The Good, the Bad, and the Beauty of Analytics
A quick guide on how to effectively present analytics.
Ever imagined finding gold? (Let’s imagine…)
“Hooray! There it is… a pot of gold!”
Your next immediate thought might be…
“Alright, I got it, now what? How should I spend it? More importantly, how should I spend it calculatedly?”
Similarly, in this modern world, data is the pot of gold. This article focuses on how to calculatedly use data to provide fruitful information in the form of analytics. It should be presented in such a way that it is understandable for the end-user and helps them make a decision or take action.
Making a decision based on analytics is not new. This method has been evolving for ages, be it predicting an emperor’s next attack based on his previous conquers (Hmm, ok.. that’s too historical). How about predicting the financial situation of an organization? (Alright, that sounds better), or even looking at possible busy hours on the road to plan a trip.
What is one common outcome of analytics in any field? — It helps in Decision Making.
That said, analytics should be effective and understandable for the end-user to make a decision.
There are several people involved in the process of providing analytics: the product team, design team, data analysts, business experts, etc.
What should the product and design team keep in mind while providing analytics to the end-user?
Let us go over a few examples to know more.
The Good
A few years ago, while I was digging for information about analytics, I was thrilled to know more about its types and uses (shown in the illustration below).
Analytics is not just a tool to bring monetary benefits, business, or information for convenience; it is also about saving lives, or even a community. The current pandemic is a great example (Never mind, let’s not go there. You might be already tired of hearing that over and over again). Looking at the other benefits, I was impressed by how the healthcare industry uses Predictive Analytics to control asthma and suicidal incidents, just to name a few.
What did the healthcare industry do?
For instance, (based on an article by McKinsey & Company),
Asthmapolis created a GPS-enabled tracker that records the inhaler usage of asthma patients. Data with varying trends are then merged with known asthma catalysts, such as high pollen counts in a location during a particular period. Together, predictive analytics helps physicians develop personalized treatment plans and spot prevention opportunities.
1st TIP — For the product and design team
Data is sensitive. Realize the cause and effects.
Your intentions might be good, but what you are presenting should still be handled with utmost care. Provide the type of analytics for the user to take the next possible action more strongly. Be very cautious in what you are presenting, and do not go for Prescriptive Analytics if the data doesn’t provide enough justification. Go for Predictive Analytics only when you can provide supporting data that also mentions facts allowing the user to make a confident decision.
The Bad
Usually, too much of anything is bad. In this data-rich world, where you can obtain information about anything anytime, one should act intelligently to make use of it.
Online retailers are one example worth mentioning where they use analysis to prescribe you something based on your past search data and help you out of bad shopping experience.
With thousands of items in the display, narrowing down your search with recommendations not only saves time but also provides you with the best possible products, which are based on your search and profile data (hmm… once you are out there online, forget about privacy. oh wait.. I am not saying that no privacy is the “bad” thing, I am talking about too much of data is bad ;) )
2nd TIP — For the product and design team
Too much data and poor insight can be pointless.
When there is too much information on display, the user tends to lose interest and confidence. To avoid this, help the user by providing filtered insights based on data.
Help the user digest the data information and tell stories through analytics by curating data into a form easier to understand.
Use data visualization — such as charts, graphs, and maps to provide a way to understand data patterns and trends. Note that whenever a graph or chart is involved, keep it simple and straightforward to get the point across. Do not over complicate it, it will bring down the whole purpose of using the visualization.
The Beauty
With enriched knowledge, improving technology, and unlimited resources, organizations should make use of them to drive their outcomes efficiently. The beauty of analytics is when effective data analysis, along with business knowledge, is combined to provide actionable recommendations.
Different types of analytics can be used to cater different types of user problems (refer to the illustration below). No matter what organization it is, this categorization is universal, making it more helpful for the organization and the end-user.
3rd TIP — For the product and design team
Make use of the technology. Plan it well.
Each of these will affect what type of analytics you provide:
1) Audience — Know the end user.
2) Goal — What you are trying to tell the user.
3) Information Availability — What you have in hand.
4) Story — Tell a story to achieve the goal.
Analytics is everywhere, whenever you see a chart or graph or whenever people talk about a trend or pattern, analytics is involved. People are easily attracted to summaries rather than reading pages of raw, lengthy information. Through this article, I not only wish to provide tips to the product and design team but also to bring awareness to the end-user on what they see daily, and how it helps them understand a certain situation.
Now…
Analytics Providers (the product and design team):
Before you provide analytics to the user, think about the core values or goals, the cause and effects, and usefulness.
Analytics Consumers (end users):
Do not blindly grasp what you see. Ask how and why something is shown to you by looking and asking for more facts.
Analytics is powerful; it is a double-edged sword. Let us handle it with care by bringing out the best of it.
