Learn about automated data analytics and how to implement it.
Is repetitive and menial work impeding your data scientists, analysts, and engineers from delivering their best work?
Consider automating your data analytics to free their hands from routine tasks so they can dedicate their time to doing more meaningful, creative work that requires human attention.
In this blog we are going to talk about:
Now let’s dive in.
Automated analytics refers to the use of computer systems to deliver analytical products with little or no human intervention.
Automated analytics can be used in a number of ways. For example, you can automate full data processes, automate full business intelligence dashboards, create data-driven self-governing machine learning models, or you can automate singular tasks such as:
There are four main advantages to automating your analytics:
Not every analytics task is ripe for automation. Before you start automating, check that:
The implementation of data analytics depends on which level of automation you are looking at:
The more you move towards full automation, the more the value of automation shifts from saving hours to providing independent impacts on the business bottom line.
But how do you start automating your data analytics?
For example, with Keboola as your data platform, you can say goodbye to human errors and hours wasted on manual efforts. Instead, you can build data pipelines in minutes and schedule automated orchestration to run whenever you want.
Any part of the data pipeline can be automated.
Before you can analyze data, you must have data. But getting the data can be time-consuming. From finding all the dispersed Excel files to building a script that extracts data from 3rd Party Apps, collecting data sets needed for analysis can take a lot of time.
By automating data collection, you can speed up the turnaround time needed to deliver a data analysis. You simply use tools to extract the data from various data sources automatically and set them on a schedule to keep your incoming data fresh.
One such tool is Keboola. Choose from hundreds of ready-to-use components to extract data:
Set up transformations…
and then click on Set Schedule to run automatic executions.
After setting up the flow schedule, the orchestrations will run automatically at the time and frequency you selected, so you can focus on work that drives revenue, not on doing mundane, repetitive tasks.
Think about what it takes to build a dashboard that tracks company KPIs. You need to collect the relevant data, analyze it to extract metrics (with Excel, Python, R, whatever your language of choice is), and then visualize the analysis on an interactive dashboard where the decision-makers can see it. Running the entire pipeline end-to-end can be time-consuming.
Automating scripts that analyze the data, or using tools like Looker or Metabase which automatically visualize data, can chip away some precious times when constructing dashboards.
Building business intelligence excellence takes more than just dashboards. You have to look at different metrics and their breakdowns by important business units to gather new insights that will drive better your decision-making. Are monthly orders increasing in the EMEA region, but not in APAC?
Automate the construction of the different breakdowns by automating data preparation. Build cubes that aggregate data broken down by various dimensions within your warehouse. For example, the total number of orders (aggregation) by geographical region (dimension). Or average order size (aggregation) by customer segment (dimension). Then just look at the cubes to draw insights from the automatically made analysis.
Machine learning specialists build statistical models which can outperform humans at many tasks. For example, a machine learning model is much better at predicting which ads will get clicks than humans are. But building a click prediction model once will not cut it.
Consumers’ tastes are changing and what they shop for online changes alongside. Instead of data scientists and machine learning engineers re-building models every few weeks, automate model construction and selection.
By using automation, you can build different models by selecting different parameters based on the different data combinations. Then automatically select the model with the best click prediction score and deploy it to production.
Automation is not reserved for building models. Banks and financial institutions use state-of-the-art anomaly detection algorithms that look for signals, which indicate fraudulent transactions. Once the signal reaches a certain threshold, the models themselves trigger an account inspection by sending alerts to human inspectors.
The possibilities for automating data analysis are endless, now let’s see how to find the perfect candidate for it.
Before you start reaping the benefits of data automation, you will need to go through the process of setting it up.
And if you already know that using an automation tool will make your life easier (it will) then look no further than Keboola.
Keboola is a data platform as a service with tools that help you automate your data analytics in minutes:
Additionally, Keboola offers a toolbox of features, that can help you with other data analytics automation examples:
Keboola has a no-questions-asked, always-free tier, so you can play around and tap into the potential of automating your data analytics immediately.