What is Data Automation? [Big Data Workflows Explained]

Remember when data automation didn't exist?

Data analysts had to manually enter every bit of information onto a CSV sheet and log every single data point through a manual data operator. Can you imagine how time-consuming and error-prone (and annoying) it used to be?

Data automation enables business systems to avoid extended durations of manual data entry. With data automation, organizations worldwide have greatly reduced their turnaround time for most of their day-to-day operations.

Sales Automation is completely changing the way salespeople do prospecting and outreach. The days of manual list building are over.

Marketing automation is also growing in leaps and bounds, with about 67% of marketing leaders using automation technologies to manage their workloads.

Data automation is about streamlining business workflows. Let's get into it.

What is data automation?

Data automation is the process by which data operations, such as data updates and importing data, are carried out automatically.

Data automation utilizes automation tools in place of human inputs.
Data automation includes a wide range of operations such as data entry, processes that otherwise require human intervention, such as RPA (robotic process automation) and BPA (business process automation), workflows that follow a fixed step of tasks, machine learning tasks, and so on.
Automating data is an integral part of modern data management systems.
It helps improve the accuracy and time spent on data operations.
Businesses use huge amounts of source data to drive their marketing and business strategizing decisions, so they benefit greatly from data automation.
Essentially, data automation optimizes data preparation and helps accelerate the data analysis process.
It helps fastens up the decision-making process, aids in making business intelligence (BI) more efficient, and helps implement cost-effective data analytics.

What types of data workflows should be automated?

Marketing workflows
Sales workflows
Human resources workflows
Finance workflows
Product & engineering workflows

A workflow refers to a series of activities or tasks to be executed in a particular order to complete an operation. Workflows usually consist of repetitive actions that are perfect for automation.

1. Marketing workflows

Marketing data can be efficiently automated to automatically capture customer data from source systems, track the lifecycle of a marketing funnel and evaluate customer behavior with ease.
You can also use automation for SEO, driving your email campaigns, subscription renewals, and so on.

2. Sales workflows

Sales workflow automation involves automating the various tasks involved in a sales process, such as to aggregate customer data, placing orders, invoices, and so on.
It allows the sales rep to focus on making a good deal rather than getting caught up with the administrative tasks involved with a sale.
It also ensures that the sales process remains consistent and can ensure a streamlined process for every sale.

3. Human resources workflows

Accelerating HR workflows involves automating routine tasks like approving leaves, scheduling shifts, and payroll management.
HR can also use data automation to quickly filter and extract potential candidate information, profile lists, and so on from applicants and job portals.

4. Finance workflows

By automating the various tasks involved in financial management, you can ensure a streamlined process that is consistent, accurate, easily trackable, and helps ensure the integrity of data at all stages of the workflows.
Some of the process workflows you can automate in finance include core bookkeeping, invoicing and accounts receivable, accounts payable, tax compliance, financial reporting, expense management, and payroll.

5. Product & engineering workflows

Engineering teams can easily set up automation to run their simulation campaigns effectively.
Employing automation in these workflows helps you optimize the time it takes to engineer the product, catch issues early on, and easily communicate the results with the respective stakeholders.

What are the top use cases for cloud-based data automation?

Streamlining customer onboarding
Reducing manual data entry
Keeping systems in sync
Powering analytical dashboards
Building data products for customers
Deepening integrations with partners

Cloud-based data automation services are used to execute the workflows in a cloud-based environment instead of a manual operator executing the same in a standalone system.

Why is cloud-based data automation strategy advantageous for a variety of business processes?

It allows you to take advantage of the enhanced security features and data compliance that come with a cloud platform.
It is also fairly resource optimized, letting you use the powerful cloud-based data engines on an affordable subscription-based model without having to deal with the high initial investments and hardware maintenance.
You get automatic updates, up-to-date patches, and efficient workflow automation.

