What is data automation?
Data automation is the use of software tools and infrastructure to streamline data management tasks. Data automation techniques intentionally limit human interaction with data-related jobs. Done properly, automation frees human talent, such as data scientists, from repetitive, time-consuming tasks. Moreover, automation executes data management tasks with greater accuracy and efficiency than manual efforts.
Data management tasks are far-ranging and include the following:
- Data collection. Locating and ingesting data from varied sources, data collection taps existing corporate data stores or accesses third-party data resources.
- Data processing. Transforming, normalizing, or performing other actions on data resources, data processing ensures that data is properly organized and formatted.
- Data quality. Reviewing data resources to ensure accurate, complete, timely and valid data is foundational to any data quality initiative.
- Data integration. Combining data resources from various sources, data integration produces a larger, more complete data environment.
- Data analysis. Performing mathematical analytics on data resources, data analysis answers questions, makes determinations, trains machine learning (ML) models or serves other practical uses.
Modern businesses routinely obtain enormous amounts of data through regular business operations, such as collecting customer data and maintaining transaction records. Third-party sources, including government research and other businesses, also provide data.
However, simply having data means nothing unless the data is complete, accurate and properly formatted. Perhaps the most common operating model for data automation is extract, transform, load (ETL), which enables an organization to extract data from its source(s), transform it into a complete, consistent format, then load it into a target database or other application for use, such as an analytics application. Some data automation use cases employ an ELT approach instead, transforming data in its target application rather than before loading, as ETL does.
Several types of data automation are used across industries. Robotic process automation, for example, handles specific repetitive tasks using software agents such as robots. Data integration, fundamental to business success, is often approached as a dedicated automation type. ML also demands high volumes of well-integrated, high-quality data to train its models.
How does data automation work?
Data automation represents a pipeline. Its purpose is to collect data, often from multiple sources, process the data into a complete, useful format, then deliver the data to tools or databases that require it. This data automation pipeline typically comprises numerous software tools or platforms that conduct the ETL -- or ELT -- process, or some variation.
Extract
Data must be collected from at least one source, but often it comes from varied sources. Common data extraction sources include corporate or government databases, web applications of online retailers, application programming interfaces and internet of things (IoT) device arrays. While data comes from many sources, it serves no practical purpose until all data is prepared properly.
Transform
Data transformation includes countless potential processes intended to ensure extracted data is accurate, complete, timely, valid, enriched if needed and properly formatted. This is the core of all data quality efforts.
For example, transformation deletes duplicate data entries. As another example, if data includes temperature or distance measurements, transformation ensures all fields are filled in and completed using uniform measurements, such as degrees Celsius or kilometers. If timeliness is important, transformation removes data entries older than a desired date. Transformation likely involves many different individual data quality checks and processes.
Evolving technologies such as ML and AI play an ever-greater role in data automation, dynamically adjusting transformation processes as business needs and data sources change.
Load
Once extracted and transformed, data must be sent to a destination for a specific purpose. A completed data set has many possible loading destinations, including a data warehouse, business intelligence tool, analytics tool or another vehicle intended for ML/AI training tasks.
Businesses prefer this traditional ETL pipeline when data is well structured, data quality is critical, and compute and storage resources are limited. Data quality is easier to achieve when transformation occurs before loading.
Still, sometimes enterprises choose the ELT pipeline, especially when flexible data requirements are present, enormous data volumes are being extracted -- perhaps from cloud data warehouses -- and compute needs are significant, requiring cloud computing support, for instance.
What are some data automation tools?
Data automation relies on software tools designed to embody and support the collection, preparation, loading and processing of data using automated mechanisms. These tools range from basic point solutions to comprehensive data automation platforms. Common tool types include ETL tools, data integration tools, workflow automation tools, data pipeline tools and data quality tools. Based on web search results, examples of popular data automation tools include, but are not limited to, the following:
- Airtable.
- Alteryx.
- Apache NiFi.
- AWS Glue.
- Azure Data Factory.
- Fivetran.
- Informatica.
- Matillion.
- Microsoft Power Automate.
- RightData.
- Stitch.
- Talend.
- UiPath.
- Zapier.
Why is data automation important?
Data automation has the same importance as any automation initiative and improves five distinct processing issues:
- Speed. By removing human delays and uncertainties, data automation renders results faster than manual data management. Faster outcomes save time and money on repetitive tasks.
- Scalability. Automation recognizes and supports enormous data volumes, enabling data automation scalability from single data sources to vast cloud-based data warehouses, all with the same fundamental software platform.
- Consistency. Applying automation technologies to data management tasks yields consistent outcomes, ensuring the same tasks are performed in the same way every time. This aids both data quality and regulatory compliance efforts.
