Analytics Dashboards: Some Say The End Is Near, We Say Not So Fast
When a year comes to an end, there’s always a tendency to look back at the days gone by to analyze what certain developments mean for a certain industry or sector.
So, as the curtains are set to come down in 2020, we at Express Analytics, too, plan to bring you a roundup of all the prominent developments of the year in the world of data analytics. But that will be in another blog post.
Today, though, we will look at a prediction made by none other than well-known research firm Gartner in late October and the intense debate it generated in the data analytics industry.
In “The Top 10 Trends in Data and Analytics for 2020” report, Gartner laid out the top technology trends that data and analytics leaders need to focus on as they look to make essential investments to prepare for a reset.
In it, it has virtually forecasted the end of “pre-defined analytics dashboards”, to be replaced with “dynamic data stories”.
Said the report: Dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration. As a result, the number of time users spend using predefined analytics dashboards will decline.
The shift to in-context data stories means that the most relevant insights will stream to each user based on their context, role, or use.
The Gartner team added that such dynamic insights would leverage technologies such as augmented analytics, NLP, streaming anomaly detection, and collaboration.
The report was followed up with a series of articles on Medium and on other blogs, much in the same vein – “Analytics Dashboards are dying”. Not that this lot was saying something entirely new.
For some time now, there have been similar predictions, on and off, claiming pre-defined analytics dashboards were “just not up to the mark”, and their end was near.
Ranged against such naysayers are those who continue to insist (a) analytics dashboards are here to stay (b) dashboards do not really have a suitable replacement for now (c) notebooks are the best alternates for dashboards (for now, at least).
When they first came into play, data analytics dashboards represented a leap forward from the staid (and cumbersome) spreadsheet. Suddenly, visualizing and reporting complex data using interactive and, dare we say, colorful dashboards was so much better than the average, one-dimension spreadsheet.
Why the Problem with Analytics Dashboards Now?
The way we see it here at Express Analytics, the foremost problem with analytics dashboards is context, or rather, the lack of it. The other is that “one dashboard does not fit all.”
Let us not forget that with the increase in data democracy, dashboards today are used by two distinct categories of users – the data scientists/analysts, and then the “non-technical” users like the Sales and Marketing teams.
But context is the paramount reason for the negative outlook on analytics dashboards today. Often, perspective is missing when dashboards are designed. Without it, a dashboard’s as good or as bad as your spreadsheet.
Here’s an example: If you have a dashboard that visualizes the number of clicks and likes got on your company’s social media channels, you need to put it in perspective in the same dashboard.
That perspective could be: how are the numbers in comparison to the previous month’s, or the same time last year, or how are they in comparison to your rival’s?
A poorly designed analytics dashboard seems to be the main reason for detractors predicting its death. Ease of use maybe the other.
Pre-defined or DIY Analytics Dashboards?
This brings us to the question – do DIY Analytics dashboards offer a better alternative to pre-defined ones? There’s no clear answer here, but we can offer some tips: everything depends on the requirements of the data project, the budget, and the profile of the end-users.
Out-of-the-box boards often come with some expensive license fees, but if you have a special requirement there are always boards you can design using open-source code like Python, for example.
But what’s even more critical is that the time has also come to move on from static analytics dashboards to those that provide actionable insights. Actionable insights cause action to happen rather than merely answer a question. Which means instead of the user spending valuable time to make sense of the visualization, actionable analytics dashboards can directly tell him about an insight.
At Express Analytics, for example, as part of our customer data platform Oyster, we are currently building a sentiment analysis and voice of customer analytics service.
Here, one of our aims is to develop an analytics dashboard that can directly tell the client that 40% of the negative reviews are for Product X because it’s over-priced. This, then, will serve as direct insight to the user which they can promptly rectify.
D3 stands for Data-Driven Documents. d3.js can thus become your preferred tool for data visualization.
Notebooks: Are They the Ideal Replacement for Analytics Dashboards?
For a couple of years now, data notebooks have been climbing the popularity charts, and some users have even started touting them as a replacement for the dashboard.
Editor of Memory Leak Astasia Myers describes them as: Notebooks are a form of interactive computing, in which users write and execute code, visualize the results, and share insights.
Notebooks are used by analysts and even non-technical users for exploration tasks. The versatility of a notebook has led to users demanding more by way of visualizations.
But data enthusiast Axel Christ while writing in Medium says notebooks still face some challenges that need sorting. “I believe that Analytics dashboards have their place in a business context and are here to stay. At the same time, I would love it if the foundation they were built on would be a notebook-style technology”, he concludes in the article.
As with any other technology, data analytics, too, is evolving. Analytics dashboards are certainly not the most optimal interface in visualization and reporting, hence, they cannot be the last word, even as data analytics gets more common-place and used by non-tech teams.
There’s a case then perhaps for two types of analytics dashboards – one for the analysts and the other for those who are not. Market evidence certainly supports this view.
In the meanwhile, if notebooks or any other form of data visualization tool come online that helps democratize access to data, even more, they could evolve and replace dashboards eventually. But for now, dashboards are not going anywhere. Perhaps insisting on adding context to the dashboards and making them easier to use could lead to more adaptability.
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