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The obvious Getting started
Why are people misreading your data? Because it is uneducated information. That's your answer. Done. The end of the article. We can go home.
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Yes, it is true; Data analytics is still at a low level in many organizations, even those that are “data driven”. However, ours should not go home, but to stick and try to change that I present our information. We can only improve our data skills.
If you want to delve into how you wrap data into narrative, through plot, anecdotes, and visual appeal, check out this guide composing an impressive analytical portfolio. It offers practical tips for creating data stories that actually connect with your audience.
Knowing all this, we can be sure that our data understands the way we intended, which is true, which is the only important thing in our work.
The obvious Reason #1: You think logic always wins
It is not. People interpret data emotionally, through personal narratives, and care. The numbers will not speak for themselves. You have to get them to talk with little to no room for interpretation.
Example: Your chart shows sales have left, but the head of sales explains it. Why? They heard that the sales team was working harder than ever. This is an excellent example of cognitive ambiguity.
Fix: Before showing the chart, show this takeaway: “Despite more sales, sales fell for the quarter. This may be due to fewer customers.” It gives context and clearly provides a possible reason for the decline in sales. The sales team doesn't feel pressured to accept the cold reality of pitch sales.
The obvious Reason #2: You're relying on the wrong chart
A bright chart can grab attention, but does it present the information clearly and attractively? A visual representation is exactly that: Visual. Angles, lengths and key areas. If they are clean, the interpretation will be cleared.
Example: A 3D pie chart makes one budget category appear larger than it is, changing the revenue stream. In this example, the commercial slice appears too large for viewing, even though it is the same size as the hr slice.
Fix: Stick to using chart types that are easy to interpret, such as a bar, 2D pie line, or scatter plot.
In the 2D pie chart below, the size of the budget allocation is much easier to interpret.
Use good sites only if you have a good reason for it.
The obvious Reason #3: Convergence
You understand that connection is not the same as admiration. Of course, he did; You analyze the data. The same cannot apply to your audience, because that is often not the case in mathematics and statistics. I know, I know, you think the difference between connection and recognition is common knowledge. Trust me, it's not: The two metrics go hand in hand, and most people will think that one causes the other.
Example: A spike in Social Media Mentis of the Brand (40%) meets an increase in sales (19%) in the same week. The marketing team doubles the ad spend. But the spike was caused by a popular unpaid influencer review; Spending more money had nothing to do with it.
Fix: Label the relationship clearly with “connected,” “cuasa,” or “nothing proposed.”
Use experiments or additional data if you want to prove an observation.
The obvious Reason #4: You launch everything at once
People who work with data often think that the more data they put into a dashboard or report, the more sophisticated it is. That's not the case. The human brain does not have an unlimited capacity to process information. If you overload the dashboard with information, people will skip, miss important information, and miss the context.
Example: You can show six KPIS simultaneously on one slide, e.g
The CEO was prepared with a small DIP in NPS, mocking the meeting while completely losing a 13% drop in premium customer retention, a very big problem.
Fix: Have slides and slides: “One slide, one chart, one main point.” For example, in the previous example, would the carryover be: “Premium customer retention fell 13% for the quarter, mainly due to resource outflows.” This keeps the conversation focused on the most important issue.
The obvious Reason #5: You are programmed with precision
He thinks showing granular regression and mature numbers with six place values is more reliable than adding numbers. Basically, you think that more decimal places shows how complicated the calculation is. Wow, congratulations on that complexity. However, your audience will pick up on your round words, trends, and comparisons. Sixth decimal of precision? It's confusing. It's disturbing.
Example: Your report says: “Damage rate increased from 3.267481% to 3.841029%.” Wtff!? People will get lost and miss the fact that change is important.
Fix: circle the numbers and determine. For example, your report could say: “Rose damage rate from 3.3% to 3.8% – a 15% increase.” Clean and easy to understand change.
The obvious Reason # 6: You use visible words
If the terminology you use is vague words, or definitions, and vague labels, you leave the door open to multiple interpretations. The wrong one among those, too.
Example: Your slideshows are “final level.”
Whose custody or what? Half the team will think it's customer retention, the other half revenue retention.
Fix: say “customer maintenance” instead of just “maintenance.” Be strong. Also, whenever possible, use short and specific descriptions of the metrics you use, such as: “Customer retention = % of active customers this month who were active last month.”
You will avoid confusion and help those who may know what metrics they are talking about, but are not sure what it means or how it is calculated or how it is calculated or how it is calculated.
The obvious Reason #7: You are using the wrong content ranking
When presenting data, it is easy to miss the context and present data that is over-delivered or zoomed out. This can distort vision; Insignificant changes may seem important and vice versa.
Example: You show a 10-year income trend at a monthly planning meeting. Yes, kudos for showing the big picture, but it hides a smaller, more important picture: there is a 17% decline in the last quarter.
Adjust: Zoom in to the correct time period, eg six or 12 months. Then you can say: “Here's the money for the last 12 months. Note the drop in Q4.”
The obvious Reason #8: You are too focused on ratings
Yes, the ratings are good. Sometimes. However, they do not show distribution. They hide the extremes and, thus, the story behind them.
Example: Your report says that the average customer spends $80 per month. Cool story, bro. In fact, most of your customers spend $30 – $40, which means that a few customers who spend a lot push the average. Oh, yes, that campaign that made sales based on your report, targeting $80 customers. Sorry, it's not a job.
Fix: always show the distribution using histograms, box plots, or percentage breakdowns. Use Median instead of Mean, eg. With that information, the marketing strategy can be greatly improved.
The obvious Reason # 9: Re-cross the visuals
Too many colors, too many shapes, too many labels, and Legend sections can turn your chart into an invisible puzzle. Visuals should be attractive and informative; striking a balance between the two is almost a work of art.
Example: Your line chart that tracks 13 products (that's 13 lines!) over 12 months. Each chart has its own color. Three times a month, no one can follow one habit. In addition, he added data labels to make the chart easier to read. Well, you failed! The data labels initially resembled Jamie and Cezei Lannister – they were very close.
Fix: Simplify the charts. Show three or five categories, group the rest as “other.” Provide only relevant information; Not all information is worth seeing. Leave something for later, when users want to dig down.
The obvious Reason #10: You don't say what you did
Details are not the goal itself. It should lead to something, and that something. You should always provide recommendations for next steps based on your data.
Example: You show a Churn increase of 14% and end the presentation there. OK, everyone agrees high churn is a problem, but what to do about it?
Correct: you must write all the great insight into the practical recommendation. For example, say “Churn Rose 14% for the quarter, especially for premium customers. We recommend introducing a retention offer for this group next month.” With this, you have reached the ultimate goal of Data Storytelling – making business decisions based on data.
The obvious Lasting
As a data presenter, you need to be an amateur psychologist at some point. You have to think about the people you bring to him: their background, prejudices, emotions, and how they process information.
The ten points I mentioned show you how to do that. Try using them the next time you present your findings. You will see how illegal deductions are possible and your work becomes much easier.
Nate receipt He is a data scientist and product strategist. He is also a self-proclaimed educationalist, and the founder of Stratascratch, a platform that helps data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the job market, gives interactive advice, shares data science projects, and covers all things SQL.