- Accuracy. When properly introduced and validated, automation technologies eliminate errors found in manual, human-driven processes. Data automation ensures data management tasks are handled accurately. Any issues are quickly flagged and addressed.
- Resourcing. Automation frees human professionals from mundane data management jobs to conduct more productive and innovative tasks for the business.
Examples of data automation
Data automation, done properly, streamlines operations and enables an organization to make faster, more efficient and more accurate decisions, accelerating business growth. Major industry verticals already use data automation productively. Some practical examples include the following:
- Retail. A retail business employs data automation to optimize and manage inventory, personalize customer interactions and enhance supply chain efficiency. Data automation tools track inventory across many locations using point-of-sale data, as well as data collected from IoT devices. Data automation triggers reorders from suppliers when stock levels reach a threshold, meaning the seller saves time, optimizes inventory spending and reduces human error. Similarly, online sales systems analyze purchasing patterns to engage customers with personalized marketing strategies and sales recommendations.
- Manufacturing. Among its varied uses in manufacturing, data automation collects and analyzes real-time data from sensors on manufacturing equipment across the facility, producing accurate predictions of potential failures and scheduling preemptive maintenance to reduce downtime and optimize operations. Similarly, data analytics tracks inventory and aids logistics by automating orders for parts and materials from various suppliers. This means carrying fewer materials, and the manufacturer reduces inventory costs and increases needed space.
Such insights are sometimes combined with sales forecasts, and the manufacturer then brings in materials, schedules production and creates needed goods with higher confidence in revenue generation.
- Finance. Data automation enhances financial transactions as they are entered, processed and reported. It's now commonplace for accounting software to automate the entire bookkeeping process -- tracking income and expenses, reconciling bank statements and invoices, even producing timely financial reports. Similarly, the process combines and scrutinizes complex data to search for fraudulent transaction patterns or other malicious activity, bolstering both financial efficiency and security.
- Healthcare. Healthcare organizations use data automation to strengthen patient record management, consolidating patient records from different facilities and caregivers and updating them in real time. Data is then available to produce fast, accurate diagnoses and treatment plans, resulting in better patient outcomes with more cost-effective healthcare, while reducing error and oversight.
Benefits and limitations of data automation
Data automation has gained significant business attention through its proven and measurable benefits, such as the following:
- Productivity. Data automation reduces the time and effort needed during many manual data entry and management tasks. Humans instead focus on strategic data use rather than simple data entry and correction. And reducing manual interventions reduces costly human errors and saves money.
- Quality. Quality data produces better and more accurate results, while older data -- often inaccurate or incomplete -- produces poorer results. Data automation enhances data quality, ensuring accurate, complete, properly formatted and timely data for any process.
- Scalability. Large volumes of complex data overwhelm manual practices quickly, but data automation readily handles enormous volumes of unrelated and unstructured data as required. This lets the business scale data automation tasks to high levels with little increase in resource use -- time, talent or cost.
- Speed. Data automation enables faster collection and processing of large volumes of complex data, improving the speed and accuracy of subsequent business decisions. The adopting business forecasts, recognizes and responds to its changing needs faster -- a competitive edge.
Despite these significant benefits, data automation also challenges business leaders with drawbacks each must carefully consider prior to adoption. Data automation limitations include the following:
- Quality. Although data automation supports data quality initiatives, it cannot inherently ensure quality data. First, considerable human effort must address data cleansing and processing strategies to ensure automation performs any quality-related tasks successfully. These problems are compounded at scale by data integration demands when using data with different formats and origins.
- Scalability. As with quality, data automation promises significant scalability. However, the volume and sophistication of modern business data often challenges the scalability of even the most capable data automation platform. Test and validate potential scalability of any data automation tool. Ensure it is indeed capable of supporting increasingly complex data while maintaining computing performance and efficiency.
- Integration. Data automation tools and platforms need access to data -- for extraction -- and destination systems -- for loading results. This demands careful integration between data automation systems and other business systems, such as databases and computing resources. Many businesses rely on legacy systems; these meet business needs but do not necessarily integrate well, if at all, with modern data automation systems. With older systems in place, expect impaired data automation workflows requiring costly and fragile workarounds.
- Security. Security and regulatory compliance issues arise anytime a business interacts with sensitive or personally identifiable information in data storage or processing tasks. Data automation processes and workflows -- secure storage, security in flight, along with protection after processing -- must meet laws and regulations, including the General Data Protection Regulation and California Consumer Privacy Act. Data automation adoption absolutely requires input from an organization's legal, regulatory and/or security teams.
- Skills. Effective data automation employs sophisticated tools that require specialized skills from professional staff members who introduce, maintain and manage these tools. Consider the available staff and skillset. Plot a clear strategy for the team's development and training to maximize data automation tools and platforms.
