When to Use Automated Data Analytics in Your Business

Data analysis is an essential part of running any business, but it can be time-consuming and complex. Automating data analysis processes can be a great move for your business. It saves you time and money, gets you valuable insights faster, and frees up your team to work on higher-level tasks that keep them engaged, motivated, and productive. 

Automated data analysis is performed by machine-learning AI platforms with little to no human intervention. Machine learning platforms are able to find patterns in huge amounts of data, so you can simply train your employees how to set up data analysis models and you’ll be able to get insights in minutes. 

In this article, we’ll talk about when to use automated data analysis and the benefits of doing so. We’ll also introduce you to Akkio, an autoML platform that can handle all your data analysis needs. 

The Benefits of Automating Data Analysis in your Business

Handling big data manually involves a herculean effort. The "5 Vs" are commonly used to describe big data – Volume, Variety, Velocity, Variability, and Veracity. That means that big data isn't just large in terms of size, but it also involves a huge variety of fast-moving data types and sources, which need to be handled quickly and accurately. Automating your data analytics process will help you manage this data load much more quickly and easily.

When moving from small data, like simple Google Sheets, to big data, automated data analysis is essential. Automated data analysis can be used for a wide variety of tasks ranging from data discovery, data preparation, data replication, and data warehouse maintenance.

Automating your data analytics means that your in-house data analysts don’t need to spend their time on routine tasks, and can instead focus on taking analyzed data and creating actionable plans to reach your business goals.

For example, analysts can use the data to identify credit card users who are likely to churn and take steps to prevent that. Many factors are involved in whether a customer will churn, but with automated data analysis, analysts can quickly and accurately identify patterns and correlations in the data and take the necessary steps to reduce churn.

This gives non-technical teams the ability to make data-driven decisions. Your team will also have more capacity to add new data sources to your ML model and make future insights more comprehensive. 

And finally, automation will increase the speed of analytics - you simply need team members who are able to set up and train the ML model, and you could get results in minutes. With no-code and low-code platforms like Akkio, even non-technical teams can take the reins of data analysis and get insights in the least possible time.

When to Automate Your Data Analysis

Not all business decisions require complex data analytics systems. After all, you can often uncover new insights into business processes, metrics, and profitability through basic data analysis and visualization tools, like Excel. For example, a simple spreadsheet tracking key performance indicators (KPIs) can yield quick insights. 

But as your business grows and data becomes more complex, you'll need to lean on automation tools and data analytics systems to process, aggregate, and visualize your data more quickly. Python, SQL, and ETL (extract, transform, load) tools can be combined to build a data pipeline capable of quickly extracting, processing, and modeling data. This automation can be used by data scientists and analysts to create dashboards, graphs, spreadsheets, or other visual elements to discover new insights or correlations in the data. 

Advanced machine learning models and artificial intelligence can also be employed to automate advanced analytical tasks. Or you might leverage APIs or connectors to access larger pools of data stored in any number of streaming data lakes or cloud storage services. Akkio, for instance, integrates with a host of popular tools, from Snowflake and BigQuery to various other applications with Zapier.

By automating complex analytics, businesses can focus on what they do best: creating and selling products and services. Whether your day-to-day work involves creating webinars, hosting product demos, or crafting clever content marketing strategies, data analytics automation tools free up your team to optimally focus on these areas while still keeping track of your critical metrics. 

Examples of Automated Data Analysis

Practical examples can help us put these abstract concepts into real-world terms. As AI and data science continue to take an increasingly prominent role in the world, the automation of data analysis has become a valuable tool for businesses. Let's explore three different types of automated data analysis, including business intelligence automation, ML models and big data automation, and automated data collection

Business Intelligence Automation

Business intelligence automation is the use of AI algorithms and tools to automate the process of gathering, storing, and analyzing data. Business intelligence automation is used to gain insights into customer behavior, market trends, and other business-critical information. With it, businesses can quickly and easily analyze large sets of data to identify patterns, trends, and correlations. This can be used to make better decisions, create more efficient processes, and gain a competitive edge. 

Most analysts are familiar with tools like Tableau and PowerBI, which are used to organize and visualize data. However, these tools are often limited in their ability to automatically uncover deeper insights. Business intelligence automation gives users more power by allowing more complex analyses. For example, AI algorithms can be used to detect anomalies, uncover hidden relationships, and predict future trends.

Anyone who has used legacy BI tools knows that the process can be tedious and time-consuming. By automating the process, businesses can save time and money while still receiving the same high-quality insights.

ML Models and Big Data Automation

Going beyond BI automation, ML models can be used to automate the analysis of large datasets. ML models use algorithms to automatically detect patterns in data, which can then be used to make predictions and decisions. ML models can be used to analyze customer behavior, detect fraud, and predict future events. 

By automating the analysis of large datasets, businesses can gain insights faster and make more accurate predictions. For example, an ML model can be used to quickly detect patterns in customer behavior, which can then be used to optimize product pricing and marketing campaigns.

