How Machine Learning Improves Data Analysis?

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How Can Machine Learning Improve Data Analysis?

There are thousands of companies working online today who use Data Analysis (DA) to use the information they gather more effectively. Prominent companies such as Facebook or Google use it to manage their massive amounts of data. It’s impossible to deny the impact of DA in the world of business. However, it shouldn’t be the only tool in your arsenal. Machine Learning (ML) takes things to a whole new level, and with the enormous gains in efficiency it offers, it is undoubtedly the future of analyzing data.

Machine Learning For Data Analysis: What You Need to Know


While DA offers an easy way for companies to asses their data, Machine Learning refers to the application of Artificial Intelligence (AI) which enables systems to automatically learn from data and make predictions based on the data input.


As you can see, the difference between Data Analysis and Machine Learning is the predictive nature of the latter. While data analysis algorithms are effective in addressing the challenges of data management, Machine Learning takes it a step further by identifying various patterns of large data sets that companies can use to be more efficient.


Healthcare, transportation, financial services, government - these are just a few industries already using Machine Learning. The following are some of the most remarkable examples of ML and AI put to good use:

  • Volvo - This car manufacturer has taken predictive technology a step further by using Machine Learning to determine when various parts of a car fail. This helps the company serve its customers better and prevent deferred maintenance.
  • American Express - Credit card companies deal with a lot of data. Because most of the data they deal with is financial, DA in combination with Machine Learning has been used for security purposes.
  • BMW - BMW has also implemented DA and ML in various departments. These range from the process of designing vehicles to sales management. Machine Learning has also pioneered a way for BMW to stay among the top companies in driverless technology.
  • Netflix - A big media corporation such as Netflix has a lot to gain by knowing what people want to watch. With Data Analysis and Machine Learning, they can predict certain trends, thus, offering more relevant and in-demand content for their audience.

4 Ways A Business Can Benefit From Data Analysis and Machine Learning

Data Analysis using Machine Learning is becoming a prevalent trend, and many companies are already seeing its potential. It has been estimated that the total revenue in the ML and AI industry will reach $57.6 billion by the year 2021 . AI and ML can change how teams develop, deliver and organize work and how companies enhance their performance with business operations. Using Machine Learning in Data Analysis can enable a business to earn more profits from its projects, making it more competitive.


Data Quality

All business owners need to know the difference between good and bad data. It can be challenging to distinguish the two, especially when data quality is assessed manually. Advanced Data Analysis techniques are important, and this includes the use of Machine Learning software as well.


ML and DA can enable large enterprises to improve the accuracy or their inventory and product data. For a large business, manual management of data can be a big problem and can easily result in mismanagement of company data, deteriorating the quality of customer service in the process.


Data Visualization

If you are running a large scale company, your primary data visualization goal should be making sure all the information is well illustrated. This is done using graphics that represent company statistics and other relevant information.


Dynamic dashboards are a vital component of business intelligence. They will enable team managers access to all the company data with the best available graphic representations. Machine Learning for visual Data Analysis plays an important role in helping companies decide on what to do with the data by making predictions.

Ad Targeting

Machine Learning predictive quality comes in handy during company ad campaigns. The connection of Machine Learning with Data Analysis makes it easier for companies to process complex data. Once this is done, they can look at customer behavior and their reaction to certain products.

Besides, Machine Learning will also help to personalize certain marketing trends that will help companies identify specific "pain" points to focus on when creating ads for their products.

Data Classification

Data security should be among the top priorities of any large company. When it comes to a company’s defense sector, data classification is essential for keeping intelligence private.

A system that learns from data patterns is better equipped to protect it. Poor data classification can lead to great company losses. Therefore, it is better to invest in security solutions than to take any additional risks. At Trust Sourcing, we used data classification to create an AI-based recipe search engine. We grouped over 10 million ingredients into categories and implemented a detailed filter system to help a user find the recipe he’s looking for by typing an ingredient into the search bar.

How to incorporate ML and DA into your business



Machine Learning in Data Analysis can be very beneficial to any business as we have seen above. Now let us look at how you can use these features to your company's benefit. Nowadays, a company can choose one of two methods for how it develops a DA solution, either using in-house resources or outsourcing. Let's find out what the main differences are between the two.

In-house vs Outsourcing Software Development



In-house Development Outsourcing
Costs more to form a team of full-time employees that will create a system. The costs of operation are lower and more convenient for budget saving.
Companies have to make sure they find the best possible talent to bring on board. Companies don't have to worry about investing in any infrastructure or hiring any personnel since they get all of this right from the start.
During the in-house development, all company's data stays within the team. If a reliable outsourcing partner is found all working process will be carried out in the smoothest way possible eliminating any data leaks or security breaches.
An in-house development team always works according to the company’s schedule. Due to a possible time zone difference with an outsourcing team all communication has to be negotiated in advance. However, with proper management on the outsourcing company end, it's not an issue and can even take advantage of productivity outside of your company’s regular working hours.
It is difficult to be sure that an in-house team has the right skillset for every new project. Eventually, a company will still need to hire new professionals or consult with outside sources. The outsourcing agency handles all hiring and risk management. Aside from that, outsourcing can enable large companies to start new projects while working on the current one. This increases the probability of making more revenue because multiple projects can be tackled at once.


A side-by-by comparison of in-house vs. outsourcing software development shows that the latter is more profitable and faster solution.



Summing Up


If you are looking for a better way to manage your company’s data, adopting Data Analysis with Machine Learning is a step in the right direction. A lot of successful companies have proven that ML and DA can make a big difference in how you run your company.

At Trust Sourcing, we will provide you with the best services to incorporate machine learning and data analytics into your business. Our ultimate goal is to make sure your company keeps soaring to new heights.
Contact us for more information!

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