Today, business leaders rely on data to reach informed decisions that were once just based on experiences or a team's knowledge. Because of this change, data teams everywhere saw a significant increase in the data they process.
Regardless of the size of your company, you are likely sitting on high volumes of valuable data. If you think you need to automate data analysis but you’re having trouble getting started, this article can help.
What Is Data Analytics Automation?
Automated data analytics is the process of using computer processes and systems to perform analytical tasks with fewer human interventions. Many modern businesses can benefit from automating their data analytics processes.
For example, a reporting team that requires analysts to produce manual reports could instead use an interactive dashboard for automatic updates.
In data analytics, automation is particularly useful when handling big data. Automation can help data teams perform various tasks, including data discovery, preparation, replication, and warehouse maintenance.
Automation in data analytics can also give business leaders insights that they might miss otherwise.
When to Automate Data Analytics
Automation can make the life of your data team more manageable. But how do you know when and where to automate?
Generally, automation is most appropriate for rules-based tasks that employees often perform. The procedures must also be a part of a routine business process, like managing company documentation, data analysis, or payroll processes.
To know if your business needs to automate its data analytics process, here are the tasks that are suitable candidates for automation:
- Developing dashboards and reporting tasks are ideal candidates for automation. Automated analytics tools can stream, aggregate, and manage data for publishing to live data summaries and interactive plots.
- Modifying and organizing a data warehouse can be much easier with automation. Businesses should take advantage of the artificial intelligence (AI) solutions we have today to facilitate automatic data maintenance.
- Automation can make data preparation tasks less tedious and more streamlined. Today, there are tools that automatically label data, train and validate models, and facilitate study runs to optimize parameters.
- Automated data validation can flag missing values, detect typos, and find formats that do not match a dynamic data model. This feature makes data modeling processes easier and promotes adherence to models by automatically changing data.
- Monitoring available bandwidth, as well as engineering and delivery calendars, are also ideal candidates for automation. AI tools can perform batch ingestion and processing tasks at scheduled times and optimize streaming systems without human intervention.
These are only some of the complex processes that your data team can automate for increased productivity. That said, it is crucial to remember that human intelligence remains irreplaceable.
Validating data or statistical models, asking questions, and translating numbers and figures into actionable insights are all tasks that machines cannot perform.
How to Get Started With Automated Data Analytics
If you think your data team is swamped with tasks that are ideal for automation, you can get started by doing the things below:
Organize Cluttered Data
Data in a business is typically messy and requires a lot of tidying and organization before it can become useful. You need to identify the data that is valuable to you, tidy them up, and put them in the right place.
Hire Experts
Remember, business executives are not data analysts. They will not have the time, patience, and skills to perform data analysis on top of their everyday duties. Build your data team and hire expert analysts who will understand your business needs for data management.
Prepare Datasets
Although AI and machine learning (ML) are exciting technologies, both tools need large datasets to test and train. With well-organized data and the right team, you can have a valuable and large dataset on which to train an AI model.
Organizations handling big data can enjoy significant benefits from automating parts of their data analytics procedures. With automated processes, analyzing data lakes filled with unstructured information becomes possible.