If you are a Data Analyst or are on the path to becoming a Data Analyst, you are bound to make some mistakes. Everyone does that.
However, when you are a data analyst, there are some mistakes that might really mess up your data models and can often lead you to backtrack and put in immense effort to rectify those mistakes.
After all, with great powers come great responsibilities. And being a Data Analyst you hold an important responsibility to manage and analyze data and derive important insights from it.
The wrong analysis of data may lead to devastating results.
The worst part is that there are a plethora of such incidents where data has been interpreted wrongly and has led to some gloriously catastrophic results.
To avoid these, it is generally best to assume some safe constraints to help you minimize the scope of mistakes.
As a data analyst, there are many things are which are performed mistakenly and result adversely. So, here are given 10 common mistakes to avoid as a data analyst.
10 mistakes to avoid in data analytics
Well before we dive into the problems related to data analytics let’s revise a bit about what is data analytics.
The science of studying raw data to draw productive conclusions is known as data analysis. Many data analytics approaches and processes have been mechanized into mechanical processes and algorithms that deal with raw data and are intended for human consumption.
Data analytics is a broad phrase that refers to a variety of data analysis techniques. Data analytics techniques can be applied to any sort of data to gain knowledge that can be utilized to improve things
Data analytics is crucial because it assists firms in improving their performance. Companies can help cut costs by developing more efficient ways of doing business and storing big amounts of data if they incorporate it into their business strategy.
Now, moving on to the primary focus of this article, which is, the top 10 mistakes that Data Analysts often make, and why you should be avoiding them at all costs!
Here they are:
1. Difference between Correlation and Causation
To be correlated for two variables is a thing of chance but causation is more of logic. Often it is seen that data analysts fail to see this minor difference. The core premise of statistics and data science is that correlation does not imply causation, which means that just because two things appear to be related does not mean that one causes the other.
The inability to see the difference in both of these can be a grave mistake that you should always seek to avoid.
2. Ignoring key performing indicators
You’ll need to understand how to determine your key performance indicators if you want to receive outcomes from your data (KPIs). KPIs are metrics that show how far you’ve come toward your business objectives.
We’ve highlighted some of the key data items that make up KPI sets for various businesses below.
Marketing: Customer acquisition costs, new customers, a channel’s conversion rate, and so on. Average consumer spending, Customers who are no longer active
Retail: Percentage of sales made, Gross profit margin, The percentage of total stock that isn’t visible, Inventory turnover, average sales per transaction
Finance: Turnover of accounts receivables, Gross profit margin (GPM) a fast ratio The debt-to-equity ratio, or D/E ratio, is a Turnover of inventory, Cash flow from operations, capital for working capital, Return on investment
Medical Assistance: The rate of inpatient mortality, Cost per discharge on average, The rate of readmission, Operating margin, total Turnover of the beds, Receipt of cash instead of payment on a bad debt Denial rate.
If you don’t establish your KPIs beforehand, chances are, you might be running into a heavy fire when you actually start deriving baseless insights from your data.
3. Not knowing the problem well enough
This might be considered the tone of the most fundamental problem in data science. The majority of problems in data science originate because the problem for which a solution must be discovered is not specified adequately.
It would be a complete delusion to solve if you couldn’t adequately define the problem. One will thoroughly investigate the problem and assess all aspects, including stakeholders, action plans, and so on.
Make sure that as a Data Analyst, you go with the flow, though you may have a great experience in this field if the flow of work and strategy is broken then the desired results are not possible.
4. Lack of feedback to improve analysis
Your or your department’s analysis isn’t the end of it. To acquire full insights from your data, you’ll need feedback from everyone who is affected by it.
The most prevalent blunder is to believe that analysis ends once you have a report in hand.
Most marketers are taught to write reports, then get enthusiastic about the findings and want to share them with their superiors, but data analysis is never complete until the last person who needs it has had a chance to digest it.
Try to gain insights directly from the end consumer, and if that is not possible, then try to predict it
Feedback is important in the process of data analytics.
5. Not cleaning or normalizing data
Always presume that the data you’re dealing with is incorrect at first. Once you’ve gotten to know it, you’ll be able to “feel” when something isn’t quite right. To clean up your results, use pivot tables and other quick analytical tools to look for duplicate records or inconsistent spelling first.
Furthermore, not standardizing the data is an issue that can cause the research to be delayed. Most of the time, when you normalize data, you eliminate the units of measurement, making it easier to compare data from different places.
6. Biased sampling
Is the information in your analysis relevant to your target audience and company objectives? You could fall into the trap of sampling bias if you don’t pay attention to your sample.
It taints an analysis’s internal validity by causing inaccuracies in assessing relationships between variables. It can also impact an analysis’ external validity because results from a skewed sample may not generalize to the entire population.
When some population members are statistically more likely to be picked in a sample than others, this is known as sampling bias. In the medical world, it’s known as ascertainment bias. Because it jeopardizes external validity, particularly population validity, sampling bias reduces the generalizability of findings.
7. Not using appropriate graphs
Yes, even the type of graph that you use to represent data can lead the misleading results.
Deceptive graphs can be purposefully misleading, or they can simply be the result of somebody not comprehending the data underlying the graph they’ve created. The “traditional” sorts of deceptive graphs include those in which the vertical scale is too large or narrow, skips digits, or does not begin at zero.
The most common type of deceptive graph is one in which the Y-axis has been modified. Many people try to remove the zero from the Y-axis when comparing huge numbers to better display the differences between them.
8. Overestimating the accuracy of your model
Someone shouldn’t put too much faith in their model’s correctness to the point of overfitting it to a certain case. Machine learning models are created by analysts to refer to general scenarios. Overfitting a design can only make it work in a situation that is similar to the one being prepared for.
In this example, the model would fail miserably for any situation other than the training set.
9. Not having a clear objective
Setting up campaigns without a particular goal in mind will result in sloppy data collection, incomplete findings, and a disjointed, useless report. Asking a generic query like “how is my website doing?” will not delve into your data.
Continue your campaigns based on a single test hypothesis instead. Ask yourself specific questions to clarify your aims. Decide on your Northern Star metric and the factors you’ll be testing for, such as the times and locations.
Here northern star metric stands for good strategy. A North Star metric is the metric that most accurately predicts a company’s long-term success. A metric must perform three things to qualify as a “North Star”: generate income, reflect customer value, and track progress.
The importance of this northern star theory is paramount and leads from here to the main goal of data analytics. And distracting from it is also one of the mistakes often made by the data analyst which is the last on the list.
10. Losing track of your goal
Be the “one metric that matters” for your progress using Northern Star Metric. That is the single indicator that accurately gauges the performance you want to achieve. All additional parameters you keep track of will be linked to your north star.
So be careful not to get sucked into a sea of pointless vanity metrics that don’t help you achieve your main aim of expansion. Distracting yourself is simple, especially when you’re using various platforms and channels. Stick to the most important metric and only look at the metrics that have an impact on it.
These are the mistakes commonly done by the data analyst.
With little change in strategy, giving time to things can make things super easy and more productive in the desired way. Data analysis, in short, is not something to be done in haste but needs a different consideration and a relaxed environment to avoid such mistakes.