10 Mistakes to Avoid in Implementing Predictive Analytics

Predictive Analytics

Adopting data analytics solutions is a significant milestone in the development and success of any business. Predictive analytics is a widely used data analytics strategy that improves your company decisions by observing patterns in previous occurrences. As predictive analytics methodology predicts outcomes based on data, it proves to be more accurate than any result achieved through gut feelings or being influenced by anecdotal experiences.

Even though implementing predictive analytics solutions enables managers to make informed decisions, there is no perfect predictive model. The data scientists are always searching for unbiased results that can be used for their business purposes. The only way to ensure this is to be aware of and avoid potential inaccuracies and errors.

Let us discuss some common mistakes to avoid when building predictive analytics project for your business:

   1. Uncertain hypothesis

Just like any other activity where you don’t know what to achieve, you usually end up wasting your time for nothing. Similarly, before beginning with your predictive analytics project, it is wise to understand your goal and have all the necessary sources that you need to achieve those goals.

    2. Uncleaned and imbalanced data

Data imbalance is a critical component of any predictive analytics puzzle, and it’s something that you can’t measure in a traditional accuracy evaluation. Remember that your predictive analytics model is only as good as the data you have. If the information is outdated, scattered, or incomplete, do not expect to get reliable results out of it.

As a solution, make sure your data is clean, organized, and ready to get processed before implementing the model. You can use tools like pivot tables to quickly analyze your dataset and avoid duplicate records, errors, and biased models, which can mislead you towards false predictions.

    3. Working with a closed mind

Too frequently, data scientists work with what they’ve been given and don’t spend enough time thinking about more creative elements from the underlying data that might improve models in ways that an upgraded algorithm can’t. You can significantly improve the results of your predictive analytics projects by creating some unique features and characteristics that can better explain your data patterns.

    4. Not differentiating between causation and correlation

While analyzing the solutions of any data analytics model, it is a widespread mistake to define the correlation between two or more variables. It is easy to assume that one of them caused the other, but that’s not the case every time.

Mixing causing correlation is like finding the correlation in the statement– “everyone who ate the fruit died,” as this statement cannot be universally true. There are hundreds of such fake correlations that exist, and hence, do not jump to conclusions before identifying the actual causation of your results.

    5. Over/Underfitting data

Over or underfitting the predictive analytics solution is a common mistake that any data scientist makes while developing their model. Overfitting your data refers to creating a complicated data model that fits your limited set of data. On the other hand, underfitting your data refers to the missing parameter, which can provide a transparent and impartial outcome.

To avoid this common mistake, devise a data analytics model that fits your set of data efficiently. Use external tools like OpenRefine and IBM InfoSphere to cleanse your dataset and provide yourself with transparent outcomes from your project.

    6. Sampling bias

It is often noticed that many prospective data analysts fall victim to sample bias. It happens when the analyst tries to identify the results by inputting just a sample of data. For example, analyzing and predicting the results by running a Twitter Ads campaign for just a couple of days. This cherry-picking nature of data analytics can lead to false outcomes.

Moreover, many business sectors experience a drastic change in their sales depending on the seasonality. For instance, e-commerce sales go spaced out during festivals and holidays. Ignoring this sales prediction by considering the seasonality change can be a costly mistake.

Remember that various elements such as time duration, tools, etc., play a vital role in your outcomes. Consider every aspect of your metrics and acquire as large and feasible an image as possible.

    7. Data dredging

Data analysts often test the new hypothesis with the same old dataset to save significant time and effort. Doing this will always lead to biased correlations with the results of the previous theory.

Do not repeat this mistake. Testing new hypotheses with a new dataset will always provide you with a clear better picture of your predictive analytics project. For example, you wish to identify the e-commerce sales depending on the sales data of years 2019 and 2020.

To correctly train your model in such a scenario, you can separate the datasets into two groups, i.e., training and testing. Later, consider the sales data for 2019 as the training data and test the predictions against the data of the year 2020.

Suppose the findings are too good to be true while working with a predictive analytics project. In that case, it’s worth investing additional time on your validations and maybe seeking a second opinion to double-check your work. Doing this will provide you with two different results, and hence you can measure the accuracy of your outcomes for a well-informed decision.

    8. False-positive and false-negatives

While working with a predictive analytics project, statistics is the game’s hero. Most of the time, data scientists fail to identify the errors present in the statistics and ultimately end up with the wrong prediction.

Identifying false positives and negatives from analytics is the most crucial task in dealing with data science projects. False-positive indicates the condition where the statistics suggest the results which are not valid. On the other hand, false negatives are reciprocal, i.e., the statistics incorrectly fail to reveal the presence of the results present in the data.

To avoid this common mistake while dealing with your predictive analytics project, pay extra attention to your statistical hypothesis testing. You can use many online tools to filter your dataset and identify the errors that are pretty standard to notice but can impact your results.

  9. Ignoring the possibilities

Always remember that every action has its equal opposite reaction, and at the same time, every reaction has its level of uncertainty. Data scientists often assume that the results are 100% reliable, and if the company takes action A, it will achieve goal B.

However, in reality, it is not that easy. There is always more than just one possibility of results while working with a predictive analytics project. As the model fetches data according to their need and requirements, you cannot ignore the possibility of more than one outcome.

Make sure you always plan your scenarios and company decisions considering more than one possibility and use probability theory to ensure accuracy in results.

    10. Using primitive tools

Modern predictive algorithms forecast the outcomes from the data but cannot explain the “why” behind the results. For instance, why will promoting X product generate more revenue than product Y? What product factors should we consider promoting the most?

The primary issue is that marketers expect to anticipate the future based on current data and fail to employ cutting-edge techniques and technology. As a result, the number of characteristics defining the future is relatively low, which doesn’t provide you with deeper insights.

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