Data Process Automation: Tips to Transform Your Enterprise

Data Analysis
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Over 40% of employees report spending at least a quarter of their week handling repetitive, manual tasks. That time could be better spent on high-level tasks, but the influx of data isn’t going to stop. To continue gaining insights from data, organisations are using data process automation to manage their manual tasks and execute business processes.

In this article, we’ll explore all there is to know about implementing a data automation strategy and consider some of the best data automation tools in the market.

Coming Up

1. What is Data Automation?

2. What are the Elements of Data Automation?

3. What are Data Automation Examples?

4. What are the Benefits of Data Automation?

5. How to Start with Data Automation?

6. What Data Should be Automated?

7. What are the Steps for Data Automation?

8. What is the Future of Data Automation Tools?

9. Who Owns Data Process Automation?

10. How to Automate Reporting?

11. Closing Words

What is Data Automation?

Data automation is the process of uploading, managing, and processing data with automated technologies as opposed to doing so manually. Automated technologies include the application of artificial intelligence (AI), infrastructure, and software (such as SolveXia) that is able to collect, store and analyse data.

The best data automation tools are able to remove the need for human intervention so that your staff can spend their time on analytical and strategic endeavours rather than parsing data. That’s not to say that data automation makes humans obsolete. Rather, automation software like SolveXia complements your team so that repetitive tasks are handled and the professionals can utilise the insights to make better informed decisions.

What are the Elements of Data Automation?

Regardless of the data automation tools you select to use, the common elements of data automation include ETL, or extract, transform, and load.

To explain what that means:

1. Extract - Pulling data from a single or multiple source

2. Transform - Formatting the data to meet the requirements of the destination system by applying functions such as filter, sort, etc.

3. Load - Inputting the data into the target system, like a data warehouse or database, which allows it to be used and analysed for insights

After ETL is taken care of, the final step of data automation is applying to various use cases, which comes down to conducting analysis. Data automation tools like SolveXia are able to perform advanced analytics that can help for forecasting, budgeting, and understanding trends. Data appears as visualisations in customisable dashboards and automated reports.

What are Data Automation Examples?

Any industry can benefit from the use of data automation as every business relies on data in one way or another. Some data automation examples can be seen in:

  • Accounts Payable: Data automation software can transform paper documents and invoices digitally so that everything can be uploaded into a secure and centralised system without mistakes to streamline processing.
  • Customer service: Data automation tools, like chatbots, can provide customer support 24/7 to recognise and resolve customer needs, even when humans are unavailable to do so.

What are the Benefits of Data Automation?

With so much of the workforce spending too much time manually handling repetitive and meticulous tasks, data automation provides a multitude of benefits for businesses of all sizes.

These benefits include:

1. Risk Reduction

Without the chance of manual errors, companies are able to reduce their risks across the board. This is especially true when it comes to financial or sensitive customer data that must adhere to strict regulations.

2. Enhanced Accuracy

Data automation tools can help to sort through unstructured and structured data to remove inaccuracies, missing data, and duplicate data so you can rest assured that you are using the best data to perform analysis.

3. Time Savings

One of the primary and most noticeable advantages of data automation can be seen with the time your team saves. Rather than having to collect data across sources and use disparate spreadsheets to sort through data to execute processes, data automation software does it for you, more accurately and faster.

4. Lower Costs

Data automation can help save on operational costs as you’ll reduce errors and free up your team’s opportunity cost to be spent on valuable tasks rather than resolving bugs and manually executing queries.

How to Start with Data Automation?

Getting started with data automation can be very simple. It just requires some focus and consideration before jumping in.

To determine whether or not your desired project, process, or procedure is suitable for data automation, answer these questions:

  1. Is the data volume high?
  2. Does the data come from multiple sources?
  3. Does the data necessitate frequent updates?
  4. Does the data need to be reformatted before processing or uploading?

Any data that fulfils these needs would be a great candidate for data automation. Once you implement data process automation, you’ll quickly see the benefits in action.

If you choose to implement a data automation solution like SolveXia, you can get up-and-running quickly as the software offers a low-code/no-code solution.

This means that you won’t require any specialised IT team or knowledge of code to get started, but rather the software provides drag-and-drop functionality to start automation in seconds.

What Data Should be Automated?

In theory, the more you automate, the more you’ll realise the advantages of doing so. When you utilise data process automation, you will require less resources and be able to maintain high data quality over time.

The following can serve as a checklist to determine the criteria for eligibility for automation.

  • The process takes a non-trivial amount of time to complete manually (i.e. more than a few hours per month)
  • You use multiple data files as inputs, outputs, or interim files
  • The data comes from separate legacy systems
  • You run the process frequently (i.e. at least monthly or more)
  • There’s a high cost of error associated
  • You rely on a specific key person to execute the process, and without them, the output is at risk of delay or error
  • There are many steps involved in the process

Read more on how to identify processes for automation here.

What are the Steps for Data Automation?

To get started with data process automation, follow these steps:

1. Identify Problems

Start by narrowing in on which core areas could benefit from automation. To get the most from data automation, pinpoint the processes that are the most time-consuming. Make a list of processes that are ripe for improvement.

2. Categorise Data

Categorise and sort data based on its accessibility and importance. Figure out which source systems you have access to so you can find the right data automation tool that can extract from it.

3. Prioritise Operations

With your list of processes outlined, put them in order of priority based on those that take the most time. Start with these because your team will immediately recognise the benefits of data automation and be more likely and enthusiastic to use it.

4. Identify Transformations

Outline what transformations are required to move the data from its source to its target. This is a crucial step so that your database is protected once you start automating.

5. Select a Tool and Execute

Choose your data automation tool that suits your needs and budget and get started!

6. Schedule Data for Updates

Don’t forget to keep your data up-to-date. You can do this by choosing a software tool that performs these capabilities for you, without the need for human intervention.

What is the Future of Data Automation Tools?

The high demand for data automation tools is prompting the top automation companies to evolve their offerings rapidly to best serve customers like you.

Concepts like automated feature engineering are beginning to take shape, which extracts features from raw data using data mining techniques. This can solve many data science projects with real-world data sets, thanks to the acceleration of machine learning.

Who Owns Data Process Automation?

As an organisation that currently manually processes data, you may be reliant upon highly-skilled and trained professionals to carry out the process. The risk here is that if a certain person is absent or out sick, you’re suffering from key person dependencies that can delay timely processes.

There are different data automation ownership setups that you currently may use, including:

  • Centralised: Central IT organisations own the entire ETL process.
  • Hybrid: Individual departments will extract and transform the data and then the central IT organisation or team will be responsible for the loading process.
  • Decentralised: Every department will own their own ETL process.

When you implement a solution like SolveXia, you’ll remove the risk of key person dependency. Anyone who is granted access to the centralised system can execute processes or initiate the automation sequence.

How to Automate Reporting?

Once you have processed data, you’ll want to use it for insights and agile decision-making practices. However, without automation, your data analysts are stuck spending most of their time creating reports and visualisations to make use of the data.

Rather than being bogged down with report generation, you can utilise tools like SolveXia that automate reports. With automated report generation, relevant stakeholders will receive updates and finished reports directly without having to do anything. You can also connect SolveXia to integrate with your existing systems and tools via APIs to get the most out of your existing toolstack.  

Closing Words

Data process automation can be a game changer for any business in any industry. With the growing volume of data and its use cases, data process automation tools like SolveXia can save your business time, money, improve accuracy and alleviate the strenuous tasks that are holding back your team from being to get more done.


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