How to drive productivity with automated data analytics – Part 1

Most organizations have a team at hand – data analytics engineers, data scientists, or data analysts – to analyze raw data and unlock key insights on their customers, products, and operations.

It’s clear that it brings value to businesses – 49% of those surveyed by Deloitte say it helps them make better decisions – but there’s one problem. The majority of the tasks needed for data analysis – i.e., data aggregation – are repetitive and eat into the team’s time.

The solution? Automated data analytics software.

But first, let’s explore the definition of automated data analytics.

What is data analytics automation?

Data analytics automation is the process of using computer systems to compile, process, and analyze raw data. Since it’s automated, this kind of data analytics requires little human input, enabling teams to cut manual tasks from their workflows and focus on what’s most important – deriving and interpreting insights that the business forward.

Use cases of data analytics automation

Data analytics automation is incredibly versatile, meaning you can automate any part of the pipeline. Let’s take a closer look at some of the most popular use cases of automation in data analytics.

1. Reporting pipelines

A reporting pipeline is made up of many tasks – i.e., creating reports, tracking KPIs, and extracting metrics – which is sped up using automation. Not only does this enable your team to focus more on the actual analysis, but it will also help facilitate real-time, interactive dashboards when you plug into a visualization tool.

2. Data extraction and aggregation

From sifting through documents and spreadsheets, to reviewing historic data going back days, months, or sometimes years, extraction and aggregation is one of those mindless tasks that quickly becomes unsustainable without automation. As well as reducing the time it takes to aggregate data, an automated data analytics system can also be set to follow a specific schedule – ensuring that new data is extracted, stored, joined and normalized automatically as it comes in.

3. Big data

Automation can also be helpful if you’re dealing with tasks that rely on data at scale, such as machine learning (ML) models, data discovery, and data replication. Depending on software you use, you may also be able to generate complex scripts (prediction tools, detection algorithms, and statistical tests), as well as more simple scripts that fit a pre-set data model.

4. Business intelligence

Organizations can make better data-driven decisions by implementing automation into their business intelligence systems (i.e., data mining, business analytics, data visualization, and existing infrastructure), speeding up the time it takes to get to critical insights based on the business team’s specific KPIs.

How automated data analytics can add value

Data analysts can use automation to ensure that the data they’re working on is complete, high-quality, and accurate. They can also use it to ease their workloads and focus on the tasks that bring the most value. Some of the biggest benefits of automation in data analytics include:

• Increased data handling: Computer software can process and analyze large swathes (petabytes) of data in one go, as well as working on different tasks at the same time, drastically reducing project lead times.

• Reduced human error: Human error is inevitable, but it can also be very dangerous for the business when it goes undetected. Automation prevents this from occurring in the first place, ensuring that your data team only gets access to complete, high-quality data they can trust.

• Accelerated analytics: Automation can reduce the time it takes to perform data analytics, even if you only automate a part of the pipeline.

In our next post, we’ll look at the four steps to automating data analytics, as well as which parts of the data pipeline you should automate.