The answer to everything is the final statistic? Sure.
But is it right all the time? Not really sure about that.
It has always been psychological regarding what a person believes to be true. One might think from one point of view while others might have different point of views.
Besides psychological bias, there are other issues that might mislead the companies concerning their data analysis: Resources, filtering, processes, and visualization.
Many ways can interpret the data wrong to an extent which might have a very different affect on the business.
Here are some alarms to understand if the data is right or misleading:
Diverse Visualization
Sometimes, there is a chance to misinterpret the data based on how we see it. There are many possible reasons for such issues like difference in the scale of graphs.
Consider the same data on two different graphs with different scales. If the scale is large it looks like the data didn’t change at all. But if we look at the graph with small scale, it looks like the change skyrocketed.
This concludes that it can be really misleading based on how the data is visualized since there are many opinions and many points of view regarding a single data structure.
Issues in Processes
A single missing variable can cause the data to be flawed.
And when data is flawed, it leads to faulty conclusions and sometimes unwise investments.
To avoid such situations domain expertise and data expertise is much necessary. An imbalance in both domain and data expertise may lead to misinterpretation of data.
Finding Invisible Patterns Present
The human mind is a complex structure so when it comes to interpreting data insights, it can easily play tricks on itself.
It always sees pattern-making how it wants to see. We usually decode, simplify, organize and label to get a better understanding of the patterns.
Unlike the graphs, patterns which may or may not be present can be manipulated from various data sources: numbers, spreadsheets, code and charts.
It can be solved to an extent when the analyst keeps in mind that it is a pure data he is searching for, patterns may or may not exist in it.
Misunderstanding Correlations to Causations
It is important for a data analyst to understand that correlations are not causations. If we run correlations on two columns, they are going to tell us if they are moving in the same direction or inverse directions.
For example – If I have data for shoe sales and discounts on women footwear. Even if they show a positive correlation we cannot conclude that the discounts led to increase in sales of shoes.
Lack of Data
In some cases, we may not capture the actual factors of a replica. For example, if we don’t have our competitors data we might assume what seems to be an exact replica of the data. So when its not the same, it can put the company in jeopardy since the competitors still attract their target market.
Or in other cases, we might not have the data because we don’t have authorization to access the data. For example, some websites hide few web pages so their authorized users can only have access to them.
Wrong Timing
While measuring and analyzing data insights, timing counts for a lot.
If the business executives are too quick make their judgments regarding the data with a glance and not quite looking into the historical issues, then it may cause misinterpretation of data.
The data can belong to one month time period or one year time period, but if you look into it and make assumptions without taking the historical trends into considerations, there is a chance that the assumption might not make any kind of severe impact on the business.
Aggregation and Interactions
While building a statistical model, there are a lot of factors that must be taken into consideration. Among the bundle of campaigns a business makes, only one or two of them actually influence the target.
Since many factors are involved, there are chances that independent entities might get correlated creating a ruckus.
In such cases, we create interaction variables and analyse the combined impacts instead of one.
So, it becomes difficult to segregate how these factors affect the business individually for future references.
Wrong Averages
Averages are a messy concept.
A country’s economy is judged based on the GDP. So, if the country has a lot of poor people with a below poverty line net worth and still the GDP is not necessarily low. This may be because of those few rich people with their net worth at peaks.
This will mess up the whole ordeal of the country’s economy.
Applying the same concept to the data insights, they may get closer towards the company’s goals but individually, they need to be worked on.
How to get the Data Insight Truth
One of the best ways to get the data insight truth is to be aware of the series of false interpretations that are mistaken as reality.
Second is to utilize a dashboard that you are sure will deliver factual data. Appropriately visualizing and filtering will also minimize the chances of retrieving false data.
Do not make quick assumptions, make sure to check the historical trends before coming to a conclusion.
Remember, Not All Data Insights are False
However, it is not our intention to suggest that all data insights are false based on human visualization and interpretation. Excluding the human errors, that were taken into consideration in this blog, there are data insights which are not skewed in any manner.
Besides, if you are sure that the data you are analyzing is perfectly categorized and there is no chance of it being false, you can get a piece of reality.