1. Streamline customer onboarding

Cloud-based solutions are usually device agnostic and can be accessed from anywhere worldwide, given that you have the necessary authorization and access.
Thus, it provides a perfect way to make the first interaction with your customer from anywhere in the world effective and optimized.
Every step of customer onboarding can be automated with cloud services, from collecting customer data to getting them to sign up for your services.
These solutions include features such as customizable contact forms, welcome guides, automated booking, automated proposal and contract mail, welcome package mail automation, and so on.

2. Reduce manual data entry

Every step of a workflow within an organization involves some datasets.
If all of this big data were manually entered, it would not only delay the processes but also bring in more complications, such as needing more manpower, error-checking routines, and rework in case of errored entries.
Cloud-based data automation solutions can help you avoid the pitfalls of manual data entry by providing a consistent and uniform interface across the organization and ensuring you maintain consistency and data integrity in all your workflows.

3. Keep systems in sync

When multiple systems run on huge amounts of data, you need powerful cloud-based automation solutions to keep all these data synchronized and managed for consistency.
For instance, your sales data may contain customer data with outdated addresses, while the customer database might have the proper data available.
Syncing up these two systems will ensure that customer information stays consistent across both systems.
Cloud solutions help you achieve this with ease and efficiency.

4. Power analytical dashboards

Powerful data engines from cloud platforms can easily extract huge volumes of data (through API and other methods) and present them more meaningfully in analytical dashboards.
Data is only as useful as the insights you can gather from them. Simply looking at raw data might not do much in that case.
The analytical dashboards from cloud-based automation solutions can help you visualize data and easily ingest the information presented without having to do any coding or programming.

5. Build data products for customers

You can easily broaden your product lines by including data products for your clients with the help of cloud-based data automation.
Data products help ensure all consumer needs are met, and you are working on decisions backed by real-world data, not just assumptions.
Cloud solutions can help you design and implement such data products with additional machine learning and AI-based technology support.

6. Deepen integrations with partners

When you have different systems and data collection points working with multiple partners, it makes sense to use cloud-based solutions to offer a consistent interface to work with.
It makes data collection and integration with all your partners smoother and aids in maintaining a uniform format across all your data sources.

What are the top benefits of data automation?

Reduced processing time
Improved scalability & performance
Efficient use of time & talent
Enhanced customer experience

1. Reduced processing time

When dealing with a huge data management system, you will observe that data come from many different sources and formats. This often interferes with data quality.
Manual data entry, in these cases, will be a humongous task that cannot be achieved within the time frame required to make the data operations usable for the business.
Data automation helps you process huge amounts of data from multiple sources at a much reduced time, minimizes errors, reduces manual intervention, and thus helps with efficient use of resources and time.

2. Improved scalability and performance

Consider hiring two data operators to carry out your data operations and manual data entry. As your company grows, the amount of data coming in will also grow exponentially. It would be impossible to handle all the growing data with your original manpower.

But just as you hire more people, you may notice a drop in incoming data and might not need as much manpower as you initially projected.

With data automation, you can rest easy as automation can be scaled up or down as per your demand, and you can limit your manpower to only the crucial tasks that require manual intervention.
Data automation processes help improve your data management systems' overall performance and scalability.
With features like CDC (Change Data Capture), you can easily update the entire database without going through each dependent record and manually making a change.
Data analysis can also be done much more effectively as now, with automation, you can easily deal with huge amounts of data quickly.

3. Efficient use of time and talent

When you have your valuable professionals held up with repetitive tasks such as data entry and preparation, they get little time to focus on their core operations that add value to your business.
With automated data processing, your data scientists, sales reps, managers, and every other employee can focus on their core business activities rather than manually collecting and updating data. It also makes way for quick and better decisions.
Data automation will ensure they get high-quality data with no errors in real time.

4. Improved customer experience

Data automation can hugely improve all your existing operations and speed up all your internal and external operations.
This automatically means that your customers are presented with a consistent experience and get their queries resolved quickly.

What are the limitations of data automation?

