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Small Data vs. Big Data: What Does Your Company Need to Perfect Your AI Techniques?

Big data is the buzzword for every company working in the digital market right now. Alternatively, is it just the du jour that will vanish without a trace by the end of 2018? Big data is the fancy term that refers to vast sets of data, predictive analytics, customer behavior analytics and even other data analytics methods that marketers need to dominate. Scientists, medical professionals, government officials, e-commerce pioneers, CEOs, CIOs, and marketing professionals regularly meet some facet of big data during their everyday work life. 

Dig this – Wal-Mart handles over 1 million customer transactions per hour. These transactions represent a lot more than dollar signs. They represent the buying trends, customer preferences, brand choices, retailer performance and customer information. These enormous bulks of data go into databases the size of roughly 2.5 petabytes; in other words, a hypothetical place large enough to hold all the information in the books in the US Library of Congress, only 167 times over!

What is the right way to look at Big Data?

When most engineers, CIOs, and data miners look at big data, they do not see the mountain. They know every rock that builds the base of the mountain and supports it to the very top. Therefore, what they look at is technically small data. You can see small data as a wholesome entity that composes big data, but that is manageable on a single machine. So, even when small data comes in a completely unstructured form, you can easily identify it and label it. They do not bear the regulatory risk and compliance issues that most big data carry. This de nouveau trend is putting big data in a new perspective for all data handlers. Without the integrity of small data, it will be quite impossible to leverage big data.

It is the most probable reason of AI still struggling on the benches, while big data is playing the bigger matches. A company needs to fine-tune its small data operations to perfect AI for each task and process. Unless there is a small data plan in place, it is quite impossible to manage big data. Your AI needs access to the finer information on customer actions, background histories, demographics and customer behavior to function. It comes at a time where companies are still unsure about the purpose of their million dollar AI technology, and they are too anxious to ask about it.

What does your organization need right now?

At a glance, the solution seems simple enough. The users need to feed relevant bits of information to the AI for it to work. However, you need to find the answers to these few questions before you can go ahead and dive into the AI craze:

Do you have necessary data in an organized format?

Most users have multiple terabytes of data without any labels. Any data without tags is can be quite daunting for any DBA to sort. You can always take help from a resident database admin, or you can approach your remote DBA services for sorting out the small data within your big data. Check out remoteDba.com for a clear idea on how to label your data for easy future use.

In case, you have no small data, only big data, take a look around. Chances are there is a small dataset lying in some dusty corner without any label. These small datasets or subsets of big data are more usable for AI than the “real” big data databases.

How much data do you need for immediate troubleshooting?

Now, there are various models and equations that you can implement. However, sometimes simplicity is the essence. Always compare the predictable output of the process before you get down to the vector models. Find your "minimum viable product," reduce the cost and maximize the efficiency of the process. The trick is to use minimum data for the maximum output.

Does your IT team know enough about AI/ML?

While most of us focus on the more complex and glorified complaints, we rarely think about the most obvious challenges that our teams face. First, find out if your team has a working knowledge of AIML. Without this XML-based markup language, monitoring the activities of any AI is going to be impossible. If your organization is new to the concept of AI and AIML, you need to start by feeding them smaller lessons on small data. Fundamental problem solving on small data is the best place to begin your journey to big data and AI.

Big data was here, and it will be here forever. Small data is just a newer term that is finally doing its rounds around the data engineering, DBA, and marketing blocks. The gist is to focus on the smaller details of big data, to make the final picture a lot more perfect. 
Small Data vs. Big Data: What Does Your Company Need to Perfect Your AI Techniques? Small Data vs. Big Data: What Does Your Company Need to Perfect Your AI Techniques? Reviewed by Lokesh kumar on 3:33 PM Rating: 5