Figure 1 — Search to Insight
In a typical data analysis scenario, end users have to create a SQL query to interact with the data within the database or the analytics cube. However, it is not always feasible for business users to understand the nuances of SQL, resulting in incorrect analysis. The query is then passed on to IT who have to then build a report to cater to the particular scenario.
SAC leverages the features of conversational AI to provide the end users with a state-of-the-art natural language processing (NLP) interface to interact with the underlying data. This feature is known as “Search to Insight.” End users can converse with the underlying data models to get the required information as well as slice and dice the data without any knowledge of the underlying SQL. The Search to Insight feature further assists the end user by providing recommendations for models as well as dimensions within the query.
Search to Insight enables end users to reduce the IT effort and enable self-reliance for data analysis. Business users can form their own analytic queries, the results of which can be incorporated into a story or saved for further analysis.
Figure 2 — Smart Discovery
Once data is transformed and analyzed, the next effort is spent in building a visual representation of the data. Typically, a job for the IT team, the story building effort has to go through the process of iterations before the visual representations are available for the business teams to analyze further and operationalize. However, the need of the hour is to have a mechanism for the business teams to have readily available stories which can be plugged into the data rapidly. Not only is the effort saved considerably but also the time saved enables crucial business decisions to be made on time.
Smart Discovery is a unique feature of the Smart Assist portfolio which enables business users to create a whole story, without any effort in building it. The underlying machine learning algorithm scans the entire data set and provides the business user with the requisite information in the form of an overview page, key influencers, unexpected values, and simulation features. The only information required of the users is the selection of the measure. The entire contextual analysis over the available dimensions is carried out and presented by Smart Discovery.
Moreover, Smart Discovery can also be initiated within an existing story. The result can be either consumed in the existing story or can be discarded altogether.
Smart Discovery enables business users to utilize the self-service analysis capabilities of SAC to the fullest, resulting in considerable savings in the development effort.
Figure 3 — Smart Insights
Each dataset has multiple insights to offer. However, analyzing data is a specialized skill, often not within the skillsets of business users. Machine learning algorithms can rapidly scan through the available data and offer insights to enable the business users to extract maximum value from the available dataset.
This unique feature of churning the data is provided by SAC in the form of Smart Insights. Smart Insights can be enabled within a story and the additional insights provided can be consumed, in part or as a whole within the existing story.
Smart Insights is invaluable during the data exploration process wherein the business users can derive additional insights within the existing dataset. Machine assisted data discovery enables business users to discover hidden trends, resulting in informed business decisions.
Figure 4 — Predictive Forecast
Statistical methods offer the advantage of being able to predict business outcomes based on available data. Predictive analytics is the process of predicting future outcomes based on statistical methods and data mining techniques. Of the multiple methods and algorithms available, time series is a prevalent and popular statistical method for analyzing business trends and outcomes. A typical example for predictive forecast would be for business metrics like sales forecasting which typically follow a trend.
Predictive forecasting in SAC provides the business users with an easy to use interface to bring in linear and triple exponential forecasting into an existing business data analysis. Business users can use the existing dataset and apply the forecasting feature to arrive at decisions aligned with the predictive forecasting. Since the feature is available out of the box, comprehension of complexities of the underlying algorithm can be avoided by the business users.
Predictive forecasting allows the business users to align decisions to the machine forecasted values. This ensures that decisions are backed by solid data and algorithms and are not driven by hunches. Considerable saving in terms of analysis and prediction is saved by employing the ready to deploy predictive forecasting of SAC.
Figure 5 — Smart Grouping
Grouping of data as per specific criteria is a time consuming and difficult task. Business users who delve into the task of data analysis are thus faced with the dilemma of accuracy versus speed of analysis, especially if the grouping of data turns out to be incorrect. In the event of such a requirement, a machine assisted algorithm which would smartly group the data based on fixed characteristics would be very handy.
Smart Grouping enables creation of groups of data, especially while building cluster-based charts like scatter charts and bubble charts. The background algorithm skims through the data and creates clusters which can be incorporated by the users into existing charts.
9 minute read
Augmented analytics is defined by Wikipedia as an approach to analytics that employs the use of machine learning and natural language processing to automate analysis processes typically done by data scientists. Smart Assist is a collective group of features that enable augmented analytics to occur in SAP Analytics Cloud. The Smart Assist portfolio enables features across multiple aspects of data analysis, story creation, conversational analytics, and statistical methods. This article will explore each of these features in detail.
After reading this article you will be able to:
– Define augmented analytics and its role in data analytics;
– Describe each of the features available in the Smart Assist portfolio and how each feature can assist the end user in adding business value; and
– Understand how the Smart Assist portfolio aligns with SAP’s forward-looking and cloud-first strategy.
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