Learning curve
Worker displacement
Still need some level of human intervention
Cost and security concerns

1. Learning curve

As with any new tool or process, data automation might also come with a learning curve that your employees and stakeholders need to get used to.
If people find using automation too much of an effort in itself, they will be slow to adopt the same, and you might not be able to achieve the expected results you seek from data automation.

2. Worker displacement

There could be resistance to accepting data automation tools due to fear of worker displacement.
But in reality, a data operator, if trained well, can make great use of data automation solutions and make sure the systems are properly configured.

3. Still need some level of human intervention

While data integration does offer minimal manual effort, it still might require a level of human intervention for some of the crucial tasks as part of a workflow.
For instance, approvals in a workflow process such as leave management, order placement, and so on would still need human intervention, and if the process is not followed properly, it could get stuck at these points.

4. Cost and security concerns

While data automation does provide an excellent way to reduce data operation costs, it still does bear a good deal of initial investment costs or subscription charges to implement the systems.
The cost-benefit analysis for these expenses must be carried out to figure out if data automation does indeed help your case.
Security and confidentiality concerns might also arise in the case of sensitive data, which must be handled with care when using data automation services.

Common methods for automating data integration workflows

ETL
iPaaS
Reverse ETL
Webhooks
Data sharing

1. ETL

ETL process represents the three main elements of data automation and stands for Extract, Transform, and Load.

Data from one or more sources will be extracted, transformed into the acceptable format and structure, and loaded into the data system.

However, don't confuse data pipeline with no-code ETL.

Click here to learn the difference between ETL and data pipelines.

2. iPaaS

iPaaS stands for Integration Platform as a Service, a cloud-based model that helps with data integration between one or more systems and third-party systems.

This method of data integration services provided by an iPaaS solution helps you maintain a seamlessly uniform data system across all your connected parties.

3. Reverse ETL

While ETL collects data from multiple sources and stores it in a database, reverse ETL extracts data from data warehouses and supplies it to pacific applications like CRM tools, data analytical applications, marketing tools, and so on.

4. Webhooks

Webhooks are a way to collect data from web apps.

These programmed triggers send automated messages and information from one app to another, allowing for automated data extraction and data collection.

5. Data sharing

By using data-sharing frameworks, you can easily automate data updates and sharing across multiple channels.

For instance, updating a company policy can automatically update the work calendar across multiple departments via data sharing.

What are some modern cloud-based data automation tools?

Portable

Portable helps you easily set up ETL connectors for a wide range of long-tail business applications, be it marketing, people analytics, data products, and eCommerce. It is one of the go-to data automation solutions for teams using long-tail data sources.

Portable offers the data connectors you won't get with Fivetran. You can also get custom data connectors developed for your particular use case at lightning speed.

Informatica

Informatica provides a portfolio of data virtualization tools with several data governance and analytics features. It provides both cloud-based and on-premise deployments.

This platform would suit business users looking for a comprehensive data management solution.

Talend

Talend is an easy-to-use platform with support for ETL and data transformation options. While it comes under a free plan, it has quite a limited set of features and virtually no customer support.

It is probably best suited for smaller teams looking for a comprehensive platform for easy cloud data integration and data connectors such as Hadoop, NoSQL, IoT, Spark, and more.

Dell Boomi

Dell Boomi is another data platform that allows both on-cloud and on-premise deployments. It has an easy-to-use GUI, automation, and reporting portal that requires little coding experience.

It is best suited for use cases where you must perform data operations across hybrid infrastructures.

JitterBit

JitterBit is an iPaaS platform providing integration services for both SaaS (cloud-based) and on-premise applications. It provides efficient automation features built on top of artificial intelligence (AI) technology and gives you about 300 pre-built data transformation templates to get started with.

It can be best suited for business owners that require a tool that can be used throughout the entire data life cycle.

The Bottom Line on Data Automation

Data automation is no more a luxury but a necessity for businesses. It saves you a lot of time and rules out the hassles of dealing with relational databases.

Instead, you can have easy data visualization, derive key insights, and automate tasks on the go.

Portable, a cloud based ETL solution, helps you with that. With 500+ data connectors, it facilitates easy data automation on the go.