Big data automation is the process of automating the analysis of large datasets. Big data automation allows businesses to quickly and easily query large datasets to uncover insights. With big data automation, businesses can quickly analyze large datasets to identify patterns, trends, and correlations. This can be used to make better decisions, create more efficient processes, and gain a competitive edge. 

Automated Data Collection

Finally, automated data collection is the use of AI algorithms and tools to automatically collect data from a variety of sources. Automated data collection can be used to quickly and easily collect data from websites, social media, and other sources. This data can then be analyzed to gain insights into customer behavior, market trends, and other business-critical information.

Automated data collection can be used to quickly collect large amounts of data, which can then be used to train ML models. Automated data collection can also be used to quickly build large datasets, which can then be used to perform advanced analytics.

How Akkio Automates Data Analysis for Your Business

Akkio is a leading AutoML platform that automates the data analysis process for all kinds of applications.

In a comparison between Google AutoML, Microsoft Azure AI, Amazon SageMaker, and Akkio, Akkio emerged as the most cost-effective, fast, and easy-to-use machine learning platform to automate data analysis processes. Akkio is the perfect choice for businesses that need to make data-driven decisions quickly, accurately, and reliably.

Akkio provides a unique experience to business analysts by providing an AI model embedded in the platform that they can use to analyze data. With this model, users can input sample numbers and receive accurate predictions in real time. For example, a business analyst may input sales data for the past month and receive an accurate prediction of sales for the coming month. This helps businesses to make data-driven decisions with confidence and accuracy.

Below is an example of a churn prediction AI model built with Akkio. Try it out with sample data and see how Akkio can help you analyze data and improve customer retention. 

Akkio also integrates with many other tools that your business may already be using. This makes it easy to share insights across teams and departments, which leads to better collaboration and faster decision-making.

Akkio is an invaluable tool for businesses that need to make data-driven decisions quickly and accurately. With its intuitive design, fast performance, and integration capabilities, Akkio is the perfect choice for businesses looking to automate their data analysis process.

A Guide to Automating Your Data Analytics with Akkio

Years ago, businesses that wanted to take advantage of data analytics had to manually sift through rows of data and analyze the results. Since businesspeople can't simply drop everything else and focus on analyzing data, this meant requiring deep pockets and a lot of time.

Today, the process of automating data analytics can be done in a matter of minutes with the help of Akkio. Akkio is a revolutionary data analytics tool that helps businesses automate the entire data analysis process.

Here is a step-by-step guide to automating your data analytics with Akkio.

Step 1: Design the Data Set to Allow Automation

The first step in automating data analytics is to design the data set to allow for automation. This involves ensuring that the data set is clean, structured, and organized in a way that allows for easy automation. This includes ensuring that all columns have consistent values and removing any unnecessary data. With Akkio, you’ll get a detailed overview of the data to ensure everything is in order.

‍

Common data issues that can arise include inconsistent data formats, missing values, and duplicate entries. For example, if you've merged two data sets, you may find that the columns don't match and need to be adjusted. Or, you may have missing values that need to be filled.

Using Akkio, the entire process of cleaning and organizing your data set is easy and can be done in minutes. You can even tell Akkio how to edit the data with our Chat Data Prep™ feature, seen below.

‍

Step 2: Create a Test Plan

Once the data set is ready for automation, it is important to create a test plan. This plan should outline the objectives of the automation, the steps that need to be taken, and any expected outcomes. This will help ensure that the automation process is successful and that the results are accurate.

Step 3: Create a Clear File Naming System 

Creating a clear file naming system is essential for successful data analytics automation. This system should be easy to understand and should be consistent across all files. It should also include descriptive tags that make it easier to identify and access the files. 

Step 4: Store The Resulting Data Files in A Specific Folder

Once the data has been organized, it is important to store the resulting data files in a specific folder. This is to ensure that all of the files are easily accessible and that the data is organized and structured. Additionally, it is important to back up the data to ensure that it is protected. 

Step 5: Generate Analyses From The Resulting Data Set

Once the data is organized, it is time to generate models from the resulting data set. This can be done by using Akkio's AI algorithms which are designed to identify patterns and correlations in the data. These models can then be used to make predictions and decisions or to gain insights into customer behavior, market trends, and other business-critical information.

‍

You can also deploy the models in production so that they can be used to make real-time predictions and decisions. This can be done with Akkio's integrations with popular tools like Snowflake, BigQuery, and Zapier.

Automate Your Data Analytics with Akkio

Data analysis is an essential part of running any business, but it can be time-consuming and complex. Automating your data analysis processes can be a great move for your business. It saves you time and money, gets you valuable insights faster, and frees up your team to work on higher-level tasks that keep them engaged, motivated, and productive. 

Akkio makes data analysis automation effortless. Akkio is an AutoML platform that automates the entire data analysis process, from data collection and cleaning to analysis and visualization. With Akkio's intuitive design, fast performance, and powerful analytics engine, businesses of all sizes can quickly, accurately, and reliably automate their data analytics processes. 

By understanding when to use automated analysis and how to make the most use of it with Akkio, businesses can maximize the benefits of automation and make data-driven decisions faster than ever before. See Akkio’s pricing plans or get started with a free Akkio account today to start automating your data analytics processes!