When used in data analysis, automation pertains to replacing the human factor with computer processes or systems. The act of automating analytics requires constructing systems that can automate one part of a data pipeline, or the entire pipeline. The various mechanisms that automate data differ in complexity. Whereas some are basic scripts that are compatible with pre-established data models, others are complex, full-service tools that enable users to carry out actions such as exploratory analysis, statistical analysis, and model selection.
Automated analytics is especially useful for businesses in that it offers insights that may not be available through any other means. This powerful technology draws from machine learning and AI, which enables it to analyze huge stores of data, offer hypotheses, train hundreds of machine learning models, and generate thousands of data patterns. Although automation can’t completely take over the data science process, it helps to eliminate some of the more tedious aspects.
Data analytics automation is currently still in the early stages of development but is already playing an integral role in the speed and efficiency with which businesses can gain insights from data. According to a 2020 survey, nearly a third of businesses have completely automated at least one function. This number is projected to continue to increase, as more data is created, and as new machine learning and AI techniques become more commonly applied to the data sector.
Why Use Automation in Data Analytics?
There are many benefits to using automation in the analytic process. Here are a few reasons why automation is a powerful analytic tool:
- Speed: Because automation requires little or no human input, Data Scientists or Data Analysts can complete time-consuming or complex analytics tasks much faster than would be possible by relying on a human.
- Financial benefits: It costs much more to pay a human to work than it does to program a computer to do the same tasks. Automated data analytics saves time, therefore money, for organizations.
- Ability to handle time-varying data: By categorizing data into various time segments, data from a specific time frame can more easily be retrieved for decision-making purposes.
- Benefits for predictive analysis: Predictive analysis is typically a tedious, costly, and time-consuming endeavor. Automated data analysis languages and tools make it much easier to identify problems with prediction. Automating this process can also work with different analog categories as well as labels of data.
- Discovering unknown unknowns: Data Scientists can use automation to test for scenarios that they may not have otherwise considered, and do try significantly more cases in order to isolate the impactful ones.
- Increased business value: Automation mechanizes activities that are generally tedious and repetitive, which saves Data Scientists valuable time that can be used for more valuable pursuits, like devising new questions to ask of the data, or pinpointing new sources of data.
- Quicker decision making: Decisions can be made without human input, and variables can be adjusted in real-time.
- Quality insights: Manual human analysis can provide certain business insights, but not always the kind of complex insights that can be offered using automated data analytics.
While it’s unlikely that automation will one day take the place of all human contributions to the data analytics process, the role automation is expected to play in the data world is expected to continue to increase in the future.
When to Use Automation in Data Analytics?
Automation is a valuable tool for both small and large businesses, but how will your organization know when the right time is to automate its data analytics process? In general, automation is particularly useful for processes or tasks that have to be repeated often and are rule-based. For tasks that only need to be performed once, automation isn’t always the most efficient solution.
The following are a few of the tasks that are most suited for automated analytics:
- Preparing data: Visual programming tools like KNIME perform tasks like labeling, validating, and training data models.
- Validating data: Typos, content mismatches, and formatting errors can be identified and flagged, as well as corrected, using data analytics automation.
- Maintaining data: Automation simplifies tasks like modifying data.
- Reporting/creating dashboards: Automated data analytics allows data to be processed, streamed, and aggregated publishing on interactive plots or live data summaries.
- Replicating and ingesting data: Available bandwidth and delivery calendars in a given system can be monitored, allowing for batch processing at the appropriate moments, as well as systems streaming in real-time.
Using automation at the right time is a time-saving, cost-cutting tool that is transforming how businesses analyze data.
Hands-On Automation Classes
For those who want to learn more about automation, as well as the other tools available to efficiently work with big data, Noble Desktop’s data science classes provide a great option. Courses are available in-person in New York City, as well as in the live online format in topics like Python and machine learning. Noble also has data analytics courses available for those with no prior programming experience. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and data analytics.
If you want to learn more about how Python can be used for automation, Noble’s Python for Automation class is for you. This six-hour class teaches students how to collect, store, and analyze web data using Python.
Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech.
For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows users to find and compare classes to decide which one is the best fit for their learning needs. This tool can also be used to choose from more than 100 computer science classes as well.