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AI & Data Migration: Transforming modern-day business operations

July 26, 2023 No Comments

by Dmytro Reshetchenko, DICEUS

The way artificial intelligence has disrupted modern-day business operations post dot com and the digital revolution is astounding. It reduces cost and increases efficiency, growth, customer retention, and user experience. Many heavyweights, including Google, Microsoft, and IBM, have been investing a humongous amount of money in the research and development of artificial intelligence over the last decade. With the increase in popularity, most businesses boast of using AI on their platform for customer attraction. Let’s dive into how implementing artificial intelligence and data migration is helping businesses in the contemporary chaotic marking condition. Simply, artificial intelligence is the humanization of computer science that helps function in building systems and processes. 

With the fast-paced technical evolution in the business sector, the expansion of a business is made easier with the help of various data migration and storage technologies. The process of migration adaptation and easier handling of business data between evolving platforms play a key role in the overall success of a business. Reliability and quality of the migration service are extremely important due to the fact that a simple error can result in partial or heavy loss of the business. Cloud storage, or simple transfer of central data, has become efficient and less time-consuming with the help of similar applications of data migration technology.

Artificial intelligence in business


In the pre- AI world, businesses had enormous amounts of data that needed systematic structuring to discover relevant information to help their business grow. Not to mention, it was done manually, which led businesses to invest time, money, and human efforts. Post-AI revolution, even after the exponential increase in business data, AI adoption has made it easy to dive deep into anomalies and deviations to reveal relevant insights and has helped tackle a lot of business uncertainties.


To reduce cost and time, almost all companies are looking up to artificial intelligence, and to be honest, AI is doing its job quite efficiently. Automating a monotonous and repetitive task reduces human error, thus increasing productivity as well as the quality of outputs.

Artificial intelligence is also being used by enterprises to automate the hiring and training of new employees. Unilever receives millions of job applications every year. It is not practical for the company to go through all the resumes. Hence, Unilever is using artificial intelligence to enhance its recruiting techniques. Moreover, automation like “Robotic Process Automation” uses AI to create bots to complete several tasks and can use insights to process use cases. AI has a remarkable presence in every industry. From using artificially intelligent robots in surgeries to driverless cars, from calculations and payment in the insurance industry to using AI in healthcare for diagnosis and treatment.


Forecasting is the process of utilizing already available data to predict the future behavior of an industry. Forecasting can primarily be summarized into two categories:



If historical data is unavailable or irrelevant, qualitative forecasting can be used as it depends solely on research and expert opinion about the current market situation. Qualitative forecasting needs more time and effort as compared to quantitative forecasting.

On the other hand, quantitative forecasting is based on historical data and uses time series analysis and other statistical models to predict more accurate results. Either of these forecasting techniques can be chosen depending on the business problems. It depends on factors like availability of relevant data, context, forecast urgency, and cost vs. benefits.

Compared to the previously used traditional way of forecasting, AI-based techniques allow you to use numerous metrics and KPIs resulting in more accurate projections.  AI uses algorithms to forecast demand for inventory and services, anticipating expenses and growth projectile. As the complexity of the supply chain is upsurging exponentially, it’s becoming harder to forecast and be ready with additional plans. AI can quickly analyze the data for the supply chain and thus can predict inventory and service time. 

Ever-changing intelligence

AI is evolving the same as we did once. These models capture knowledge by obtaining and resuscitating information over time. Compared to traditional AI systems, which require pre-defined programming and rules to adhere to, these AI systems evolve by involving the user and apprehending its changing environment.  These models not only replicate human behavior but also transcend them. These AI models help businesses to find unknown structures within the dataset to yield desired outcomes. There are 3 types of these models:

Supervised Learning Model

This technique is where algorithms work on test data. This test data (trimmed-down version of a bigger data) has pre-defined inputs/outputs that the algorithm learns over time to come up with the desired result for the targeted dataset. Businesses often use this model for collecting, labeling, and data accuracy.

Unsupervised Learning Model

These models are fed with unlabeled datasets. Unlike the previous model, where inputs-outputs are pre-defined, unsupervised learning provides the genesis of unknown connections within the data. Businesses often use unsupervised learning for data exploration, target marketing, etc.

Reinforced Learning Model

This model uses pragmatism to imbibe and better itself. Desired outputs are accepted, while undesired ones are not. Several OTT music and e-commerce platforms use reinforced learning models to target audiences.

In today’s world, AI has tremendously influenced how modern organizations approach digitalization strategies. This is just the beginning, AI has a lot to offer in the future. Stay tuned.

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