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Is Your Data Ready for Machine Learning?

Artificial intelligence and machine learning technologies have recently become popular in the corporate sector, but most companies don’t figure out how to use their data correctly and how they can benefit from it.

Today, most of the companies’ data remains scattered, raw, or otherwise not ready to use in machine learning models, so business cannot benefit from AI. In this case, there can be no operational optimization, products personalization, and future needs forecasting.

So, before you decide to implement a machine learning platform in your business, make sure your data is ready for this.

How Much Data Is Enough for Machine Learning?

You should start machine learning development planning in your company by answering the following questions:

  • What do we want to know?
  • What do we want to forecast?
  • How will it affect the company profit?

It helps you to figure out what data the company will need, to what extent, and where you can find it. But the amount and types of data your business will need also depends on the machine learning model you are going to use — supervised or unsupervised.

A supervised machine learning model is applied most often and forces the model to search for specific results. This type of training requires a reasonable amount of tagged data, but it also enables powerful predictive models to be created.

An unsupervised machine learning model includes raw data evaluation to identify patterns and anomalies. For example, you can identify potential cyberattacks analyzing security logs. The amount of data that is needed depends on the future model functionality.

Your data should reflect the actual situation in the real world, otherwise your model would have low predictive validity. Therefore, you will need three different pools of data:

  1. For model training.
  2. For accuracy test.
  3. For testing the whole system before its start.

You Need Pure Data Values for Machine Learning

Data cleansing for machine learning, its origination, and labeling are mundane tasks that include de-duplication, data consistency checking, etc. It takes a long time to collect data and convert it into the necessary format, but if you do it incorrectly, the data cannot be used.

Raw data marking may be required in a variety of situations. For example, when you have a lot of reviews, and it is necessary to determine and tag the sentiment of statements, for example, sarcasm or other stuff incomprehensible for the model. Or when you have many unstructured random images and need to tag them to help the machine learning model understand what it is looking at.

In some cases, you can use a mix of raw and structured data to recognize such stuff as comments and entries from a CRM database.

Machine Learning Implementation: How to Start

Today, there is a growing number of projects that provide model building services based on customer data — machine learning as a service. This service is provided by some world corporations, but the work with them is still too expensive, and the functionality is limited by a certain platform commonality.

In this case, a suitable solution is a cooperation with a provider who will develop a model based on external or your own data meeting all company requirements.

It helps a business gain a competitive advantage, for example, with identifying disloyal customers and offering them some additional bonuses or better terms. With machine learning, you can forecast delivery of goods to stores based on their annual attendance or the current and future needs of the population based on reviews and comments from social networks. Machine learning also helps automate processes and makes them more efficient.

Rakesh Babu
Rakesh Babu
Rakesh Babu is a business analyst with a focus on startups. With an MBA and years of experience, he's a go-to source for insights on entrepreneurship. Beyond the business world, Rakesh is a chess aficionado and an amateur astronomer, always curious and seeking new patterns – whether in the stars or the stock market.

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