Smarter Analytics Dashboards With Automation and Machine Learning
An introduction to DZone's 2020 Data and Analytics Trend Report
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Like any other tool in software, analytics dashboards are abstractions. They allow data scientists, analysts, developers, and statisticians to make it as easy as possible for non-experts and peers to gain insights from data. This allows everyone to collect important data and business intelligence without having to put in the additional effort usually required for collecting those insights in the first place.
For those, like me, without a strong understanding of statistics, this means not having to pour over a textbook for a few months just to figure out if a trend is significant or not. For others, this means not having to repeat the same process of collection, processing, cleaning, and analysis to acquire the same insights.
Whether an end-user or data scientist, visualizing data allows us to better see trends, spot anomalies, and gain a richer understanding of datasets that hard numbers simply cannot.
However, getting to a place where a dashboard can be effective for all end-users, regardless of their understanding of data manipulation and analysis, is easier said than done. To help you avoid common pitfalls, make the most out of your datasets, and create robust visualizations, we’ve sought the opinion of experts in the industry to share their knowledge on what it takes to up your data analytics toolset in 2020.
In “Best Practices for Analytics Dashboards,” Dmitry Pashkevich emphasizes the importance of simplicity when building dashboards. He goes in depth on how analysts can create purpose-driven visualizations to ensure that end-users come away with necessary insights in an efficient amount of time.
Readers can then see Terence Shin put some of these tips into practice, as he walks through his thought process and methodology for creating a dashboard that focuses on telling an unbiased, data-driven story of the Coronavirus as of February 27, 2020.
Finally, Chris Lamb, in his article, “Priming Data for Machine Learning Applications,” discusses the dangers of organizations neglecting “data grooming.” He then expands on this point and shows readers how to ensure high data quality and availability throughout your organization.
To bring this all home, DZone’s Principle Research Analyst, Matt Leger, shares key research findings on analytics dashboards and where this technology is headed in 2020.
(You can find the full report here.) We hope you enjoy!
Opinions expressed by DZone contributors are their own.