Avoid these 10 Big Data Analytics Blunders in 2021

Big Data Analytics
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Big Data analytics is the field reshaping every industry continuously. Besides other technologies and advancements in the area, it is driving businesses with colossal success. A statistical report shows that the data analytics industry will reach the U.S $64 billion by 2021. It is a massive number to digest. Observing the rising trend, several companies have already started providing big data solutions.

They acquire the firm’s data and process them to fetch different levels of details (also known as data granularity). The data analysis process requires statistics, probability, and exhaustive computational power to gain resourceful insights. Any minor mistake while performing the analysis can mislead the whole result. Here, every single detail which may seem unneeded can be the game-changer.

As the data industry is new to people, they often make mistakes that lead the whole process in the wrong direction and waste time, resources, and energy. This article will highlight the ten big data analytics blunders to avoid in 2021 and prosper.

  1. Correlation Does Not Imply Causation

While working on data, many data scientists presume that correlation implies causation. It is useful in many scenarios, but the case is not the same every time. Using the same approach for every data can deliver false results and predictions. Data scientists have to understand the difference between correlation and causation necessarily to be effective.

Correlation means observing the two or multiple events simultaneously, whereas causality means event B happened because of event A. Both terms are in complete contrast, but some analysts often struggle to understand the principle and consider it the same. Remember, it is not mandatory that if two events seem related, the one causes the other. It can be a coincidence.

  1. Making the Data Big Unnecessarily

Young aspiring data scientists and analysts grab everything to make the data ‘Big’. A considerable amount of data is necessary to find patterns and insights, but collecting irrelevant items will not give one anything informational. Instead, it will confuse everyone.

So, before jumping into the ocean, wear a life jacket. In other words, first, analyze the data thoroughly and then plan things accordingly. If the planning is not aligned with the goals and objectives, whatever insights one discovers, nothing will make a difference. So, do homework first.

  1. Relying on Data Warehouse for Everything

A data warehouse is a central system that collects data from different sources. It is the core component of business intelligence (BI). However, they are best suited for structured data. Several organizations use traditional data warehouse technology for storing and managing data. They are useful for solving several problems but in a limited way. We cannot use a warehouse for data like videos, images, and visuals. Using it where it is applicable is what you should go for and excel.

  1. Only Focusing on Long Term

Everyone uses to think for long-term profits and develop strategies accordingly. It could be a big mistake in the big data era. Companies and their analyst department have to understand that data they collect on a small scale and usually daily have a significant value to improve short-term growth and long term. Small and steady steps bring big success. Success and development are not overnight kinds of thing. Using data tools and AI-enabled programs to manage and examine data is the prerequisite.

  1. Analysis Paralysis

Analysis paralysis is the state when an individual or group starts interpreting an overwhelming size of data and overanalyze it. After some time, they reach the point when things get stalled, and the analysis process cannot move forward.

To avoid such a problem, always start with a small and define the aims clearly. Holding everything at once often jumble up everything and make everyone clueless.

  1. Paying Importance only to data

It is the point that everyone’s eye misses. People rely only on statistics, probability, and machine learning models to find patterns. However, it is not enough. Industry and domain knowledge is very crucial. Once you know the industry and its process, you can play with it like a pro player. To understand the data and generate useful results, an analyst should learn about a particular industry. Without substantial knowledge, it is like playing archery at night.

  1. Using Inaccurate Data

No matter how vigilant analysis one performs, if the data and its sources are inaccurate, the game is already over or probably never started. The very first thing data scientist do is to preprocess the data and eliminate the outliers. Outliers are the values that can disturb the whole analysis process and lead to inaccurate results. For performing analysis, we cut down such matters and work on the normally distributed data.

  1. Expecting too Much from Machines

No doubt, AI is mighty and smart now, but it cannot do everything. Human expertise always has the upper hand. Gathering the data, feeding it into the machine, and waiting for the results is not data analytics. AI is a helping hand but cannot do everything by itself. One should know what is happening on the backend. Only then they can use it effectively and tweak it according to the problem. Nevertheless, AI can save lots of time by automating the manual work and process.

  1. Going with One Solution

Data scientists often made mistakes by looking only in one direction and ignore the other potential possibilities. There is always more than one resolution to the specific problem, which helps make informed and sound decisions. It is why every data scientist should look at different probabilities and avoid going in one direction.

  • Underfitting And Overfitting

When developing a machine learning model, the ML engineer often makes a mistake by overfitting or underfitting it. Overfitting performs well on training data but performs poorly on real data, while underfitting performs poorly on both training and real data. To avoid such issues, always use a balanced approach and normalized data.

Takeaway

The analytics field is very vast and diverse. It is a blend of mathematics, statistics, technology, and industry. Due to its diversity and varying nature, the chances of blunders and mistakes become high. But it is acceptable. The more mistakes one makes, the more he or she will solidify their knowledge.

However, if the mistake is not an option for you and you need some professional support for developing a big data strategy for your organization, contact Cubix without any second thought to be successful.

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