A British mathematician– Clive Humby, coined the phrase “data is the new oil” in 2006. This analogy has proven correct over time, as data has indeed become the most valuable asset for businesses in the 21st century. It assists enterprises in deriving insights, developing strategies, and ultimately playing a critical role in their success.
However, unrefined data, just like unrefined oil, is pretty worthless. To be useful, raw data must be refined and processed to provide valuable insights that can be employed to drive results.
Nevertheless, given the massive amount of data that large enterprises generate daily, manual data analysis is practically impossible. This is why we require data automation. This guide will explain all of the key elements of data automation, including its benefits, process, and the best data automation tools.
What is Data Automation?
Data automation is the process of uploading, managing, and processing data using automated technologies. The process aims to reduce manual labor, improve result accuracy, and accelerate the process. However, to carry out data automation operations, businesses use various technologies, including automated data analytics tools, automated data integration tools, and ETL tools.
The planned and successful data automation benefits the business in a variety of ways, including predicting new target customers, workflow automation, measuring product demand, and detecting high product failures. It also extracts meaningful patterns from raw data and consolidates them into the flat “feature” table that machine learning algorithms consume.
To summarize, the data automation process provides businesses with valuable insights into their strategies, products, and customers.
What are the Phases of Data Automation?
The data automation process can be divided into three stages: Extract, Transform, and Load, abbreviated as ETL.
A. Extract: Data extraction is the process of extracting data from one or more sources. This activity of data collection must be completed successfully because it serves as the foundation for the subsequent processes, namely Transform and Load.
B. Transform: During the transform stage of data automation, a series of rules or functions, such as data cleansing, are applied to the extracted data in order to prepare it for loading into the end target.
C. Load: The load phase loads data into the ultimate destination. The ultimate destination can be anything from a delimited flat file to a data warehouse. It is worth noting that, unlike the previous two processes, the load process varies significantly from organization to organization.
What is Source Data Automation?
Source data automation is a popular data entry method that collects data in digital form at the point of origin. Eventually, this data entry method eliminates the risk of entering incorrect information into the system. Moreover, source data automation boosts the speed, accuracy, and efficiency of data-processing operations.
Because it reduces or eliminates human efforts, speeds up the process, and ensures zero errors, source data automation has become a preferred data entry technique for industries over time.
Understandably, source data automation has transformed a wide range of information-handling tasks.
For example, workers who take orders over the phone usually enter required input directly at the display workstation, so they don’t have to record the same data twice. Source data automation also speeds up checkout lines and inventory taking at supermarkets, factory quality control operations, and bank check processing.
The following are some of the most common forms of source data automation:
A. Magnetic Ink Character Recognition (MICR)
B. Optical Mark Recognition (OMR)
C. Bar Code Reader
D. Optical Character Recognition (OCR)
E. Remote Terminals
What Data Should Be Automated?
There are no hard and fast rules about what data should and should not be automated. To put it another way, all data can be automated. However, there are some tips, as listed below, that you can follow to select the best data set for automation.
If you answered yes to any of these questions, you could consider using that dataset for automation. To put it another way, any data set that requires frequent updating or processing and is larger in volume is most likely a prospect for data automation.
4 Steps To Develop A Data Automation Strategy
To ensure that you automate the right data in the right way with the right tools, you must create a well-thought-out data process automation strategy that is aligned with your objectives. Inefficient data automation strategies not only waste resources but also valuable time and effort.
Here’s a step-by-step guide to implementing your data automation strategy.
Step 1: Identify the issue and categorize the data
The first step in data automation is to determine why you require data automation in the first place. Consider all of the processes and areas in your corporation that can benefit from or be improved by data process automation. Steps To Develop A Data Automation Strategy
Likewise, determine which processes are repetitive tasks or consuming too much time and labor due to manual data processing. Next, categorize the source data based on its type and importance.
Step 2: Outline the necessary transformations
The next step is to sketch out the transformation processes needed to process the source data. The data transformation process can be as simple or as complex as you want it to be. Ultimately, it will be determined by your objective or the source data.
For example, if you only need to collect data from one source, the transformation process will be simple; however, if you need to collect data from multiple sources, the process will be more difficult.
Step 3: Design and test the ETL process
The third stage of data process automation is developing and testing the ETL process. Based on the findings of the previous two phases, choose the best ETL tool or automated data extraction tool that has all of the required features and can meet your needs.
Remember that the data automation tool you select must be able to process your data efficiently and provide you with the desired data transformation results.
Step 4: Schedule data updates
The last step in the data automation process is to schedule data updates. At this point, you must configure the ETL tool for periodic updates or job scheduling. This customization will ensure that the data transformation process occurs automatically and regularly without requiring manual interference.
These four stages constitute a data automation process. Remember that these are core steps that will be consistent across organizations. However, organizations can modify the process by adding more steps depending on their requirements.
What are the Key Benefits of Data Automation?
As one might expect, data automation has a number of benefits. For example, it reduces human errors, increases efficiency, and lowers overall costs. Let’s take a quick look at each key Benefits of Data Automation one by one.
1. Fewer human errors
Understandably, machine processing reduces errors significantly. Manually entering data into spreadsheets or systems is often a time-consuming and error-prone process. Even if the employee is well-trained, the possibility of error always exists.
On the other hand, data automation relieves employees of these time-consuming manual tasks while eliminating data entry or data collection errors. Moreover, organizations can use the time saved on other critical business activities.
