Augmented analytics vs. predictive analytics

By Brynne Henn - July 1, 2021

“Businesses today are living in the era of big data.” If you’re in analytics, you’ve heard this refrain more than enough times, and yet it continues to be true. With more data available than ever before, and the cost of storing that data continuing to fall, the datasets that analysts work with today are massive, intricate, and fast-moving.

Data teams and companies collect this data in the hopes that if they just have enough data, they’ll be able to make the right decisions. But if they depend on legacy business intelligence tools alone to make sense of that data, this hope will continue to fall short. Legacy BI tools were not designed to quickly analyze the volume and complexity of data businesses collect today. These tools can’t keep up with the cleaning, data preparation, and real-time analysis companies need to make informed decisions for their business.

To uncover the actionable insights hiding in your business data, you need a new interface to data, one designed to use all of the data you’re capturing in your cloud-native architecture to inform and support critical decision-making. That new interface is augmented analytics.

In this article, we’ll compare augmented analytics and predictive analytics, two advanced analytics techniques helping businesses improve their data analysis and change the way they make decisions.

What is augmented analytics?

To start, let’s take a look at what we mean by augmented analytics. Gartner officially coined the term “augmented analytics” in 2017 to describe an approach to data analytics that combines machine learning and artificial intelligence techniques to analyze data.

Specifically, Gartner defines augmented analytics as “the use of enabling technologies such as machine learning (ML) and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”

Augmented analytics accelerates data teams by leveraging ML/AI to automate the rote, repetitive parts of analysis like data exploration and data preparation. With this automation, organizations can focus on creating faster insights to answer every question from the business and allow their data analysts and data scientists to focus on what they do best – interpreting results and diving deeper into the data.

The benefits of augmented analytics

Because augmented analytics automates the manual, time-consuming parts of analysis and uses all of the data businesses collect to deliver more comprehensive insights to the business, it has four core benefits for a business:

1. Faster insights:
With more data available and more data sources, data scientists and data analysts spend most of their time preparing data, not analyzing it. In fact, according to Forbes, data scientists spend around 80% of their time preparing and managing data for analysis.

By quickly uniting data from multiple sources and applying ML-based recommendations, augmented analytics enables faster access to insights derived from massive amounts of structured and unstructured data. And rather than having to test every hypothesis and data combination manually, analysts can rely on augmented analytics to find the hidden factors and relationships driving changes for the business.

Augmented analytics leaves the painful, manual tasks to the technology, allowing the data analysts to focus on interpreting and explaining the results of an analysis to drive faster insights for the business.

2. Comprehensive and accurate analysis:
Rather than starting from a hypothesis and testing data against it, augmented analytics starts from the metrics that matter to you and then tests every single factor in your data to understand what’s driving changes in your metrics.

This automated root-cause analysis ensures that no information is overlooked, which could lead to suboptimal decisions and missing new opportunities. Rather than relying on data analysts to efficiently and thoughtfully process ever-increasing reams of data, this analysis automatically tests every bit of data to identify the factors that contribute to a KPI as they shift over time. This helps businesses quickly understand what has changed and why while also pointing decision-makers to areas for further investigation.

3. Actionable answers:
Whether you’re a start-up with a start-up budget, or a Fortune 500 company with more data than you can handle, being able to test millions of hypotheses in seconds instantly expands your team’s ability to answer questions and find critical insights.

Augmented analytics makes it possible to not only answer what happened in the data, but to find the populations and subpopulations driving changes in metrics. These descriptive and detailed answers enable data teams to be more focused when jumping into any analysis, saving time in trial and error and allowing analysts to dive deeper into the data to find actionable insights.

4. Streamlined decision-making:
Even at the largest organizations, there are rarely enough analysts to go around to enable data-informed insights for every decision.

This challenge has pushed product managers, marketing leaders, and sales operations to learn the necessary SQL and BI skills to monitor and act on changes to their KPIs. However, these business users are often limited on the questions they can answer independently, hindering decision-making and creating a backlog of questions for analytics teams.

With augmented analytics, analytics teams are able to answer more questions from the business. At the same time, because non-technical business users can ask questions about the metric instead of about the data, they’re able to find more answers on their own or receive automated insights and alerts based on ongoing monitoring of their core KPIs.

These changes eliminate the common bottlenecks to decision-making, allowing companies to make the best possible decisions for their business faster.

What is predictive analytics?

Like augmented analytics, predictive analytics is a type of advanced analytics. But instead of answering what changed in a business and why, predictive analytics focuses on identifying patterns in data and determining if those events are likely to happen again.

Predictive analytics combines historical datasets with statistical modeling, data mining, and machine learning techniques to estimate or forecast future outcomes for a business or organization.

Many industries and use cases depend on predictive analytics to make critical decisions. It’s an effective tool for modeling customer behavior, assessing risk, building accurate sales forecasts, and more.

Understanding the benefits of predictive analytics

Since predictive analytics makes it possible for companies to not only look at what happened in the past, but also make reliable predictions about changes or risks to a business, it has two primary benefits for organizations:

1. Answers “what if” before it occurs:
While predictive analytics can’t predict the future, it can help businesses forecast the impact of certain campaigns, initiatives, or changes before they’re executed. Because predictive analytics builds statistical models based on historical data, it allows business users to basically take decisions for a test drive by calculating the outcome of any choice. With this information, companies can better prepare themselves for the reality of their options and make the most informed decisions possible.

2. Increase competitive advantage:
Like augmented analytics, predictive analytics helps businesses gain a competitive advantage by allowing them to see every opportunity in front of them. These insights into the opportunities to come enables a company to be proactive, get ahead of any problems before they arise, and anticipate critical trends in its market

Predictive analytics is not without disadvantages, however. Because predictive analytics applies statistical analysis, analytics queries, and machine learning algorithms to historical datasets, it is not the right tool to use in uncertainty. When the unexpected happens and businesses don’t have historical data to turn to, as was the case with the COVID-19 pandemic, relying on predictive analytics only will make it difficult to respond.

Augmented analytics vs. predictive analytics: Differences and strengths

If you’re looking to improve your modern data stack by adding additional analytics software, augmented analytics and predictive analytics can help business users make better decisions with their data, but they differ in their strengths.

Augmented analytics uses the data your business has on hand and accelerates the analytics process for your data science and analytics teams. By combining machine learning and artificial intelligence techniques like natural language processing (NLP), augmented analytics improve the data analysis workflow allowing teams to quickly understand what’s changing in their data and identify the key drivers. This insight provides teams with a quick, comprehensive view of why their business is changing, allowing them to make the best possible decisions.

On the other hand, predictive analytics is more focused on using historical data (and some current data) to help businesses forecast and plan based on what is to come in the future. These insights allow teams to anticipate outcomes before they occur and plan accordingly. However, the insights are only as good as the data they are based on, so it should not be solely relied on in times of uncertainty.

Augment and accelerate your decision-making with Sisu

As the industry’s first decision intelligence engine using augmented analytics to analyze millions of dimensions and trends in cloud-scale data in seconds, Sisu is able to help teams of all sizes quickly make sense of their data in time to act. In fact, our mission at Sisu is to operationalize the world’s data, allowing every team to make the best possible decisions with their data.

Looking to accelerate your analytics workflow and improve your decision-making process? Get in touch with our team to see how Sisu can help.