Top 13 Augmented Analytics Features of BI Tools

Post by 
Philip Lima
Reading time: 3 min

(This is a multipart series about how to evaluate and select the right BI tool for you and your team. To see the entire outline of the series and a framework for how to evaluate a BI tool, see How to Select the Best BI Tool for You.)

Like shopping for a car or a new electronic device, there are likely a lot of features that the average Jane won’t need. We aim to cover the breadth of features that the top tools and analysts in the market are talking about.

It’s important to focus on what’s right for you and your use case. We recommend buyers be careful to not let features creep onto their “Want to have” or “Mission critical” lists when doing your research. It can be really easy to learn about a cool feature and then think about all the cool and creative ways you could use it. Curiosity and creativity is to be human.

Remember: You started shopping to fill a specific need. Keep that front and center as you research while getting educated on other cool features and how you may use them after your initial set of requirements are delivered.

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Here are the top augmented analytics features of BI tools to consider as part of your evaluation.

1. Augmented Data Preparation

The solution leverages Machine Learning (ML) automation to augment data profiling and data quality, harmonization, modeling, manipulation, enrichment, metadata development, and cataloging. It includes capabilities like automated matching, joining, profiling, tagging and annotating data prior to data preparation, sensitive attribute recognition, automating repetitive transformations and integrations, data quality and enrichment recommendation.

2. Autogenerated and Analyzed Segments or Clusters

The solution leverages Machine Learning (ML) to find new segments or clusters in a dataset automatically.

3. Autogenerated Forecasts or Predictions

The solution leverages Machine Learning (ML) to create a forecast or prediction automatically.

4. Automated Algorithm Selection and Model Tuning

The solution leverages Machine Learning (ML) to automate the process of selecting the appropriate algorithm to fit a given use case. It also automatically adjusts the hyperparameters of an algorithm to improve accuracy and optimize the predictive model.

5. Automated Anomaly Alerting

The solution leverages Machine Learning (ML) to offer automated alerting, notification, or proactive display of outliers/anomalies based on changes in the data. This feature is not a manual threshold-based alerting or visual depiction of statistical outliers within a dashboard object.

6. Automated Descriptive Insights

The solution leverages Machine Learning (ML) to automatically present basic insights such as variances, associations, correlations, or trends from a column or dataset before a content author begins actively exploring the data. These insights are typically displayed as a brief natural language description or sample visualizations.

7. Automated Feature Generation or Selection

The solution leverages Machine Learning (ML) to automatically identify the best types of data or variables to be used as part of the predictive model building process.

8. Automated Model Monitoring

The solution leverages Machine Learning (ML) to automate tracking the performance of models in production to ensure the relationships found during development are still valid, and that the model is predicting well.

9. Automated Model Packaging or Deployment

The solution leverages Machine Learning (ML) to enhance the ease and speed with which the user can move models from a development environment to a production environment or embed them in a business process. It automates the ability to create APIs or containers (such as code, PMML, and packaged apps) that can be used for faster deployment.

10. Contextualized or Relevant Insights

The solution leverages Machine Learning (ML) to automate insight generation that incorporates explicit and/or implicit usage and feedback data from users to display content that is most relevant for their role and use cases.

11. Key Driver Analysis

The solution leverages Machine Learning (ML) to automatically identify the most important key drivers or attributes of a given metric in a dataset.

12. Text-based Natural Language Search

The solution offers a text-based search interface to search through the data using natural-language statements in the text.

13. Voice-based Natural Language Search

The solution offers a voice-based search interface to search through the data using natural-language statements in voice.

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Follow the links above to read more about core functional requirements of a BI and analytics tool.

Did we miss something? Drop us a line and we’ll see about getting it added.

Need help with your BI tool selection? Book a call with us and we’ll see if we can help. If we can point you in the right direction with a short phone call, great! If you’d like to hire us to do an evaluation and selection for you, contact us and we’ll make a selection together.

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