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November 13, 2020

Data Analytics Solutions – How To Avoid Mistakes


Artur Haponik

CEO & Co-Founder

Reading time:

8 minutes

Nowadays, data science services (especially data analytics) plays a crucial role in every big company. This discipline allows you to analyze, process, and model data your organization processes. Thanks to data analytics solutions, companies can interpret the results and create actionable plans and insights. However, many dangers lurk along the way. What are the most common ones and how to avoid them?

business chart, analytics, meeting

Without a doubt, adopting data science is a massive milestone in every company’s development. With data analytics solutions implemented, managers can make a more informed decision and act based on a solid foundation. Data analytics provides companies with priceless business knowledge that refers to their:

  • Business
  • Customers
  • Procedures
  • Vendors
  • Workforce
  • Cash flow

And many other significant spheres of running a company. The main concern of every decent data scientist is to get reliable and unbiased results. Only then obtained information can be useful business-wise. The only way to ensure the accuracy of the results is to be aware of potential mistakes and errors and avoid them.

In this article, we want to show you thirteen dangerous mistakes and errors that frequently happen in various data analytics solutions. Let’s take a closer look at them!

Common mistakes in data analytics solutions

Vague hypothesis

Just like in any other discipline or activity, if you don’t know what you want to achieve, you usually waste your time. That’s why every data analytics activity should start with defying a clear and measurable hypothesis. Make sure you understand the goal and have all the necessary tools and sources to get there.

Solution: Make sure your hypothesis is clear and verifiable.

Uncleaned data

In our last article Business Intelligence Product: Which one to pick for your team, we told you how important it is to operate on clean and organized data. Make sure your databases are organized and ready to be processed. Don’t expect to get reliable results when the source is disordered.

Solution: Make sure your data is clean and organized. Take a first glance using pivot tables or quick analytical tools to look for duplicate records, errors, or other kinds of inconsistencies.

charts, laptops, people working together

Start data analytics solutions with an open mind

When it comes to data analytics solutions, you have to bury personal bias deep down. Allowing your own bias or assumptions to speak is a recipe for disaster. In short, you will eventually get the results you expect to get, and that’s not the point. Let data speak, don’t tell it what to do!

Solution: Approach each project with an open mind and make no initial assumptions.

Don’t mix causation and correlation

Yes, that exclamation mark at the end is necessary 🙂 That’s a very, very common mistake. If you find a correlation between two or more different variables during your analysis, it’s easy to assume that one of them causes the other. And, yes, sometimes it may be accurate, but it’s not a rule. That’s like looking for a correlation in a statement saying that “everyone who drank water eventually died”. There are hundreds of similar spurious correlations.

So, what are possible correlations between two different variables (let’s call them X and Y)?

  • Both X and Y can be a consequence of the third factor Z
  • X and Y have nothing to do with each other
  • X causes Y
  • Y causes X
  • X causes Z, which is caused by Y
  • Y causes Z, which is caused by X

As you can see, there are many possible options, and they have to be investigated. Don’t just jump to a conclusion!

writing code, typing

Solution: You have to understand the difference between correlation and root cause.

Over/under fitting data

These two mistakes in data analytics solutions are also common and dangerous. Overfitting is all about developing a too complicated model trying to fit a limited set of data. Underfitting is a reverse problem–the model misses parameters that it should have included to produce a clear and unbiased result.

Solution: Always try to devise a data analytics model that fits your set of data.

Sampling bias

A multitude of aspiring data analysts tends to fall into the sampling bias trap. It happens when you take just a sample of data and draw conclusions based on it. Consider an example. Let’s say you run a Google Ads campaign. After only two days, you take and analyze the results. It’s a sampling bias. A set of data you want to study is not representative.

Solution: Keep in mind that various elements (duration, platform, dataset, etc.) can influence your outcomes in a specific direction. Try to obtain as big a picture as possible.

Cherry-picking in data analytics solutions

It’s another prevalent mistake. Cherry-picking comes deep from our nature. It happens when you take into account just one metric that proves your initial assumptions. Why is cherry-picking dangerous? Because it leads to false outcomes. You shouldn’t rely on only one specific variable.

Solution: Consider multiple metrics when drawing conclusions.

business meeting

Data dredging

You’re a victim of data dredging when you test new hypotheses against the same dataset. It’s a straightforward way to obtain correlations that are biased by your first results. It may be tempting; after all, it’s a “fantastic” way to save a lot of time and work, but just don’t do that.

Solution: New hypothesis = a new dataset

False positives VS. false negatives

These errors are common in data analytics solutions and, for instance, healthcare and medical research. You have a false positive when the result incorrectly indicates a condition when it is not present. And a false negative is reciprocal: The result incorrectly fails to reveal the presence of a situation when it is present.

Solution: Pay extra attention to statistical hypothesis testing.

Data is important but so is the benefit

Many data scientists tend to focus solely on data analysis without paying attention to the benefits such research will bring to a company. Data science can never be art for art’s sake. There always has to be a clear goal and real benefits. If there’s no benefit, there’s no need to create a data analytics model. Also, bear in mind that the numbers you get should always be placed in a specific context. Always ask yourself, “why am I doing this?”, and “what’s the main goal here?

Solution: Think about the company first.

data analytics

Ignoring the possibilities

You can never forget that each decision, each version of events has some level of uncertainty. Data scientists frequently fall into the trap of saying with 100% confidence that if the company takes action X, it will achieve goal Y. In real life, it’s not that simple. In most instances, there is more than just one possibility, and they cannot be ignored.

Solution: Master scenario planning and probability theory to ensure that the decisions your company makes are more often correct.

Ignoring seasonality

Many businesses experience seasonality, which can severely affect the obtained results. This is especially true in the e-commerce sector when sales go sky high during Black Friday or Christmas. Ignoring that trend can be a costly mistake.

Solution: If the company you work for experiences variations in sales due to seasonality or other temporary fluctuations–always take them into account.

Neglecting data visualization in data analytics solutions

Today, the vast majority of data analytics solutions are equipped with data visualization tools, so it’s not that big a problem, but still, you have to pay attention to data visualization. Data scientists are frequently so fixed on all of the technical issues they tend to forget about presenting their results in a clear and transparent way. Don’t make that mistake. Remember when we talked about the benefits for your company? If you provide the decision-makers with a wall of digits, they won’t understand a thing. So there’s no benefit for the company.

Solution: Make sure your results are visualized and prepared to be shown in an attractive way.

How to avoid mistakes in data analytics solutions?

We showed you thirteen common data analytics mistakes and solutions that will help you avoid them. If you want to avoid the mistakes mentioned above and others (there are many more!), you have to arm yourself with diligence and an open mind. In fact, that’s the only way to avoid mistakes in data analytics solutions.

Every time you start a new project, ask yourself a couple of questions:

  • Where is data coming from? Is it clean?
  • Are there any known biases in it?
  • What should I take into consideration in this project?
  • Are there any additional elements I should be aware of?

Moreover, always broaden your knowledge. Learn about possible mistakes and find out how to avoid them. If you have a more experienced data scientist in your team, ask them to help you improve your work. And finally, if you have any doubts, let someone else check your findings. At some point, we cannot see our own mistakes. Another set of eyes can be a priceless help, especially if it’s someone who has more experience with similar data analytics projects and statistical models.

And if you need help with data analytics solutions, keep us in mind! Addepto is an AI consulting company. Data analytics is our daily bread. We have many experienced data analytics experts on board, and they are eager to help you with your data challenges.


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