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What Makes a Good Dashboard: The Rise of Augmented Analytics

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  This article is part of the series: Productizing Data with People

A good dashboard is all we need — As a person working in the field of data-driven business, I strongly agree with the statement written in "One Thing to Maybe Build Now: A Great Dashboard".

How to build a good dashboard

Thanks to the recent advance of data analytics, building an intelligent "shiny" application becomes easier. In the business context, however, the end-users do not always have sophisticated data science background to understand what happens behind the scene and perform advanced analytics. Thus, as the article mentioned, a good dashboard is defined by how a UI helps (and even automates) an entire process of visualization:

Requires zero work from the dashboard consumer. The data is automatically generated by the app.

That would be awesome if we could build a meaningful dashboard consumed by the beginners with zero configuration. But, how?

Here, recent trends in Augmented Analytics can be a solution. On the Augmented Analytics applications, data is explained for / searched by citizen data scientists, who might be capable to perform basic analytics but do not have advanced skills, unlike professional data scientists.

To be more precise, Augmented Analytics is an automated BI for business people, powered by ML and Natural Language Processing (NLP). By deeply embedding these intelligent functionalities, the advanced BI guides citizens to extract accurate insights at ease. I personally believe this is a key focus area to build a "good" dashboard we need.

How Augmented Analytics changes a way of data analytics

As I learned from "Augmented Analytics and Data Discovery", the emerging technique widely applies ML/NLP to each of (1) Preprocess, (2) Discover, and (3) Explore phases in the data science lifecycle.

First of all, "What is Augmented Analytics and Why Does it Matter?" emphasizes the effectiveness of automating data preparation. Commonly speaking, most of professional data scientists' work is for data preparation, and the most time-consuming and boring part is not that simple as many people imagine; preparing data does require in-depth knowledge and experience in this field. Therefore, automating the least enjoyable task largely helps the citizens to move forward and focus on essential things in their work i.e., day-to-day strategic decision making.

Second, Augmented Analytics applications can automatically find out insights from data and notify users about them in an actionable form. Since there is no free lunch in the world, the auto-generated insights could be basic ones derived from classical ML techniques such as linear models and decision trees. But those outcomes can be very new to business people, and the biggest advantage is that we can obtain accurate results with less efforts since data is well-prepared by the previous step. Meanwhile, informing citizens about the ML-based insights is a challenging part as Gartner pointed out an importance of ensuring explainability of ML. Translating the insights to human-readable format such as texts and pictures is not straightforward, but this is definitely an area where we can leverage NLP.

Third, the citizens can proactively seek insights by exploring the auto-generated content via natural language and/or visual interfaces. For instance, according to "Augmented Analytics in 2020: Democratization of Analytics", two out of three Augmented Analytics interfaces are designed for search-driven analytics and visual analytics; NLP clearly makes data analytics more accessible for those who are not familiar with its scientific aspects, and interacting with visual representation gives a clear picture of what data tells us.

Dashboard + Augmented Analytics = <3

Augmented Analytics is an area where BI meets ML/NLP. We indeed need a good dashboard, but the practical discussion is clearly lacking today; letting a data science team create a report, and watching a bunch of colorful charts in your screen — This is not a smart way to run data-driven business.

As we've seen above, a majority of the dashboard consumers are categorized as citizen data scientists, and hence the assistance from ML and NLP techniques is strongly required to make the dashboards meaningful and effectively leverage data in their day-to-day activities. On that point, Augmented Analytics is the one that makes the conventional dashboard a "good" one.

  This article is part of the series: Productizing Data with People



Design Data Science & Analytics

  See also

Validate, Validate, and Validate Data. But, in terms of what?
Datavis 2020: A Free Online Course About D3.js & React
Rethinking the Role of Data Leaders @ Data Leaders Summit Europe 2019


Last updated: 2022-09-02

  Author: Takuya Kitazawa

Takuya Kitazawa is a freelance software developer, previously working at a Big Tech and Silicon Valley-based start-up company where he wore multiple hats as a full-stack software developer, machine learning engineer, data scientist, and product manager. At the intersection of technological and social aspects of data-driven applications, he is passionate about promoting the ethical use of information technologies through his mentoring, business consultation, and public engagement activities. See CV for more information, or contact at [email protected].

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