is corrupted.

Implementing automated data quality checks increases the overall data integrity and it also increases trust in data for better decision-making. 

ADP benefit #4: Automate data management

Where there is data, there is data management. 

Data compliance laws change all the time. The regulation surrounding what we do with data has been increasing in its demands. 

From stricter privacy rules to higher fines for violating data regulations, it is hard to keep track of what your workers are doing with data and if they follow data regulations. 

If you rely on manual processes, you can quickly make mistakes that can put a dent in your bottom line (fines) or even prevent you from working (personal data breaches can allow authorities to seize your assets).

Data automation tools can make sure you collect, transform, save, and move data following regulatory compliance.

ADP benefit #5: Get a competitive advantage

Big data and machine learning can help you stay ahead of your competition. By using artificial intelligence to automate your reporting, anomaly detection, and recommender systems, you can build products with data that have an edge over your competitors.

How to unlock all these advantages of ADP?

Use the right data automation tools for ADP

To get ADP benefits, use data automation tools. These software solutions can be found under different search criteria:

1. ETL tools for automating data collection, transformation, movement, and storage. Check an example list of the best ETL tools here.
2. Data governance tools for automating data management and security. For example, this list compares the best data management tools.
3. Tools for the other areas of automation (data quality, data analytics, machine learning, …).

Or pick a single tool that does all the above and more, like Keboola.

Keboola is a Data Stack and a Service platform that joins all the functionality above in a single user-friendly solution:

Automatically collect data from hundreds of sources with Keboola’s Extractors.

1. Transform the data into the shape and form you desire using R, Python, or SQL. Keboola helps you automate any data transformation process: clean data, aggregate it, compute metrics, remove outliers, and many more. 
2. Send your data to multiple destinations: databases, data warehouses, single files, and data lakes.
3. Build ETL (extract-transform-load) data pipelines end-to-end and automate them with Keboola’s Orchestrations.
4. Automatically take care of data governance and security. Every touchpoint is recorded in Keboola, to make compliance smooth.
5. Build machine learning algorithms with data science tools.

And multiple other features! 

‍Try Keboola yourself, it is free (no credit card required). 

Or get inspired by data automation stories, like How you can automate marketing reports, How we automated personalized communication with clients at scale, or How we automated invoicing.

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