The advancement of science in the fields of machine learning and deep learning has brought several benefits to businesses around the world, and data analytics is one of them. Business leaders have been relying on these advanced tools and software to make informed decisions as they implement new strategies to aid business growth.
The key is to analyze your data correctly. Unfortunately, some minor mistakes can impact your business negatively, causing you to deviate from your desired business outcomes. But you can avoid this if you are aware of the common data analytics mistakes analysts often commit and take steps toward better decision-making. To help you out, we have listed some of them below.
1. Using Unreliable Data
Using incorrect or unreliable data is one of the biggest mistakes you can make in data analytics. And unless you’re paying attention, you may end up working with this data and producing insights that don’t have any value for your business.
Before starting with data analysis, make sure to confirm that the data you are using is complete, consistent, accurate, and up-to-date. Moreover, if you are using data from multiple sources, chances are they might use a different format. Therefore, ensure you standardize the data format before working with them. This will help you achieve accurate results.
2. Not Having Clearly Defined Objectives
The main goal of data analytics is to examine data and come up with possible outcomes or solutions to particular problems. But, a common mistake that data analysts make is to analyze data without first determining a clear goal or objective. And if you aren’t sure what you want to achieve, you are simply wasting time.
Begin your data analysis with a clear goal, and work towards it. It’s essential that you fully understand the objective before you begin. This will ensure you get valuable and actionable insights related to the business processes you are targeting.
3. Reporting Numbers with no Context
Your data analysis report shouldn’t only contain pages and pages of figures and charts. You need to provide some context as well. Otherwise, the people you report to might have trouble interpreting the results.
For example, instead of simply showing the numbers and saying that sales figures have gone up, you can elaborate on the success of specific campaigns, product and service initiatives, expansions, and marketing efforts that have helped increase conversion rates. Similarly, you can also point out the strategies that have met with only little success.
Ideally, your report should be paired with recommendations on how you can move forward with ongoing campaigns. This will help management and all key decision makers get a clear picture of what’s working and what’s not, enabling them to make the right decisions for achieving future goals.
4. Not Looking at Relevant Timeframes
If you are analyzing metrics like sales volume or conversions, you need to choose a relevant timeframe. For example, a period of a week can be too short to judge the success of any campaign accurately. On the other hand, it may so happen that you have five paying customers in one week and over 50 the next. Therefore, choosing a relevant timeframe is key to gaining accurate business insights.
5. Mistaking Correlation for Causation
If you observe two metrics moving up or down at the same rate, it is easy to think there is a correlation between them. You might even conclude that one is causing the other. But this may not always be the case. Sometimes, it may happen that even though the rate of increase or decrease of certain metrics is similar, the correlation may be caused by some other factors or even be purely coincidental. In such cases, mistaking it for correlation or causation will only give you incorrect outcomes.
While independent variables affect dependent variables, you first need to identify whether there is any relationship between the two metrics you are analyzing. Research thoroughly on the relevant context – this will help you arrive at the right conclusion.
6. Not Considering Seasonality
Another mistake data analysts make is failing to factor in seasonality in their data. Depending on the industry, there are some specific days or months when your sales figures are bound to increase, while you may experience a lower-than-average conversion rate at some other times of the year. Therefore, it’s a good idea to analyze data from past years to identify similar periods of high and low sales and make decisions accordingly.
7. Wrong Selection of Visuals
It’s a good idea to present your data as visuals to aid clear understanding. But a common error data analysts make is choosing the wrong type of visualization for showcasing their data.
For example, if you are tracking the rise and fall in sales patterns for each month of a given year, presenting it as a line graph will make it easier to understand than using a pie chart. In addition, you can get creative with your visualizations to make them easier to understand at first glance. For example, use different colors to highlight changes or present different variables.
8. Using Small or Biased Data Samples
The data sample you are analyzing needs to represent your total customer base or, sometimes, a specific group. Working with small or biased data samples may provide some insights, but it won’t represent the needs, interests, or behavior of your target audience. Implementing sales or marketing strategies and campaigns based on such insights is unlikely to provide you with the success you envisioned.
The right approach here is to first write down the demographics of your target customers and make sure that your data sample matches these. This will ensure that you are working with the correct data set.
9. Not Measuring Traffic Generated by Different Channels Separately
Another common mistake businesses make while analyzing data is looking at website traffic statistics as a whole instead of going into the details. Not all your visitors come to your website through the same channel. Some may find your page on search engines, while others may land there from an email link or a social media ad.
You need to break down the percentage of traffic generated by each channel to identify which one is doing well and which one needs more work. These insights will help you focus your efforts in the right direction, saving both time and resources in the long run.
So, these were some of the most common mistakes businesses make while analyzing data. Try avoiding these to ensure the best outcomes for your business. With the right approach to data analysis, the right tools, and some professional assistance, you can not only make better decisions to grow your business further but also create a much improved customer service experience.
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To learn more about data analytics check out our frequently asked questions below.
How can you avoid errors In data analysis?
Data analytics tools use deep learning models to analyze data and make accurate decisions. But even the smallest mistakes can negatively impact the outcome. To avoid this, you need to ensure that the data you are working with is correct, up-to-date, complete, and relevant to your objectives.
In addition, you need to use the right data set to gain insights on a variety of machine learning projects, such as sales analysis, customer behavior predictions, and more.
What are some common mistakes committed while analyzing data during business research?
Businesses depend, to a great extent, on data analytics to make meaningful decisions regarding upcoming strategies. But, often, the outcomes differ due to minor errors during analysis. Some of the most common mistakes analysts make while examining business data include the following:
Using unreliable data
Not clearly defining goals and objectives
Reporting numbers while providing no context
Not choosing relevant timeframes
Mistaking correlation with causation
Not factoring in seasonality
Using the wrong type of visualizations for presenting data
Analyzing small or biased data samples
Not measuring the traffic generated by different channels separately
What are the 5 types of data analytics?
There are different types of data analytics that can provide insights into distinct business needs. And one defining characteristic of a capable data analyst is identifying the right data analytics method depending on the answers they are looking for. The 5 main types of data analytics include:
Descriptive Analytics: Descriptive analytics is among the most common analytics tools businesses use worldwide. It helps you gain insights related to what is happening with your business.
Diagnostic Analytics: Diagnostic analytics help answer questions related to why something is happening in your business.
Predictive Analytics: Predictive analytics help you analyze past trends to predict future changes or outcomes.
Prescriptive Analytics: Prescriptive analytics help determine the best way to respond to future changes.
Cognitive Analytics: Cognitive analytics combines advanced technologies to apply human-like intelligence to solving specific tasks or making decisions.
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