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Recent breakthroughs in analytics and business intelligence are changing the way business users and data scientists are able to manipulate data and retrieve valuable insights that inform important business decisions. Not only is it easier to access and structure data without data science knowledge or training, users are now even able to use natural language to gain deeper insight into complex data landscapes. Through smart features like machine learning, artificial intelligence, automation and natural language querying, everyday business users are more empowered than ever in achieving true business intelligence.

In this blog we’ll look at the next generation of business intelligence and analytics, with suggestions of SAP solutions pioneering the latest breakthroughs.


Business intelligence (BI) is a technology-driven process for analysing data to retrieve actionable insights that help business users make more informed business decisions. The BI process entails collecting data from different sources, preparing it for analysis, running queries against the data, and creating data visualisations/BI dashboards/reports displaying the analytical results. To achieve the goal of business intelligence, a combination of data management, analytics and reporting tools is required.

BI data can include historical data and real-time data to support strategic and operational decision-making processes.


Augmented analytics, sometimes referred to as “smart features”, incorporate machine learning and artificial intelligence technologies and techniques, including natural language processing and automation. These features improve data access, suggest data sources and analyses, uncover latent insights, predict future outcomes and even suggest actions. The ultimate goal is to drive better decision-making in organisations.

In summary, augmented analytics is computer-enabled analytics that automate processes, provide calculated suggestions, and predict future outcomes to complement and enhance human insight.


According to a recent report on the impact of augmented analytics on business intelligence conducted by Constellation Research, the following augmented capabilities are emerging:

  • Augmented data prep, including automated data profiling, guided cleansing and formatting steps, recommended data sources and assisted joins, and assisted data-enrichment steps and sources.
  • Augmented discovery and analysis, including recommended insights, recommended visualizations, key-influencer/driver analysis, and intent-driven recommendations based on content/source popularity or more-sophisticated analysis and learning of behavior patterns by user, group and role.
  • Natural language interaction includes both NL generation and NL query. NL generation adds detailed textual descriptions to KPIs, charts and dashboards to provide additional context and improve human understanding. NL query supports data exploration and analysis through typed-in (or speech-to-text translated) questions rather than SQL code.
  • Automated trending, forecasting and prediction features start with simple push-button trending and forecasting features that can be harnessed by data-savvy analysts. More advanced are automated machine learning and predictive modeling features.

Here’s a closer look at each capability and its impact on business intelligence.


Data prep is a necessary step to ensure that data is cleansed and in the correct format for analysis. The self-service capabilities these features enable make it easier for mainstream users to prep data without the help of a data scientist.

Augmented data prep features include:

  • Auto data profiling: automatically lists the measures and dimensions in the data set (addresses, ZIP codes, dates, currency values, product names, etc.) and for each field statistically and/or visually details the number of records, range of values, average value and quality stats, such as the percentage and number of missing or exception values.
  • Guided formatting and cleansing: guides the user in standardised data formatting and repair or delete exception values, and flags potentially sensitive information such as Social Security or credit card numbers .
  • Recommended sources and guided joins: automatically suggests statistically related or frequently combined data sets or tables and data sources and guides the user on how to join such data based on observed behaviour and guided workflows.
  • Assisted data enrichment: automatically suggests data enhancements like assigning values, hierarchies etc, to enrich the value of a selected source.


Exploring and finding trends, correlations and patterns in data is made easier through the following augmented features:

  • Recommended insights: Using machine learning and automated discovery techniques, data correlations, patterns, trends and outliers in data is highlighted to the user.
  • Recommended data visualizations: automatically recommends best-fit data visualisations for selected data. For example, bar charts, scatter plots or map views based on specific data and data relationships.
  • Key influencer/driver analysis: automatically highlights notable data points like marked increases or decreases and suggests relevance-ranked attributes that may have influenced the value change.
  • Intent-driven recommendations: automatically recommends content, data or insights based on popularity or learned behaviour.


Natural language interaction enables non-data scientists to use everyday business terminology to ask questions about data, instead of having to learn query languages like SQL or Python.

The following augmented trends in natural language interaction are making it easier for users to retrieve meaning from data:

  • Natural language generation: automatically interprets data using underlying metadata and offers background context or analysis through textual descriptions.
  • Natural language query: supports custom queries using natural language and dictionaries for synonyms and company-specific terminology to drill down to deeper insights.


Automated predictive capabilities rely on built-in regression and classification algorithms to extrapolate historical trends into the future. These features can be used to deliver suggested actions based on previous outcomes.


The augmented analytics capabilities listed above are all available in SAP BusinessObjects BI, an enterprise reporting and analytical business intelligence (BI) software solution. Comprising a variety of supporting applications that simplify the reporting and analytics process, business users are enabled to search for data, execute analytics and generate reports with ease, and without the help of data analysts. Using the simple drag-and-drop functionality, data from different sources can be integrated into one platform for a single source of truth.

A number of valuable reporting and analytics applications work with SAP BusinessObjects BI to help streamline these functions. Here are a few examples:

  • SAP BusinessObjects Dashboards: an application tool for data visualisation that allows users to develop customised dashboards using the reports. It includes gauges, interactive charts, and widgets.
  • Web Intelligence and Webi: this web-based browser is used for analytical and ad hoc reporting.
  • Crystal Reports: another powerful reporting and data analytics application that helps in the creation of dynamic reports.
  • SAP Lumira: an application tool that helps business users find data and develop customised dashboard and analytics applications.
  • SAP BO Explorer: similar to SAP Lumira, this application also enables users to find data from different sources and create data visualisations.
  • Query as a Web Service (QaaWS): A useful application for developing and publishing web services.

The SAP BusinessObjects Universe is the semantics layer between the database and the user interface that enables users to use natural language to find and visualise data.

Find out more about SAP augmented analytics and related software solutions from MDSap, experts in all things SAP-related.