Automation in Analytics

September 13, 2021

written by Toni Mamolo, Software Engineer at Sparkline.

Photo by Alex Knight on Unsplash

In our current era, most of us are constantly relying on automation. Could we even try not to use  an automated device for a single day? 

Think about the cell phone and computer you use every day to do your job. Think about the car you drive to take to work. Think about the food you eat, water you drink, clothes you wear and appliances you use to store, prepare, and clean them. Think about the television you watch, video games you play, or music system you listen to. 

Without automation, our world and our future would be very different. 

On the other hand, Automation in data analytics is also very trending nowadays, and most of the world's big companies are applying it to their own strategies. Have you checked your Facebook or YouTube ads? Why do you keep on getting those specific ads? Did you check the weather tomorrow on your search engine? In today’s generation, data automation will help businesses and companies grow. But what is automation in analytics?

Automated analytics is a practice of computer systems and processes to perform analytical tasks without human intervention. It is very useful when dealing with huge amounts data, and it can help us align on multiple tasks such as : 

    - data discovery

    - data preparation

    - data replication

    - data warehouse maintenance

Automated analytics helps us to identify relevant anomalies, patterns, trends and deliver insights to business in real-time without doing a manual analysis. We have some useful technologies that can help us in doing this such as : 

    - artificial intelligence (AI)

    - natural language generation (NLG)

    - machine learning (ML)

When automating data analytic,  we need to ensure effective implementation, prevent interruptions and minimize inconvenience for the data analyst; hence, we can follow this process:

First, we need to identify our objectives, set clear goals and expectations for the automation process in advance, Next Determine metrics for measuring the performance and utility for the automated process,  And last choose reliable and well-supported automation tools such as Python, R and Scipy packages.

As a Software Engineer at  Sparkline, I use Python as a programming language with Apache Spark for big data workloads. Spark is a fast and general engine for large-scale data processing, it can handle up to 1 petabyte of data, it's also easy to use and supports analytics automation technologies like Machine Learning, supporting my workflow to ensure we deliver the best results for the business and our customers. 

Sparkline aims to provide data accuracy, comprehension and consolidation, and most importantly, tangible insights for businesses. Get in touch if you’d like to learn more.