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What is Data Automation and Why is it Important?
Types of software development

What is Data Automation and Why is it Important?

Data automation is a process of automatically collecting and processing data to minimise manual errors and increase productivity. This reduced human intervention helps businesses save time and improve efficiency.

Whether dealing with customer records or invoices, intelligent automation improves decision-making, boosts productivity, and enables organisations to cope with growing data demands by bringing order to data management.

Key takeaways
  • Data automation reduces manual work, saves time, lowers operational costs, and allows teams to focus on higher-value tasks.
  • Data entry automation minimises human errors, leading to more reliable data and fewer costly mistakes.
  • The future of data automation will be driven by hyperautomation, AI, natural language processing, integration with IoT, and enhanced compliance tools.

Benefits of data automation

Time and cost savings

Data automation helps save time and money by reducing the need for manual data entry processes. Instead of dedicating hours to data entry, teams can focus on more strategic and value-driven activities. With less time spent on repetitive tasks, teams can be more creative, solve complex problems, and contribute to the company's growth. Automating routine tasks also reduces operational costs, especially for tasks that otherwise require extensive human involvement, such as processing orders or managing the entire process from start to finish. A clear data strategy is key to making the most of automation and using data effectively across the business.

Improved accuracy and reliability

Data automation reduces the likelihood of human error, leading to fewer costly mistakes. By removing manual input, the risk of errors that could result in financial losses or damage to reputation is minimised.

Increased efficiency and scalability

As businesses grow, the amount of data they handle grows too. Data entry automation software makes managing large volumes of data easier and allows scaling operations without adding extra resources for manual data processing. It also improves overall workflow efficiency, as systems can process data faster than humans, allowing them to handle a larger customer base or greater product volume without compromising performance.

Enhanced security

Data entry automation also reduces human touchpoints and limits access to sensitive data, enhancing compliance and security. With fewer people handling the data manually, the risk of data breaches or errors is lower. Additionally, data automation ensures security and compliance with data protection regulations, such as GDPR or HIPAA. By automating security protocols and compliance checks, businesses reduce the risk of non-compliance penalties or data misuse.

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Types of data automation software 

Data automation software is designed to optimise data-related tasks. These tools come in various forms, such as cloud-based, on-premise, and hybrid solutions, each offering distinct key features and benefits.

Cloud-based data automation
On-premise data automation
Hybrid data automation

Cloud-based data automation

Cloud-based data entry automation tools are hosted on remote servers and accessible through the internet. They offer flexibility in managing data workflows from anywhere. With cloud migration services, companies can easily adjust their usage based on demand without worrying about hardware constraints.

On-premise data automation

On-premise data entry automation software is installed and managed on a company 's own servers. This type of data entry software is ideal for projects with strict data security requirements, as they maintain full control over their data. However, it often requires a higher upfront investment in hardware and IT infrastructure.

Hybrid data automation

Hybrid data entry automation solutions combine the benefits of both cloud-based and on-premise tools, allowing management of sensitive data on-site while taking advantage of the scalability and flexibility of the cloud for other tasks.

Popular examples of data automation tools

1. Zapier

Zapier is a no-code tool that connects various apps and automates workflows between them. Users can create custom workflows (known as "Zaps") to trigger specific actions in different applications, eliminating the need for manual intervention or coding knowledge.

These tools and solutions make data automation easier, faster, and more efficient, helping to optimise operations, enhance productivity, and gain valuable insights.

Whether you choose a cloud-based, on-premise, or hybrid solution, the right data automation tool can transform the way your company handles data.

2. Microsoft Power Automate
3. Power BI
4. Amazon Redshift

These tools and solutions make data automation easier, faster, and more efficient, helping to optimise operations, enhance productivity, and gain valuable insights.

Whether you choose a cloud-based, on-premise, or hybrid solution, the right data automation tool can transform the way your company handles data.

AI-powered automation

AI-powered automation is designed to handle very complex tasks as it processes data, identifies patterns, and adapts to changes. One significant advantage of AI-powered automation is its ability to process large amounts of real-time data, which helps predict outcomes and offers useful insights.

For example, AI-powered intelligent automation can analyse data on customer behaviour to provide personalised experiences and detect process errors. It even is able to forecast market trends to support business strategies. Traditional automation is limited to repetitive tasks, but AI allows for interpreting language, recognising images, and understanding context in decision-making.

The process of implementing data automation solutions

Companies should assess their current data entry systems and identify areas for improvement. They should also consider the pricing and limitations of different data entry tool options. Here's how to implement data automation effectively:

  1. Identify problems: Determine what areas are causing problems, bottlenecks or frustration in your workflows. Look for tasks and processes that could be improved with automation.
  2. Prioritise needs: Focus on automating the most important and time-consuming processes first. Tackling your biggest challenges early will ensure maximum impact.
  3. Evaluate your current data system: Take a close look at your data sources—such as spreadsheets, CRMs, or databases—and remove unnecessary ones. Check for data inconsistencies and errors. Understand data volume and frequency to help prioritise automation.
  4. Set clear goals: Next, set clear automation goals such as reducing processing time, improving accuracy, or enabling real-time analysis. Define measurable outcomes, like a 20% increase in efficiency, and involve stakeholders from different departments to align automation with business needs.
  5. Choose the right tool: Work with your team to determine the features you need in an automated data processing tool. Select a tool that integrates well with your systems, fits your budget, and can scale as needed.
  6. Provide training: Ensure everyone who uses the automated data entry systems receives training. Clear instructions and hands-on practice will help employees understand the new processes, improve team collaboration and ease the learning curve.
  7. Review and optimise: Assign team members to review automated processes regularly. Frequent reviews help keep processes efficient, reliable, and adaptable to any changes in organisational needs.
  8. Monitor and maintain: Continuously track performance metrics like error rates and data quality. Use automated reports to gain real-time insights and adjust processes as needed.

