What is Automation in Data Analytics?

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.

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Benefits of Using 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.
    • 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.

Top 5 Automation Tools for Data Analytics

The following are five of the most popular platforms and tools used by Data Analysts and Data Scientists looking to automate various aspects of the analytic process:

  1. Whatagraph: This cross-channel reporting tool is primarily used by marketers and marketing professionals to monitor the success of campaign efforts across various channels. Whatagraph allows users to create customized data visualizations that lead to results-based decisions. Those working with this tool can set up KPIs for each marketing channel, monitor expenses, and bring their insights together in a single report that contains all marketing metrics. This powerful tool provides a comprehensive overview of performance that can be delivered to clients at a pre-specified frequency. In addition, marketing performance reports can be generated in just minutes using Whatagraph’s templates.
  2. Darwin: This automated building model tool speeds up the process of going from data to model. It also provides rapid prototyping options for various scenarios, and has the capacity to quickly and effectively extract insights. This tool relies on a patented approach that involves neuroevolution that customizes model architecture creation and guarantees the best fit for specific problems.
  3. DataRobot: This advanced enterprise AI platform is designed for Data Analysts and Data Scientists who wish to automate the process of creating machine learning models, and to incorporate transparency. This program includes both basic as well as complex regression techniques and models. One of the main features of this platform is that it’s able to solve simple problems with up to 100 categories.
  4. Datapine: This business intelligence software helps those who have data saved in various places combine it into a single source. Datapine users can integrate data from external applications and multiple spreadsheets. Its intuitive user interface provides drag-and-drop functionality in which users can place any desired value into the Analyzer tool to generate graphs and charts. This software also offers the SQL mode that allows users to create their own queries, as well as to run existing scripts and code. Datapine’s predictive analytics forecast engine can analyze data from a range of sources and integrate it with data connectors. It optimizes pattern recognition, neural networks, and threshold alerts to notify users of any anomalies that appear, which drastically cuts down on the time and effort manual analysis would entail.
  5. SAS Visual Forecasting: This open forecasting ecosystem provides a way to simply automate the forecasting process when working with hierarchical forecasts and large-scale time series analyses. Because human involvement isn’t required for producing accurate forecasts, this tool reduces the likelihood of personal bias affecting the process. SAS Visual Forecasting can generate millions of forecasts at extremely fast speeds, which not only cuts down on the time required to make predictions, but also the resources needed to do so.

Selecting the best automation tool for your data analytic and data science needs is a vital step to help a company create the best analytics capabilities possible. It can be difficult and time-consuming to change tools, so it’s important to make sure that the platform you select is reliable, accessible, and can perform the sorts of calculations your data demands.

The Future of Automation in Data Analytics

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.

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.