In the 1960s, some genius decided — “Hey, wouldn’t it be great if I had a platform that let me track all my data and KPIs in one place? Then I can make better business decisions rather than rely on luck.” And so the advent of the first decision support system came into being. This idea for digital dashboards led to early adopters of the modern business dashboard to be developed later (1980s). Then called Executive Information Systems (EISs), as with all new tech, things didn’t go quite as planned. There were problems with collating, handling and refreshing data, which led to incomplete and unreliable information being disseminated across too many disparate sources. It was clear that the ideas, while lofty and admirable, lacked proper approach and execution, thus missing the mark completely. It was the proverbial problem of putting the cart before the horse. So the tech went into hibernation until the 1990s and humans continued manually dealing with data.
As the information age continued growing at a faster rate, other tech — such as data warehousing and online analytical processing, (the ‘horse’) — got built to enable dashboards to function better. Fast forward to today, and the use of dashboards is second nature for large companies and forms an important part of their BPM (Business Performance Management). From Adobe to Tableau, if your company is not doing some form of BI and data analytics, you’re not going to last.
The Human Condition
However, despite the improvements made by leaps and bounds, the same problem essentially still existed. While the technology eventually caught up to be useful, the volume and velocity of data continued to rocket forward. It used to be all about buyer demographics, and while in many ways it still is, there’s so much more to add beyond just age, geographical location and spending power. We now talk about consumer behaviours — what time of day are they most likely to see, read or buy something? How do certain colours make people feel? The latest data points big players are now trying to collate are emotions, through emotion detection. Imagine being able to change your marketing dynamically, almost instantaneously, in response to your buyer’s pupils dilating. You won’t have to keep imagining for long — the emotion detection and recognition market is forecasted to reach $36.07 billion by 2021, up from $6.72 billion in 2016.
While data is winning the race in evolutionary terms, services, products and the economies of scale still exist to serve humans, not robots. Despite talk of robots taking over jobs, the current state of play is that we still use them mainly as tools for automation and to deal with volume. Take for example the Financial industry. There was plenty of talk about AI taking over the jobs of financial advisors — robo advisors. While to laymen, it conjures up the image of a robot in a suit sitting down and interacting with you, it couldn’t be further from the truth. A robo advisor is actually an algorithm-based software that allows for online wealth management, sans (expensive) financial planner. But when you’ve got a diverse portfolio and planning for your family’s future, it’s safe to say that as humans, we still prefer interacting with another who is more likely to understand and empathise with our needs and wants.
And it is this lack of understanding of the human condition that makes dashboards — while sexy in their prime — look like legacy systems compared to other offerings out there.
Time to get Visual
Don’t get me wrong, they’re still useful, but they shouldn’t be confused with actual reporting and other visualization tools available today. To illustrate this, let’s have a look at the visuals below:
Visual 1
Visual 2
Visual 3
It’s easy to realise that the standout is Visual 3, because it actually tells you straight off the bat what you’re looking at, whereas the first two (taken from dashboards and BI) still require some deciphering. Why is this important to note?
There are 3 things that users look for when using a tool or platform, and dashboards have not really addressed these:
1) Visualisation and storytelling — people look for authenticity and are visual. While clear efforts are made when we market a product or service, why should reporting or data analytics be any different? Imagine the clarity that everyone can receive when data is packaged and presented in a more approachable way. Which brings me to my next point.
2) Effective communication — as mentioned, Visual 3 provided information immediately, without the need of study. The whole point of gathering data and putting it into a report is to communicate insights, objectives and KPIs clearly, which in turn leads to informed decisions. Data can be complex and therefore, needs to be simplified as much as possible for clarity.
3) Ease of Use — and we come back full circle to the User Experience. Products, tools and services still exist to serve humans. The audience we communicate to are humans. What we can now do is use tech like AI (Artificial Intelligence) to automate the painful parts and make it easy. Yet several dashboards still require some form of technical knowledge or manual process to get the desired outcome.
It’s about Accessibility
While ease of use was listed as number 3 above, it’s not the least important. Studies have shown that our average attention span is 8 seconds. (If you’ve gotten this far into the article, chances are good you’ll read the rest; come on, you’ve got nothing to lose). As a business, if your product or service is hard to understand or complex to use, you’re in trouble because uptake will be slow. There are toys that teach children coding, and if you can’t ELI5 (Explain it Like I’m 5), people are more than likely to switch off. So accessibility through ease of use and understanding, is key on all fronts. It doesn’t matter how good the analysis is if the key decision makers (your audience) can’t understand it quickly. And the best way to communicate it is through visual languages that can be used to highlight key points in your analysis, and simplify data.
This is the biggest disadvantage that dashboards have. There are other factors as well, such as visual and output limitations, price points, and more, as illustrated below:
As different users have different skills and needs, it is important to have a BI environment that enables all users to interact with their data as needed. This should preferably be with a single solution that allows visibility across everything, rather than having multiple tools that typically increase your data inconsistency. And this should be done simply. Technology today allows for drag-and-drop functions, and they don’t have to cost an arm and a leg. It’s not a pipedream, it’s a reality and companies offering BI and old-fashioned dashboards are in for a rude shock if they don’t address it. As big banks have had to either embrace or keep up with young, FinTech companies, so too should data companies with smaller tech firms like ourselves.
There are those who would counter-argue that visualisation tools offer only a partial solution, and are not full stack. That “Data visualizations are, just as their name implies, tools used to visualize data,” and users need to work to make the data fit the software, as opposed to the software working for them to make data usable. This may be true for some companies, but not for Nugit. We don’t just take your data and try to make it look good, our visualisations actually highlight if your marketing campaigns are working for you based on the data collected, and make it presentation ready.
To round up the key points we have covered in this article, the purpose of data analytics and BI has not changed — to clearly see what’s been discovered and communicate it across to stakeholders for better decision making. But bearing in mind that consumers are the most challenging and disruptive factor, accessibility should be taken into account, and this is where most dashboards fall drastically short. If a piece of technology is hard to implement and navigate, it doesn’t matter how useful it is, people in general will be resistant to applying it, viewing it more as a bane than a boon.
At Nugit, we make ease of use a priority with simple drag-and-drop functions. By combining artificial intelligence, natural language generation, and visual design, we instantly transform your data into decision-ready reports that flow straight back into your workflow. Nugit does the analysis so you can focus on the strategy. Manual data processing is a thing of the past, as are dinosaur dashboards. High frequency visual reporting is the future.
