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Data Exploration for Business Users in SAP Analytics Cloud

Overview of Data Analyzer, Explorer, and Search to Insight

Introducing Data Exploration Tools

In today’s fast paced work environment, businesses face a multitude of problems that require a flexible, data-driven approach to decision-making to help steer themselves in the right direction and deliver business outcomes. Informed decisions enable improved forecasting of future outcomes, allowing organizations to predict and take on profitable opportunities. Unfortunately, many business users are faced with delays from IT processes or bureaucratic roadblocks, forcing them to wait lengthy periods for reports which results in reactive responses rather than proactive decisions.

Thankfully, SAP Analytics Cloud (SAC) provides extended functionalities to empower end-users with the ability to answer their ad-hoc questions on the fly. Even with limited data science experience, SAC's user-friendly interface enables users to quickly explore their data to derive key insights for optimized performance and profitability. To help you understand how SAC can help you transform your data into actionable insight and smarter decision-making, we will provide an overview of its key features including Data Analyzer, Explorer, and Search to Insight (S2i). Keep reading to learn more.

Data Analyzer

Data analyzer is a new data exploration tool in SAP Analytics Cloud that was released in 2021 and aims to provide a harmonized exploration experience by integrating S2I and Explorer capabilities. Currently, Data Analyzer is limited to creating tables but is rapidly improving. The key advantage is the wide range of connection options and the ability to save and share these insights (saved tables created in data analyzer). Data Analyzer can utilize live and acquired data sources from existing models or directly from sources without a model like BW BEx queries and HANA calculation views.


Data analyzer can be accessed in two ways: through the left-hand toolbar or within a story. Please note that you must enable Data Analyzer within the builder panel to access this feature in story view time. At this time, Data Analyzer is not supported in optimized view mode and is currently restricted to tables.

Use Case

Let us analyze the drink sales per region using data analyzer and send it to the team to help guide marketing decisions.

Step 1: Navigate to the left-hand toolbar and select Data Analyzer. Let’s select our existing sales model to derive our insights. Note*If connecting from data source, variables will need to be set.

Step 2: Open the designer panel to view the available measures and dimensions. Drag and drop the required dimensions, in our case, location. We will select quantity sold and gross margin as our measures. We can also apply filters at this stage, but we will ignore this as want an overview of all locations.

Step 3: Select the styling panel to edit the number formatting.

Step 4: Save the insight to share with the team. We have created an insight that is independent of any stories to answer our ad-hoc question.


Explorer allows you to investigate your data within a story through a wide range of auto-suggested visualization options that should be more familiar to users. However, unlike Data Analyzer, Explorer must be used within a story on existing models and cannot save independent insights. Although, these visualizations can be copied to pages to quickly build reports.


Explorer can be accessed within a report through explorer enabled widgets or the Data View in the top toolbar. Explorer is enabled for both designers and end users to better understand their data. Note that Explorer is unavailable in optimized view mode.

Use Case

Let’s build a time-series chart to understand the performance of our sales to keep track of any major changes for timely decisions.

Step 1: In edit mode, navigate to the Data View mode in the top toolbar. (If navigating via explorer enabled widgets, it must first be enabled in the designer panel)

Step 2: Select a model or dataset that has been associated to the story that you want to explore

Step 3: Choose the dimensions for your analysis and configure hierarchy presentation

Step 4: Select the measures and dimensions in which you are interested. In our case, lets look at gross margin across dates. Explorer automatically suggests and creates an optimal visualization. We can change to different visualization types or tables, but we will keep it as is.

Step 5: Most standard chart features such as reference lines and variances are available in more options.

Step 6: Let’s copy our widget to our story.

a. If you are using an explorer enabled widget, multiple explorer views can be created to investigate questions. These can be bookmarked or set as default for the specific widget.

Search to Insight

For an even simpler data exploration experience, users can leverage Search to Insight to ask questions in natural language (everyday language) and receive an auto-generated visualization. Search to Insight’s text-based interface does not require any structured languages and supports a variety of different question types. This reduces any barriers to creating visualizations and empowers all users to investigate their questions.


Search to Insight can be accessed in the home page through the "ask a question" search box or via the lightbulb icon in the toolbar. The lightbulb icon is also available in stories.

Use Case

Let’s determine our top region and assign our top manager.

Step 1: In the home page, select the lightbulb icon

Step 2: Let’s type the question “show top 3 sales managers”. S2i automatically generates a ranked bar chart to investigate gross margin.

Step 3: Type the question “Show Gross Margin by Location last year”. This creates a bar chart with a filter restricted to 2021. A log of previous questions are saved and each search result can be copied or exported. We have now identified the top regions and sales managers to share with the team.

Closing Thoughts

Hopefully by now you could see how by utilizing Data Analyzer, Explorer, and Search to Insight features, you can further explore reports by asking additional questions, confirm hunches and gain greater insight. Rather than waiting for the IT department to generate reports, Business managers can explore their own ad-hoc questions and become empowered to make better decisions. In the long-term product roadmap, Data Analyzer would integrate all data exploration features, but for now, utilizing all three tools offer a wide range of functionalities that can fit the requirements of most use cases. With this knowledge, we wish you all the best in exploring your data in order to guide your strategic decision-making processes! To learn more about exciting features within SAP Analytics Cloud, explore our blog or check out our learning options from Analysis Prime University to arm your team with the training they need to maximize the return on your SAC investment.


Author: Stefan Lim