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The first time in human history, the number of bytes and the bits that we have in the digital world is going to be as similar to the number of stars in the physical universe.
So we are entering an exciting decade for enterprise data analytics, where data is now considered among an organization’s most valuable assets. As the amount of data grows and becomes more diverse, identifying the most relevant, accurate and actionable discoveries becomes more and more difficult.

Augmented Analytics is trending since the day Gartner coined the term Augmented Analytics in the 2017 Hype Cycle for Emerging Technologies report and claimed it would be the “future of data analytics and apparently, it will gain exponential popularity in the upcoming days.
We’ve read about it, we’ve heard about it, we may even experience it. But what exactly is it, and how we recognize it?
Today I’d like to share my personal insights about Augmented Analytics.

Augmented Analytics is an approach that automates data insight by utilizing machine learning and Natural Language Processing (NLP) technologies. The ultimate goal of Augmented Analytics is to empower businesses to leverage more of their data to make better decisions faster. It embeds AI into BI to make the analytics work easier for users. It helps expert data scientists in focusing on specific problems, provides the most relevant actionable insights to decision-makers, and reduces the time spent on exploring data.

The main focus of Augmented Analytics stays in its assistive role, where technology does not replace humans, but supports them, enhancing our interpretation capabilities. It has significant potential to revolutionize the way companies create and use business intelligence. By utilizing Augmented Analytics, companies can streamline their data cleaning, compilation, and analysis. Ultimately, they generate actionable insights with a few clicks on a button.

Gartner predicts 'By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms, and of embedded analytics.' source


Why Augmented Analytics?

Business Value/ Impacts on Business:

Companies have long found it difficult to manipulate their data and apply it to their decision-making processes, and that’s where augmented analytics come in. Augmented analytics have the potential to impact businesses across a wide range of departments, and it will lighten the workload of data scientists, technical analysts, and anyone in data teams.

One of the real value of augmented analytics lies in bringing decision-making to a more intelligent level. This is the level where vital business decisions are made based on all of the available data, including real-time data, with the minimum possibility of human-made errors and bias.

When compared to traditional BI methods, the scope of augmented analytics also creates a qualitative edge by dramatically reducing the risk of missing essential insights into the data. Characterized by AI/ML automation of the insight discovery, exploration, explanation, prediction processes, significantly reduces time-consuming data handling.


Deeper Insights

Algorithms that learn by themselves are infinitely more insightful than those which rely on more structured rules because they adapt to the dynamic nature of data and can correlate between massive numbers of data metrics and sets. This advanced data use, manipulation, and presentation simplifies data, presents clear results, and gives you access to sophisticated tools that help business users make daily decisions with confidence. Users can gain real insight beyond opinion and prejudice, and respond to data quickly and accurately. With Augmented Analytics, managers and leaders can utilize their data more effectively, and use data to inform their strategic, high-level decisions.

The Democratization of Data

One of the greatest benefits of Augmented Analytics is making data accessible to everyone. In other words, democratize data, and make data more accessible to all even not technical business users who aren’t familiar with the ins and outs of data science. Augmented analytics leverage Natural Language Processing and Explainable AI to enable users with no data science experience to analyze and query data. This democratizes data literacy, extending analysis from expert data scientists to other professionals, coined in this context by Gartner “citizen data scientists.”
Are you familiar with the term “citizen data scientist”?
Gartner defines this as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. To sum up, Augmented Analytics promises to make more analytics and insights accessible from the data to more people in the organization.


Let's look at SAP Analytics Cloud Augmented Analytics features and capabilities,


Search to Insight

With conversational artificial intelligence, you ask questions about your data as easily as if you were asking your colleague. Natural language query instantly generates visualizations to get the information you need in no time.

Source & Learn more at Sap Analytics Cloud- Augmented Analytics


Smart Insights

Smart Insights lets you see more information about a particular data point in your visualization or table, as well as about a variance on your acquired data. When you look at a chart or table, there's always more to understand, and more to know about your data. With Smart Insights, you can explore beyond the data that is readily visible to you.
This feature understands the top contributors of specific data points without having to manually pivot or slice and dice your data. With the power of machine learning and augmented analytics in SAP Analytics Cloud, you can easily take action with your insights in less time.

Source & Learn more at Sap Analytics Cloud- Augmented Analytics


Smart Discovery

Identify key influencers and relationships in your data to discover how business factors influence performance. The Smart Discovery feature in SAP Analytics Cloud helps you to understand business drivers behind core KPIs and simulates the impact of strategic business decisions with machine learning technology.

Source & Learn more at Sap Analytics Cloud- Augmented Analytics


Smart Predict

This SAP Analytics Cloud feature helps business analysts answer questions about the future with predictive models created with machine learning technology.
Smart Predict augments your existing business intelligence capabilities by learning from your historical data to predict what is most likely to happen in the future. With patented classification, regression, and time-series forecast algorithms, Smart Predict creates high-performing and stable models to help you optimize operations and drive strategic decisions for growth.

Source & Learn more at Sap Analytics Cloud- Augmented Analytics

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