How to identify and avoid common pitfalls in big data analytics – Learnxt

How to identify and avoid common pitfalls in big data analytics

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How to identify and avoid common pitfalls in big data analytics

The right implication of AI or Artificial Intelligence tools and techniques is expanding several businesses. For a business to stay ahead of the competition, it needs more than just Big Data Analytics. Without assessing the quality of data, the outcome, and profit would vary.
Data Scientists are hired after a lot of planning and at a high cost. Big companies cannot afford to deal with repeated mistakes from Data Scientists. Bad practices in Data Science leads to business failures.
This job is not a ‘no-brainer’. Let’s look at some ways that can help avoid common mistakes in the field of Data Science.
Measuring the wrong thing
Like unrealistic assumptions, measuring the wrong thing can severely affect the outcome of the project. Make sure to test the single line of code before beginning the work. The most usual way is to do ‘modelling’ to assess the quality of programme or business initiatives that judge the overall effectiveness of the input as a form of output.
For instance, your organisation runs on ML that distributes content to clients via email, based on connected device behaviour. The number of outgoing emails that the system produces will affect the performance of algorithms and the end outcome those emails are intended to accomplish.
Confusion Correlation and causation
This is the most common way and falls under the category of finding a strong relationship between variables. Traditionally, the thought is in the context of relatively simple cross-tabulation or regressions, and we have to remind ourselves that there should be a logical link between two co-relational trends. 
Therefore, before beginning with an explanation of factors, one should consider whether or not there is any link or some other co-founding factors that you are missing; there is a pre-linked trend linked to any factor being studied. 
Ignore the possibilities
Often, Data Science tends to ignore the possibilities for solutions that can lead to disastrous results. Data Scientists often take decisions by assuming that if a business takes a decision then they can achieve the desired result. There is no single answer to a specific problem, and hence informed choices have to be made by data science from various possibilities. Various possibilities always exist for a particular solution, each of which has some level of uncertainty. Probability and Scenario theory are two essential aspects of Data Science that should not be ignored to ensure the decisions made are more often correct. 
Building a Model on the Wrong Population
If the goal of the Data Science project is to model the customer influence patterns, building the model on the wrong population is not a good practice. The model should be built considering the behavioural data of customers who are highly influential, and also those who are less influential and are likely to be influenced. Underestimating the predictive power of either group in the population can skew the model and some crucial business models.
In a report by SAS, “The ‘traditional’ traits associated with data scientists – such as technical, analytical, and logical skills – still dominate. However, other less technical traits – such as project management, creativity, and good communication skills – are also present. Organisations need data-savvy individuals who are technically proficient, mathematically minded, business-oriented, and strong communicators. It’s unlikely that any individual will have all of the skills required to maximise the value of big data.”

However, there a few universities in India that are making this possible.


There are many similarities and differences between every Data Practice, regardless of whether we are comparing the public and private sectors, finance, insurance, energy, and transportation, or technology and healthcare industries. Easy access to advanced tools has given us more power than ever to make informed and more effective business decisions. Protecting against these pitfalls will help your enterprise to reach all the organizational goals.

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