Predictive Analytics vs. Augmented Analytics
Predictive Analytics vs. Augmented Analytics
"Businesses are now living in the age of big data" is a term you have no doubt heard many times. This is because it is not a passing trend but a statement that continues to hold true.
Analysts today work with large, complex, and rapidly-changing datasets.
Companies and data teams collect this data in hopes that they will be able to make the right decisions. This hope will be shattered if they rely on legacy business intelligence software to make sense of this data.
Legacy BI tools are not designed to analyse the complexity and volume of data that businesses have today. These tools must catch up with the data cleaning, data preparation, and real-time analytics companies require to make informed business decisions.
It would help if you had an interface that allows you to access all the data in your cloud-native architecture and use it to support your decision-making. Augmented analytics is the new interface.
This article will compare predictive analytics and augmented analytics. These advanced analytics techniques help businesses make better decisions and improve data analysis.
What is Augmented Analytics?
Let's first define what augmented analytics is.
Gartner first used the term "augmented analysis" in 2017 to describe a data analytics method that uses machine learning and artificial intelligence to analyse data.
Specifically, augmented analytics speeds up data teams by leveraging ML/AI for repetitive analysis, such as data exploration and preparation.
This automation allows organizations to create faster insights that answer all questions from their business. It also allows data scientists and analysts to concentrate on what they do best: interpreting the results and digging deeper into the data.
Augmented Analytics: The Benefits
Augmented Analytics automates manual and time-consuming analysis and uses all data collected by businesses to provide more detailed insights for the business. It offers four core advantages to a business:
1. Faster Insights
Data scientists and analysts spend more time preparing data than analysing it because there is more data and more sources than ever. According to Forbes, data analysts spend about 80% of their time managing and preparing data for analysis.
Augmented analytics allows faster access to insights derived from large amounts of unstructured and structured data by combining data from multiple sources. As a result, analysts can test only some hypotheses or data combinations manually. Instead, augmented analytics can help them find hidden relationships and factors driving business changes.
Augmented analytics takes out the tedious, manual tasks so that the data analysts can focus on explaining and interpreting the results to provide faster insights of the business.
2. Complete and Precise Analysis
Augmented analytics is not based on a hypothesis but rather on testing data against it, and it starts with the metrics that are important to you. Then, every factor in your data is tested to determine what's driving your metrics to change.
The automated root cause analysis makes sure that no information is missed, which can lead to poor decisions or missing new opportunities.
This analysis automates analysing every bit of data and identifies factors that affect a KPI's shifts over time. This allows businesses to quickly see what has changed and why, while also pointing out areas that need further investigation.
3. Actionable Solutions
You can test millions of hypotheses quickly, regardless of whether you are a start-up with limited funds, or a Fortune 500 company that has more data than your system can handle. This will allow you to expand your team's ability to answer questions and provide critical insights.
Augmented analytics allows you to answer what happened and identify the subpopulations driving the changes in metrics. This allows data teams to be more specific when diving into analysis. It saves time and error and allows analysts to dig deeper into data to uncover actionable insights.
4. Streamlined Decision-Making
Even in most large organizations, analysts cannot provide data-informed insights that will enable every decision to be made.
Product managers, sales, and marketing leaders have had to learn SQL and BI skills to monitor and respond to changes in their KPIs. Unfortunately, these business users often have limited knowledge and need help answering all questions independently. This can lead to poor decision-making and a backlog for analytics teams.
Analytics teams can answer more business questions with augmented analytics.
Non-technical users of the business can also ask questions about the metric rather than about the data. This allows them to get more answers on their terms or receive automated insights, alerts, and notifications based on continuous monitoring of their core KPIs.
These changes remove common bottlenecks in decision-making and allow companies to make better business decisions faster.
What is Predictive Analytics?
Predictive analytics, similar to augmented and advanced analytics, can be used as a type. Predictive analytics, however, focuses not on answering the question of what has changed in a company and why. Instead, it identifies patterns in data and determines if these events will occur again.
Predictive analytics combines historical data with statistical modelling, machine learning, and data mining to predict or project future outcomes for a company or organization.
Predictive analytics is used in many industries and applications to make crucial decisions. It is a powerful tool to model customer behaviour, assess risk, and build accurate sales forecasts, amongst many other uses.
Predictive Analytics vs. Augmented Analytics
Augmented and predictive analytics may be a good choice if you want to enhance your modern data stack with additional analytics software.
Augmented analytics uses your business's data and speeds up the analytics process for your analytics and data science teams.
Augmented analytics combines machine learning with artificial intelligence techniques such as natural language processing (NLP) to improve the data analysis workflow. This allows teams to quickly identify key drivers and understand the changes in their data. This information gives teams a quick and comprehensive overview of why their business is changing. As a result, it allows them to make the most informed decisions.
Predictive analytics, on the other hand, is focused more on historical data and some current data to help businesses plan and forecast based on the future. These insights enable teams to plan and anticipate future outcomes. However, the insights can only be as accurate as the data they are based on. Therefore, it is not recommended to rely on them in times of uncertainty.
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