More About Automated Data Analytics

Do you think the menial and repetitive work is hampering your data scientists, engineers, and analysts from delivering their best work?

If the answer is yes, it is time to consider automated data analytics. Using automated data analytics, you can free yourself from routine tasks so that more time is dedicated to doing more meaningful and creative work demanding human attention.

As we walk you through the roads travelled by automated data analytics, we will cover the following points:

• What are automated data analytics?
• What are the benefits of automated analytics?
• When can you use automated data analytics?
• Examples of automated data analytics

Marketing Technology News: MarTech Interview with Anil Malhotra, Chief Marketing Officer at Bango

What is Automated Data Analytics?

As the name suggests automated data analytics refers to using computer systems to deliver analytics with little or no human intervention.

From data discovery, data preparation, replication, and data maintenance, automated data analytics helps accomplish a myriad of tasks on the collected data.

Benefits of Automated Data Analytics

Automated analytics is especially useful for businesses that require data insights not available through any other means. It is an AI and ML-backed technology enabling businesses to analyze huge chunks of data, train numerous machine learning models, offer a hypothesis, and generate thousands of data patterns.

Here are some benefits of using automated data analytics in your business process:

1. Accelerating Reporting Process

Because automation requires little or no human input, data analysts and data scientists can complete complex analytics tasks faster without relying on a human. It accelerates the data reporting process and saves time and money.

2. Financial benefits

Automated data analytics reduces the need to hire a larger workforce and pay monthly salaries. On the other hand, it doesn’t take much to make a computer program do the same tasks. Thus, such enhanced analytics saves money.

3. Create time for Revenue-generating work

Your hired staff for data analytics no longer spends time tweaking the data pipeline manually, as you have systems in place to do so. Thus, your team of data scientists and analysts have more time to tackle complex business problems and come up with creative ideas to generate business revenues.

4. Improved Processes and Systems

By automating mundane tasks, you can skip the error-prone work. Automation truly helps to build future-proof processes and systems.

When can You use Automated Data Analytics?

We have seen how automation can enhance your data analytics processes. However, how do you know when and where to use automation? As a thumb-rule, automation is the most appropriate for rule-based and repetitive tasks.

Automating a specific one-time study is fine, but automating data discovery tasks in your organization that engages data scientists, each working with varied data sources, would be more effective.

Here are some analytical tasks good for automation:

• Use automated data analytics to create dashboards and daily reporting. Leave the process of streaming, processing, and aggregating data for publishing, live data summaries, and creating interactive plots.
• Automated data analytics can perform tasks like data maintenance, modifying, and fine-tuning a data warehouse. As a smart enterprise, you must take advantage of these tools that facilitate the integration of new data sources or the migration of data from legacy systems effortlessly.
• Automation can also streamline data preparation tasks. It also automates data validation to detect typos, impute missing values, and identify the formats that do not match your dynamic data model.

Although, automated data analytics cannot replace human intelligence, many parts of your data analytics stack can benefit from automation.

As most of the organizations have adapted to cloud services such as Amazon AWS, automated data analytics become an integral part of your martech and salestech ecosystem. Let us have a close look at these tools here.

1. Amazon AWS

Automated data analytics on Amazon AWS enables organizations to drive meaningful insights from data in a matter of minutes. It comes with a simple and user-friendly interface. The solution helps to consolidate data distributed across siloes, apply fine-grained governance, and query all the data using a tailored experience.

2. Microsoft Azure

Microsoft Azure uses machine learning capabilities to train and tune a model that any organization uses to target any metrics. Irrespective of the data science expertise, the smart system empowers the users to identify an end-to-end machine learning pipeline for any problem.

3. IBM Cloud Pak for Data

Organizations can use IBM Cloud Pak to intelligently automate their data and AI strategy. They can connect the right data to the right people at the right time, from anywhere.

Wrapping Up

All the organizations dealing with big data can put automation to work and build an impermeable data analytics infrastructure. Automated processes can quickly analyze the data lakes filled with unstructured information with little or no human intervention. Even modern data warehouses with controlled and strict data modeling processes are streamlined by automation.

Marketing Technology News: Adopt AI, or be left behind