Data science and machine learning have become necessary for the survival and growth of any business.
Leading employment review website Glassdoor ranked the job of data scientist number one in the list of best jobs in the United States for the year 2018. It was rated highest regarding maximum job openings, job satisfaction rating, median salary, and others.
As more than 2.5 quintillion bytes of data are generated each day, the demand for professionals who can leverage and process this data is also increasing.
Big data is transforming each sector and industries of the business world. For example, in manufacturing, big data can help in cutting down waste production by producing what the customer needs.
In marketing, with the help of data science and machine learning, we can know about the details of our valued customers or clients. It also helps in getting more valuable consumers through engagement and loyalty programs.
In healthcare, big data and machine learning can help to know when an epidemic may occur through patient history and behavior pattern.
Here are five ways how Data Science and Machine Learning are Transforming the Business World:
# Innovation
Data science and machine learning use new and innovative ways to solve the most complex business problems. Data scientists know how to find the best solutions for the same old problems.
For example, in the popular book and movie “Moneyball” it was shown how data science outperformed the old methods and techniques which were used to evaluate the performance of teams in baseball.
With the help of data-analytics, high-performance players were selected who were previously overlooked by other teams using old methods. The result was that the team performed very well and defeated most of the other top teams in the league.
Another best example is of the package delivery company, UPS. They used data science and machine learning for finding the best possible route for their delivery trucks. It helped them to save millions of dollars and also significantly improved their customer engagement and experience.
# Exploration
Data scientists and developers should be given time to explore big data expeditions with no objective. It would help them to examine data to find new undiscovered values.
For example, data experts and machine learning scientists working at a Japanese maritime service collected some valuable data when they were providing their routine services for ship classification.
They analyzed their new data in a correct format which helped them to reduce the maintenance cost by 10%. It also increased 20% market share of the company, and more value-added services were provided to the customers.
# Prototyping
In the new business world, as the data is continuously increasing, humans are facing difficulty in the decision-making process.
Machine Learning with Python is capable of solving even the most complex data-based problems and errors eliminating the challenges coming across data management. ML algorithms and Data science concepts efficiently handle business data delivering semantic end results.
For example, the United States police department with the help of data science and machine learning was able to get actionable insights for a massive pool of data. They created an automated analytics application which helped them to predict crime using criminals behavior patterns.
It resulted in the decrease of murder rate by 35%, theft rate by 20% per year.
Data science is changing lives for the better by its automated analysis application which examine the medical test data.
# Refinement
It is one of the most common applications of machine learning and data science. With the help of the collected data, most data scientists create models for refining complex models and processes.
Some of the most common examples are retailers who change their pricing models or banks with dynamic financial models.
Recently, Zurich insurance used machine learning and artificial intelligence to automate their injury report assessments. It helped them in reducing the inefficiencies during injury claims.
They used AI to make their medical report evaluation process to work on automation so that the human agents could focus on value-added activities like getting more clients or negotiating with third-party vendors.
Also, the time needed to create a medical assessment report was significantly decreased from one hour to a few seconds which helped them to save more than $5 million per year.
# Firefighting
The methods used in this are almost the same as the exploration category but is used in different context.
When the symptoms are apparent like a drop in profits or increase in consumer complaints, then sometimes organizations trigger data science initiative. In this case, data analysts analyze only the cause which limits the number of datasets which needs to be studied.
If the data science team can deep dive, then they can get something useful and know the real cause of happening.
Some typical examples are when customers return goods online despite getting the delivery on time and quality being right, or retailers complaining about the condition to manufacturers, etc.
The Final Say
Data science and machine learning have transformed the way businesses used to work. Whether you want to get more sales or want to get more conversion, you need data analytics expertise to be successful in business today.