2. Improved data accuracy
Data accuracy is critical when it comes to developing business strategies or understanding your strengths and weaknesses. Inaccurate data leads to erroneous predictions, ultimately wasting valuable resources, time, and effort.
However, with data automation, you can standardize the process from data sourcing to data transformation to data loading. This workflow analysis reduces the risk of errors while increasing data accuracy, resulting in better decisions and predictions.
3. Better efficiency
Another major limitation of manual data processing that data automation addresses is inefficiency. When the data volume is large and complex, processing it manually is both challenging and time-consuming.
However, with data process automation, organizations can automate the entire workflow, eliminating the need for human involvement. The ETL and automated data extraction tools will ensure that the data is processed efficiently and quickly, regardless of volume.
4. Reduced overall cost
The fourth advantage of data automation is lower costs. According to Forbes, data automation typically results in cost savings ranging from 40% to 75%. This is because it reduces workforce requirements, minimizes errors, increases efficiency, and provides greater data accuracy.
Besides that, investing in an ETL or data automation tool is a one-time investment that will quickly pay for itself. Moreover, accurate data will assist you in making accurate predictions, developing effective strategies, and increasing ROI.
Top 5 Best Data Automation Tools in 2023
The top 5 Data automation tools are listed below. Let’s look at their features, pricing, and other aspects:
1. Talend
Talend is a data integration and automate tool that assists you in delivering healthy data across your organization so that you can change the way your business makes decisions. The data automation tool combines data integration, integrity, and governance into a single, unified platform that maximizes the value of your data for your activity.
The following are some of the advantages that business users can gain from using Talend:
- Enhance the customer experience by offering personalized customer service using analytics
- Save resources by accelerating operational data
- Modernize the cloud and data for greater business efficiency
- Reduce data risk while establishing data excellence
Talend Pricing: Talend provides multiple custom pricing options based on your needs and the solution you select. To get a quote, contact their sales team.
2. Integrate.io
Integrate.io (Formerly Xplenty) is a leading low-code data warehouse integration platform that assists organizations in making informed data-driven decisions. The data automation platform enables data scientists to rapidly design and execute data pipelines with little to no code, combine data from multiple sources and send it to a single destination, and gain actionable data across all teams.
The following are some of the major advantages or features provided by Integrate.io:
- Rapid and low-code implementation
- Offers centralized data access
- Protects your data from cyber attacks
- Simple customization with the X-console
- Software integration with more than 100 native sources
Integrate.io Pricing: Integrate.io provides multiple solutions, each with a different price. Moreover, the provider also offers personalized pricing based on customized requirements. To get a quote, contact their sales team.
3. Pentaho automated data extraction tool
Pentaho is BI (Business intelligence) and data automation software that tightly synchronizes data integration and business analytics. This modern platform connects IT and business users, allowing them to easily access, visualize, and explore all data that impacts business results.
Moreover, the tool accelerates business insights and increases revenue opportunities through proven, best-practice architectures and technologies such as machine learning and AI.
Pentaho provides the following benefits:
- The all-in-one solution for data automation, reporting, and data analysis
- A highly intuitive tool with an easy-to-use interface
- Allows reporting, data mining, data integration, dashboard operation, etc.
- Easy data retrieval
Pentaho Pricing: Pentaho has a flexible pricing model. To get a customized quote and the best solution for your business, contact their sales team.
4. Improvado
Improvado is yet another top data autoiamtion tool that automates omnichannel marketing analytics and reporting, regardless of the volume or complexity of your data. The platform enables you to consolidate over 300 data sources, visualize data in any BI or analytics software, and data analysis data into your data warehouse.
All of these features will eventually assist business owners in staying ahead of their competitors through precise revenue attribution.
Moreover, this data automation tool also assists the customer success team in identifying all touch points along the customer journey and calculating CACs for cohorts of successful customers.
Improvado Pricing: The provider offers various pricing models for different organizations. Keeping your needs in mind, they also provide tailored solutions with tailored pricing.
5. Panoply
Panoply is a managed data and cloud data platform for data scientists that syncs, stores, and accesses your business data. The tool allows you to connect all your data sources without writing any complicated code and automatically store primary data in analysis-ready tables.
Furthermore, the provider has developed and provides a dedicated solution for all types of businesses, such as startups, agile teams, SaaS spend, retail & ecommerce, finance, healthcare, and so on. The data automation platform provides world-class security, 99.9% system uptime, and built-in performance monitoring.
Panoply Pricing: The Panoply has three pricing options, which are listed below:
A. Lite Plan: $499/month (50 GiB Query Bytes)
B. Basic Plan: $1199/month (1000 GiB Query Bytes)
C. Standard Plan: $1899/month (5000 GiB Query Bytes)
This brings us to the end of our blog on data automation. We are confident that the information shared here has helped you broaden your understanding of the subject and dispel any doubts you may have had.
Data Automation FAQ's
Data analytics automation is the process of automating the entire data analytics life cycle by leveraging advanced computer tools and Artificial intelligence or Machine learning -based computer techniques.
Data automation is critical because it reduces human errors, increases efficiency, improves result accuracy, and lowers overall processing costs.
Talend, Integrate.io, Pentaho, Improvado, and Panoply are some of the most popular data automation software.
The process of accelerating and automating data warehouse development cycles while maintaining quality and consistency is referred to as data warehouse automation.
Updated : August 8, 2023
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