The popularity of data analysis has its own disadvantages. Some believe that the unachievable can be achieved with data analytics but that’s not true. These myths can result in data analysis failures. Let us keep the data analysis realities on higher priority and debunk these 12 data analysis myths.
Myth1: Quantity Is Directly Proportional To Quality
Many companies believe that a huge bulk of data can provide them valuable insights. The problem with this belief is that it completely neglects the aspects of data quality.

Every small and large organisation has bulk data. Large organisations always deal with huge data and it is not possible to analyse so much data. The question here is if so much of analysis is worthy at all? The companies need to rethink this. The quality should never be compromised.
Myth 2: Every Analytics Results In Future Predictions
Another most common data analytics myth is that data analytics results always predict the future actions for the companies. Imagine setting up a new work branch in your company. How would you know if this new division is going to be successful? To analyse this prediction, you need some master data which is missing in this case. Hence, it is just not possible to pretend about master data.

Predictions can also provide false values if we do not begin with the right set of rules. The prediction of the future is just an analysis and recommendation. It is best to move forward with predictions and forecasting only after clear understanding of the initial parameters. Only after repeated modelling and testing, a clear program on predictions can be developed.

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Myth 3: An Extravagant Data Science Team Is Compulsory
The competitive and cutting-edge data software is available in the market. The organisations only need a team of people who can utilise them properly and produce effective outcomes.

An extravagant data science team is a complete lie. Based on the data analytics skills of existing talented professionals, the entire company’s analytics can be performed. Expertise matters and for that you can hire certified professionals with high-quality data analysis skill sets.

That’s more than enough! Really.
Myth 4: Huge Budget For Data Analytics Squad
This was the truth a few years back but now data analysis is about reducing the company’s cost. Setting up a data analysis department in the company is no more an expensive deal. The data analytics costs are inclusive of the following –
the cost of data storage such as in clouds by Amazon AWS, Microsoft, Google, and IBM. They offer reasonable prices for the cloud storage systems.

Data analytics software does not cost a lot these days. Multiple software packages with multiple problem-solving features are available now.

The data collection has become simpler and fast. The internet of things platform has made this possible through mobile devices.
The only cost you bear on data analytics is on the person who uses these platforms to execute the task. Hire a person who understands the analytics and ensures affordable services.
Myth 5: More And More Data Is Needed For Data Analysis
This is certainly not true. Quantity of data does not qualify as a measure for effective data analysis.

Big data is surely a popular term these days but does it really affect all the analysis processes? Surely vast data sets play a vital role in huge tech companies. But a meaningful analysis can be carried out even with 10,000 records. This is enough to project the patterns and enhance the results.

Quality always matters before quantity. Always ask this important question: if bulk data analysis is going to produce the same results as the smaller data sets. If the answer is yes, go for the shorter path.

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Myth 6: Data Analytics Improves Every Sect Of Business
Yes data analytics is critical for any business and it improves the business by multiple times. Butis data analytics the answer to all the business problems? The answer is NO. The data analytics tools are effective in predictions of some aspects while they might be weak for other predictions. The customer orders, online marketing campaigns, financial data, and other similar areas can be analysed with the tools. The data analytics does not know if a new product should be launched or who should be promoted to the next level, and so on.
The data analytics strategy is best for solving critical business questions based on lots of quality data.
Myth 7: Only Online Companies Need Data Analytics
Online companies have surely enjoyed revenues and incomes from data analytics. Google, Facebook, Instagram, YouTube, or some of the major tech companies who have earned billions of dollars from revenue.

But the companies not online can employ data analytics for their business growth also. Data analytics can enhance the decision making process for such businesses. They are also helpful in the improvement of products. Let’s take the example of Uber where the customer’s need for travel was fulfilled. Soon Uber realised that food delivery is another vital business growth medium. They adopted it and are continuously monitoring the future with analysis.
Myth 8: Data Analysis Is All About Mathematics
This might be partially true but the entire truth lies in the candidates interest and critical thinking. There are so many sophisticated tools available today that maths background is not required a lot. These tools are efficient in collecting data, cleaning data, and making it readable. In fact, one can learn data analytics easily with the number of resources available. A logical mind set and enthusiasm to work with data produces a data analyst.

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Myth 9: Data Analytics Requires Huge Time Investment
Data analytics does not take a lot of time. It depends on the amount of data under analysis. We have clearly explained above that huge data is not a good choice for analysis, instead quality data is necessary. Once you choose the qualitative data and start the analysis, the results are obtained pretty soon. Once the metrics and results are obtained, it becomes easy to sort the work on priority basis. Companies often think that investing time in data analytics would prevent them from the actual work. However, data analytics results provide a clear and concise pathway for the next steps.
Myth 10: Analytics Does Not Reveal New Information
Guessing and depending on the fate of the old world business traditions. Today’s generation is highly dependent on data and practical knowledge. The data analyst analyses the data on every day basis and produces effective results. The insights provided on email marketing or social media marketing helps in devising proper campaigns. These metrics enable the firms to measure the growth rate and devise strategies for further campaigns.

Relying on one’s instincts and beliefs is not the answer to modern business systems.
Myth 11: The Company Size Determines The Need Of Data Analysis
The size of the company doesn’t matter when it comes to data analytics. Be it a small company or big, every firm needs operations based on data analytics. The small organisations can benefit a lot from data analytics and understand the mechanisms to grow. They can work on their strengths only after knowing it from data analysis. It helps in tracking leads and churn rates which helps in clarifying the area of focus. Keeping track of the daily visits on the social media platforms can help in running promotional campaigns.
These analysis tools are not very expensive to get started on. One can use Google spreadsheet or Microsoft’s Excel to monitor the progress.

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Myth 12: Every Single Metric Must Be Reported
It depends on the thinking of a data analyst if every single metric is important or not. A data analyst with a thorough knowledge of the business understands which data would help in the growth and which data is unnecessary. If the company wants hundred percent reporting on every single metric then it’s entirely up to them. However, missing one or two small details would not affect in the long run. Before conducting any analysis, the Data analyst and business teams interact to understand the critical metrics required for the tasks in hand. The essential metrics that enable solving a particular problem should definitely be reported.
Conclusion
Every business must be clear on their stand regarding data analysis. They should clear their head before starting on any analysis work. Especially, busting the data analysis myths and accepting the realities is very important.

We hope this blog helps you in understanding the true meaning of data analysis and its applications in different work domains.

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