Data Analytics

What is automation in data analytics?

Switchboard Jun 28

What is automation in data analytics
Table of Contents

    We know that data automation provides essential benefits in the world of Big Data. But how?

    Data analytics is the process of modeling data to draw conclusions and gain business insights, and with automation, this can be accomplished with much greater speed and efficiency. Here, we’ll explain some of the ways automation is applied in data analytics today, as well as the tools used.

    When should you automate?

    Automation can and should be applied at every stage of the data handling process. For instance:

    Source data automation: The ideal form of data automation uses the direct capture of data at source, rather than involving a manual data entry step. For example, when a POS (Point-of-Sale) scanner reads bar codes at a store’s checkout, this information can be automatically used to update sales figures and inventory.

    Automated data analysis: Rather than having data analysts perform menial tasks, such as reformatting raw data or manually compiling reports, automation frees them to spend time on more valuable problem-solving tasks. You can learn more about the benefits of data automation here.

    Examples of automated analytics

    As companies become evermore data-driven, there are a number of use cases for applying automation:

    • Adtech: Arguably one of the fastest growing industries, this is where websites, apps, and ad tech platforms collect massive amounts of data on user behavior and use real-time automated analytics to identify the point at which a user is primed to make a purchase. In this way, brands are able to show an ad at just the right time and gain the highest probability of making a sale.
    • Detecting bank fraud: Banks monitor customer payment card usage and apply real-time processing to detect abnormal transactions as soon as they happen, such as an unusually high spend on gift cards. This enables them to identify potentially fraudulent purchases and either present an extra security check, or alert the customer.
    • Manufacturing workloads: Factories can collect data about workloads, such as machine work queues and downtime, to gain insights and carry out production more efficiently. For example, if one of three machines is regularly running below maximum capacity at a certain time of the month, the workload can be redistributed so that work can be completed more quickly.

    Types of data automation

    Depending on the use case and requirements, different types of automation pipeline can be applied to your data analytics:

    • Batch data pipeline: Extract a large dataset from its source, process it, and load it at its destination in one fell swoop. For example, data can be transferred from a CRM system to a data warehouse on a monthly basis.
    • Streaming data pipeline: Transfer and process data continuously as it is created at its source. For example, purchase information can be moved in real-time from multiple sources into an ML (Machine Learning) algorithm, which can then make automatic product recommendations.
    • Change data capture pipeline: These types of pipelines only handle the data that has changed since the last synchronization. For example, two cloud services which share the same file store can use a change data capture pipeline to remain synced.

    Data analytics automation tools

    There are many tools available for data analytics automation, from fundamental programming languages, to integrated tech platforms which provide an all-in-one solution. You can also use open-source software. While different software fulfills different needs, the following tools are commonly used in data automation:

    • Microsoft Excel: One of the de facto standards for storing and manipulating data, Excel is able to perform complex operations using pivot tables, formulae, and macros. However, Excel still needs a high level of manual input and is therefore time-consuming and prone to error.
    • Apache Airflow: An open-source platform for creating data automation workflows, and is based on the Python programming language. It provides users with a structured coding environment, plus tools for debugging, logging, and management.
    • Prefect: A workflow management system. It is also based on Python, but extends the language with new functionality to enable you to build complex data pipelines more easily.
    • Switchboard: A powerful turnkey solution that does the heavy lifting so your teams can focus on other mission-critical initiatives.

    If you need help unifying your first or second-party data, we can help. Contact us to learn how.

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