Real-world applications of data automation

Businesses of all sizes, from small startups to large corporations, benefit from data entry automation.

One key application is intelligent document processing. Automated systems can efficiently scan, extract, and input data, streamlining tasks such as patient record digitisation and insurance claims in healthcare. This improves service quality and reduces manual workload. In finance, automation speeds up invoice processing and reconciliation, ensuring accurate, timely payments. E-commerce businesses use it to manage inventory, update product listings, and process orders more efficiently.

Data entry software simplifies data input and transfer between systems and in this way optimises workflow management. Its flexibility allows it to meet a wide range of organisational needs.

Let us explore two case studies by ELEKS in the logistics and healthcare industries. 

How Wasteer uses data automation to improve waste management

Wasteer, a German sustainable waste management solutions provider, worked with ELEKS to develop a data-driven app that optimises waste-to-energy plant operations. The app streamlines waste management by visualising real-time energy production and costs while recommending strategies to improve efficiency and reduce CO₂ emissions. ELEKS’ expertise in data automation helped Wasteer transform its concept into a functional prototype, driving both environmental and operational improvements in the industry.

Case study
Helping Wasteer innovate the waste industry with a cutting-edge waste management app
wasteer

Enhancing customer support with AI-powered knowledge management

ELEKS collaborated with Microsoft to develop an AI-powered knowledge management solution for customer support. Using the Microsoft Copilot Agent, the solution integrates with Microsoft Teams and Atlassian tools to enhance issue categorisation, speed up root cause analysis, and simplify access to past resolutions. This AI-driven system reduced time spent on root cause analysis by 20%, boosted productivity, and improved knowledge sharing.

Clients Case Studies
Enhancing Customer Support Efficiency with AI-Powered Knowledge Management

The future of data automation

The future of data automation is promising, driven by advancements in AI and machine learning. These technologies make automation smarter, allowing it to handle complex data processes with minimal human intervention.

AI-powered data extraction tools, such as OCR (Optical Character Recognition), are already being adopted to efficiently convert paper documents into digital formats. For example, Google's Document AI automates structured data extraction from unstructured documents which lets developers create high-accuracy processors to extract, classify, and split documents.

Furthermore, automation tools integrate with cloud-based platforms, allowing seamless data management across different departments. Companies like Microsoft and IBM invest in AI-driven automation tools that predict trends and detect anomalies.

The future of data entry automation will be influenced by a few key trends:

1. Hyperautomation

Hyperautomation refers to the idea of automating all possible tasks within an organisation. Hyperautomation refers to the idea of automating all possible tasks within an organisation. Companies that embrace hyperautomation aim to improve efficiency across their operations by using technologies like AI, RPA, and machine learning with minimal human involvement.

2. Natural language processing

In addition, NLP will play a critical role in automating the extraction of insights from vast amounts of unstructured data, such as emails, social media content, and customer reviews. This capability will provide deeper insights into customer sentiments, market trends, and emerging opportunities.

3. Integration with IoT

As IoT devices become more integrated, they will generate real-time data that can be processed and analysed immediately. Data entry applications will work directly with IoT solutions, improving decision-making and allowing quick responses to changes.

4. Enhanced data governance and compliance automation

As concerns about data privacy and rules like GDPR grow, automation tools will help companies monitor, report, and follow the required compliance standards so they can stay compliant without needing to do it all manually.

Maximising business efficiency with data automation

Businesses today generate massive amounts of data from sales, marketing, customer interactions, and more. Managing this data manually is not only time-consuming but also prone to errors. As data continues to grow, organisations need data entry software and automated data entry solutions to handle it efficiently through document processing and extract data capabilities.

With data automation, data collection, cleaning, and processing can be streamlined, resulting in improved accuracy and efficiency. Data entry applications offer key features like the ability to scrape data, manage workflows, and manage users, making data entry automation a game-changer for the entire organisation. These applications help streamline their data management tasks and increase operational efficiency.

Moreover, automated data management allows human resources to be allocated to higher-value and strategic tasks. By leveraging machine learning and artificial intelligence, businesses uncover hidden patterns and trends in their data, enabling faster and more informed decision-making. Companies that adopt data automation today will be better positioned to thrive in an increasingly competitive, data-driven future.

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FAQs

What are the four 4 types of automation?

The four types are fixed, programmable, flexible, and cognitive automation. Fixed handles repetitive tasks like assembly lines. Programmable can be reconfigured for different tasks. Flexible adjusts quickly for various products, and cognitive uses AI for decision-making, such as chatbots.

What is an example of automated data processing?
What is ETL automation?
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