Economists have predicted a leisurely 15-hour workweek for humans in the future. How would this be possible? Robots.
If you’re thinking that this sounds like science fiction, you wouldn’t be wrong. Robotic process automation (RPA) already manages a number of menial tasks—allowing us to explore the more cognitively stimulating aspects of our jobs
But RPA hasn’t impacted everyone (yet). A 2017 study showed that more than 40% of employees spend at least a quarter of their week on manual, repetitive tasks.
Take a moment to calculate how much time that is… spent on tasks that could be mitigated or automated entirely using RPA. This is where data automation comes in—a tried-and-tested process that offers a silver lining for the modern data scientist.
What is data automation?
When manual execution or human intervention is replaced by machines, software, and other artificial intelligence (AI) applications, it’s known as data automation. Data automation replaces manual labor in the data ecosystem. Machines and robots do manual work to ease the work done by humans.
Data automation is a collection of intelligent processes and includes the various equipment and systems needed for collecting, processing, and storing chunks of data. Automation helps data to be readily available and accessible for all team members.
Data Automation in Action
Here are some examples of how data automation looks like in the real world.
Intelligent Analysis of Human Resources
An intelligent data automation system can easily carry out critical analysis from a deep pool of employee data to find out vital patterns and outlying trends.
Currently, HR leaders worldwide use AI-powered data automation and text analysis tools like RapidMiner, Reflektive, IBM Watson Studio, Quantum Workplace, Lattice Performance Management, and more.
These tools are used to detect critical factors within a large amount of human resource data to improve employee management, employee engagement, and build on customer-centric policies.
Fortune 500 companies like Walmart, Amazon, Apple, Exxon Mobil, Netflix, and Berkshire Hathaway have already deployed AI-driven data automation software to figure out significant issues even before they arise. This intends to offer better solutions to increase overall productivity and streamline the workforce requirements with the organizational goals.
Employee Hiring Process
The workforce hiring process continues to remain one of the most challenging tasks, given its repetitive and boring nature. Even the most seasoned HR professionals need to go through thousands of applications to identify the right people for various positions. It can take days or weeks to weed out the less competent candidates, costing the business time and operating costs.
To overcome this situation, organizations are replacing their manual efforts with AI-infused automated hiring software (automated recruitment and hiring tools like Zoho Recruit, SmartRecruiters, Hired, TurboHire, and WorkStep), each of which is designed to browse through a sea of applications and identify relevant terms and experiences.
Customer Care Support
If you think chatbots are tailor-made for only greeting customers and redirecting them to an available human executive, think again. Not only can these automated answering tools figure out the nature of a customer’s query, but they can also direct them to the appropriate help article or FAQ—effectively triaging requests for the customer service team.
Going by the rapid advancement in automation and artificial intelligence, the day is not far away when an automated chatbot application will replace some of the biggest customer-centric call centers in the world.
Costs of Manual Data Processing
No matter what industry you’re in, manual data processing does exist in every business.
Take the example of the insurance sector, where manual processing of insurer documents and other important data has been rated as the number one challenge for CEOs and CFOs. Across the industry, influential leaders are trying to increase overall efficiency and reduce claim processing time, to achieve better outputs and enhance customer satisfaction.
Clearwater Analytics surveyed financial organizations and found that 34% still rely on ancient manual data processing methods. However, automating the same process can eliminate some of the manual reworking steps and cut 15% of overall processing costs.
Let’s talk about logistics and shipping corporations. As per Kofax Software, 32% of the significant global names in this industry still depend on the manual processing of various shipping and transportation-related data. Companies in this industry are working to employ automated processes to standardize tasks related to scheduling and tracking bulk shipments — taking back time to dedicate to innovation and growth.
Data Analysts and Scientists Spend More Time in Data Munging
As per a recent study conducted by CrowdFlower, data analysts and scientists spend most of their working hours cleaning, managing, and organizing data sets instead of analyzing them.
This study reveals that 80% of data scientists spend 60% of their working time cleaning data, while 19% of their working hours are spent collecting data. 76% of these data scientists feel this is a wasted effort and find it to be the dullest part of their job.
These facts are just not random numbers; each figure proves one of the widely known and lamented facts of a data scientist’s work experience, often coined as “data munging” or “data wrangling.”
IDC predicted in 2020 that organizations spending on self-service visual discovery and data preparation tools should grow at a 2.5x faster rate than traditional IT-controlled tools for similar functionalities.
After so many conversations about manual data cleansing procedures, it’s essential to look towards data automation tools as a means to the evergreen, perennial problem.
Data Automation Tools
So, how do you escape the mundane of the manual and implement data automation in your own organization? Here are a few tools to start.
Kloudio’s automated data cleaning, processing, and analysis services continue to be one of the most valued services in the market. Each different service lets technical and non-technical users run a series of SQL commands within Google documents to pull data in a matter of minutes.
The analysis levels are complex, but the users don’t have to take deep dives into understanding the data levels. Capable of handling data complexities ranging from simple to highly complex, each data connection with Kloudio’s repository comes with its own set of features, making data processing a cinch.
QuerySurge is an intelligent data processing tool used to automate, validate, verify, and qualify Big Data processes while preventing duplication of entries and business data losses.
Here are some benefits of the tool:
- Improve data quality and data governance
- Help automate the manual testing effort
- Accelerate your data delivery cycles
- Speed up the testing process up to 1,000x and provide up to 100% data coverage
- Provide testing across different platforms like Oracle, Teradata, IBM, Amazon, Cloudera, etc.
- Deliver shareable, automated email reports and data health dashboards
RightData is well-suited for enforcing data integrity, testing, reconciliation, and validation needs. An organization can significantly reduce overall TCO for data-driven applications.
RightData is designed as a self-service ETL/Data Integrations testing tool that helps design, configure, audit, and automate data reconciliation, testing, and validation processes. Its advanced, intuitive UI allows businesses to connect and query multiple data sources without additional programming.
It supports a host of different data sources, including:
- SAP Netweaver systems (S/4 HANA, ECC, BPC, APO, and BW)
- RDBMS (HANA, MySQL, SQL Server, Oracle, IBM Teradata, DB2, etc.)
- Oracle Apps (EBS and Fusion Cloud)
- Bigdata (Hadoop HDFS, Hive, Redshift)
- Flat files (EXCEL, CSV, XML)
Created by Sparkcognition, a company that builds AI systems to advance some of the most important interests, Darwin is another go-to tool for solving data engineering problems at scale.
Darwin’s automated model-building tool allows its users to go from raw data to extensive data models in less time than some of the widely used traditional methods. It also enables rapid prototyping of scenarios and productive extraction of insights.
The tool uses a patented approach based on neuroevolution as it custom builds model architectures to ensure they are the best fit for the problem at hand.
DataRobot is known as one of the most advanced enterprise AI platforms in the market currently. This platform fuses the knowledge, experience, and best practices of some of the world’s leading data scientists.
DataRobot’s platform helps Machine Learning (ML) developers automate the creation of ML models with unprecedented transparency, allowing data scientists to understand and trust the predictions they make.
This platform comes equipped with different types of regression techniques. Each of these techniques ranges from being the simplest to the most complicated mathematical regression models. One fact which stands out is this platform’s capability to solve simple problems with up to 100 different categories.
Data Automation at Work
Manual data processing is a pain. Thankfully, there are a number of solutions for automating basic, day-to-day tasks. By automating these processes, you save your team time and energy and discover better insights within your data. Create your free Kloudio account to get started with data automation.