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DATA ANALYTICS FINDING VALUE IN DATA


Expecting consistency in quality and taste from the food you love is obvious. But many factors can influence the way you want your dish to taste - like methods, ingredients, and spice content. Similarly, a number of factors may influence the overall quality attributes in data analysis.

So what do you mean by data?

Data is very much like a raw ingredient, not very useful by itself; but once we analyze and make combinations of our data, we eventually find its true value. For instance, salt is known as main ingratiate, it isn’t generally consumed by itself, but almost every meal would be lacking without it.

As we move forward with collecting ingredients or data it's really important to clean and filter it. Poor quality of food ingredients can give bad flavor or illness, the same way “dirty” data can produce disastrous results from models. Hence focusing on data type, null value inputs, and removing outliers is important. 

 

It is pivotal to adjust the compositions of these ingredients before putting them into a dish. We chopped onions, and mash potatoes before cooking them and mixing them with all species.

In the same way, we change our data so that it will produce the desired results from our models. Hence it is important to group our data, transform it, and accurately optimize it.

Considering you have just begun cooking on your own, you will start by tasting your food as its really important to understand the flavor of each ingredient and how combinations work with other raw flavors. But after gaining some knowledge about ingredients you would be able to create your finest meal. Similarly for data analytics, it's really important to explore and experiment with data before we build our foundation.

After successful assembling of ingredients, its time to choose how to cook- boil, grill, bake, saute, or deep fry. It is actually based on what you are cooking and you are likely to know which is the suitable option to obtain the best results.


Similarly based on problem-solving (regressions, classification, or clustering) category we can obtain the best process for a machine learning model.

Catchy presentation adds to the appeal of a dish. On similar lines, in data analytics, in order to explain our models to business stakeholders we have to produce visualization to better present our work.

Finally, made your perfect dish, then its time to serve your friends and share the recipe for the amazing food you created. Similarly, in data analytics, it's important to document our code and build a system to repeatedly execute our model for future references.

  

I hope the analogy between food preparation and data analytics has made the importance of cleaning, combining, and consuming raw ingredients ( data ) evident for the preparation of a Masterchef worthy dish( data analysis model)!

 

 






Bibliography

https://link.springer.com/article/10.1140/epjds/s13688-018-0149-5

https://www.thedigitaltransformationpeople.com/channels/analytics/the-impact-of-data-analytics-in-the-food-industry/

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