At a glance
- Augmented Analytics automates parts of data analysis using AI and machine learning.
- The main goal is to enable users without deep statistical knowledge to gain insights from data on their own.
- Core functions include automatic pattern recognition, anomaly detection, and explanatory analyses.
- It complements classic business intelligence but does not replace it.
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Augmented Analytics definition
Augmented Analytics is a term coined by Gartner that describes analytics systems which use machine learning and natural language processing to support or automate data preparation, analysis, and the interpretation of results. The human remains at the center but is relieved by the system of recurring and technically demanding tasks.
At its core, the aim is to shorten the path from raw data to actionable insights. Instead of analysts manually testing hypotheses, the system itself suggests notable patterns, outliers, and drivers and explains them in an understandable form.
| Analysis stage | Description | Role of the human |
|---|---|---|
| Descriptive analysis | Describes what happened in the past, for example through reports and dashboards | Interprets data largely manually |
| Diagnostic analysis | Investigates causes and relationships of variances | Searches for explanations supported by tools |
| Augmented Analytics | Automates data preparation, pattern recognition, and explanation using machine learning and NLP | Is supported by automation, decides and validates |
| Predictive/Prescriptive Analytics | Forecasts future developments or recommends actions | Uses model results as a basis for decisions |
„The automatic explanation of variances has noticeably shortened our monthly analysis. We see faster what matters and discuss measures instead of searching for causes.“Head of Controlling
Industrial company
Why is Augmented Analytics relevant?
The volume and complexity of available data increasingly exceeds the capacity of classic, manual analysis methods. Augmented Analytics addresses this gap by automating the laborious search for relevant relationships, thus enabling business users without a data science background to carry out well-founded analyses. For controlling, this means shorter response times, broader use of data across the company, and relief for specialized analysis teams.
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Augmented Analytics and artificial intelligence
Augmented Analytics and AI are often equated, but they are not the same. Artificial intelligence is the overarching field of methods that replicate human-like cognitive capabilities.
Augmented Analytics is a concrete application of these methods in the context of data analysis. AI provides the methods; Augmented Analytics puts them at the service of a business analysis process and makes their results usable and comprehensible for users.
| Characteristic | Augmented Analytics | Artificial intelligence |
|---|---|---|
| Nature | Concrete application in the analysis process | Overarching field of methods and techniques |
| Main goal | Support and automate the analysis process | Replicate human-like cognitive capabilities |
| Scope | Data preparation, anomalies, explanations, natural language output | Learning, reasoning, and recognition methods of all kinds |
| Role | Puts AI methods at the service of data analysis | Provides the underlying methods |
| Result | Explained insights and action guidance in the work context | Models and capabilities for a wide range of purposes |
Augmented Analytics vs. Predictive Analytics
Predictive Analytics uses historical data, statistical methods, and machine learning to predict future developments and events. The focus is on the question: What is likely to happen?
Augmented Analytics goes beyond this: using AI and machine learning, data analyses are automated, patterns are recognized, and insights as well as recommended actions are generated. The goal is to support users along the entire analysis process.
Conclusion: Predictive Analytics focuses on predictions, while Augmented Analytics intelligently supports the entire analysis process. Predictive Analytics can be a component of Augmented Analytics.
| Feature | Augmented Analytics | Predictive Analytics |
|---|---|---|
| Main goal | Support and automate the analysis process as a whole | Predict future values or events |
| Scope | Data preparation, anomalies, explanations, natural language output | Individual forecasting models for defined target figures |
| User group | Broad range of business users without data science knowledge | Analysts and data scientists |
| Result | Explained insights and action recommendations in the work context | Probabilities and forecast values |
How does Augmented Analytics work?
Augmented Analytics typically goes through several stages in which AI methods accompany the analysis process:
Prerequisites for Augmented Analytics
For Augmented Analytics to be effective in the company, some functional and technical foundations must be in place:
Application examples
Augmented Analytics is used wherever large volumes of data need to be interpreted quickly and comprehensibly. Typical examples are:
Where would you like to use Augmented Analytics?
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Augmented Analytics with Bissantz
For years, Bissantz has been developing methods that support users in the analysis of business data in an automated way. These include the automatic detection of anomalies, the identification of value drivers, and the comprehensible presentation of analysis results directly in reporting.
| Function | Benefit |
|---|---|
| Automatic anomaly detection | Relevant variances become visible without users having to search through every metric manually. |
| Textual explanation of figures | Results are translated into words and are understandable even without prior analysis knowledge. |
| Integrated visualization | Condensed, intuitively readable displays accelerate interpretation. |
| End-to-end analysis process | From data preparation to insight, a consistent, traceable workflow emerges. |
„Our business departments can now ask questions of the data themselves and receive understandable answers. This relieves IT and significantly increases acceptance of the analyses.“BI Manager
Retail company
Frequently asked questions
What is Augmented Analytics?
Augmented Analytics refers to the use of machine learning and natural language processing to automate steps of data analysis. This includes data preparation, pattern recognition, and the explanation of results, so that even business users without specialized knowledge can gain well-founded insights.
What is the main goal of Augmented Analytics?
The main goal is to support and accelerate the analysis process by automating time-consuming manual steps. This allows more users to reach relevant, explained insights faster and more reliably.
What is the difference between Augmented Analytics and AI?
AI is the overarching umbrella term for methods that perform human-like cognitive tasks. Augmented Analytics is a specific application of AI methods in the field of data analysis that supports people in evaluation and interpretation rather than replacing them.
How do Augmented Analytics and Predictive Analytics differ?
Predictive Analytics focuses on predicting future values or events. Augmented Analytics covers the entire analysis process and includes data preparation, explanation, and natural language output, but can incorporate forecasts as one component.
What prerequisites are needed for Augmented Analytics?
The foundation is a reliable, well-integrated data basis, clear governance and data quality, as well as business users who can put results into context. The better the data basis, the more robust the automated analyses.
Who is Augmented Analytics intended for?
Augmented Analytics is aimed at a broad group of users, from controllers to business departments to decision-makers. Since many technical steps are automated, even users without data science knowledge can work with data.
Summary
Augmented Analytics describes the use of AI and machine learning to support and automate data preparation, analysis, and interpretation. The main goal is to make well-founded insights accessible to business users as well and to shorten the path from raw data to decisions. Unlike predictive analytics, which focuses on forecasts, Augmented Analytics is an overarching approach that concerns the entire analysis process.
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