How To Use Data Science For Your Business

It’s well known that 90% of the world’s data was generated in the last two years. This means that exponentially large number of internet users have been leaving impressions of their choices, thoughts, preferences in the digital form. And this information is being collected by organizations. Consequently, there is a locked potential for companies and businesses to understand their customers better. But this isn’t so easy. Data itself isn’t so pure and rich with information. It is raw. When data is coupled with tools and skills that extract value in the form of insights, that’s when this potential is unlocked. Hence, the name ‘Data Science’ is quite apt.

Advantages of Data Science for your Business:

Any business that uses Data Science, aims to achieve value across different functions. It helps to identify trends which can be used by experienced management to make improvements in multiple areas in your business. For example, in manufacturing, a bottleneck in the process can be identified or in marketing where product pricing can be achieved more skillfully. Methods like time series analysis have improved over the years to help traders analyze the stock trends more accurately. Time series analysis deals with analyzing data that varies over time. In this case, stock prices.

Traditionally, businesses use macro-level data (mean, median, mode, etc) to analytically make inferences and derive insights from data of a large customer base. This is one of the places where advanced analytics techniques like Machine Learning can trump human abilities. Meaning, these modeling techniques can analyze each and every data point and create a model. We can make predictions for each and every customer which otherwise isn’t possible. Not only that but processing text data using Natural Language Processing or NLP methods have become essential for almost every internet giant like Facebook, Amazon, Google.

According to a report by Tata Consultancy Services (TCS), Data Science and AI startups can generate economic activity worth $13 trillion by the year 2030. By this time, 70% of companies, globally will have incorporated AI in some way or the other. Today, companies are eager to leverage Data Science and AI to foster product conversions, awareness, and audience retention. Many of them have already had their hands inside this data jackpot. Another analysis of the Data Science scenario by Accenture says that applications of AI and Data Science can increase labor productivity by 40%.

To fully understand how Data can be leveraged to increase revenue and profit, let’s study a few cases. These cases are picked from a wide range of industries and a variety of applications

Application of Data Science to businesses:

Spotify, a popular music streaming platform is famous for its recommendations. They can make you a playlist exclusive to you based on your taste and preference of music. Spotify collects micro-level data of users like – songs played, how frequently a song is played, whether the user visits the artist’s page after listening to one of his/her songs to check out more of his/her music? and much more. They use a popular technique called Collaborative Filtering to create recommendations. On a high level, it analyzes users who have played the songs that you have and recommends you the songs they have played but you haven’t. Spotify’s recommendation engine had a hooking mechanism from the beginning itself. Today, it has acquired over 60% market share in online music streaming market. Recently, it has also reported profits for the first time in 13 years.

Frauds in financial transactions have been a menace for financial institutions since forever. In 2016 alone, around $6 B were lost to fraudulent transactions. Machine Learning algorithms have been largely explored to tackle this problem. These models can take large input data and understand the patterns.

For a new customer, these trained models can predict the probability of whether the customer will commit fraud or not. Solving this problem will help companies save billions of dollars. MasterCard uses Machine Learning and AI to keep a track of data such as transaction size, location, time of the day in real time to predict whether the transaction can be fraudulent. However, a false decline of the transaction can be very costly to merchants as well as clients. Meaning, denying a transaction because it is predicted to be a fraud when in reality it isn’t. Merchants can lose profits worth $118 billion.

We all know that cab rides have varying prices. Sometimes normal, sometimes surging. However, behind these prices is an advanced Machine Learning algorithm that creates a robust and reliable way to automatically set prices dependent on many external factors like weather, holiday or not, traffic, time, etc. Uber exploits deep learning models built on LSTMs or Long Short Term Memory networks, to predict the future stance of the market. These models get better with the amount of Data fed to them.

Amazon has leveled up its Data Science game by introducing a feature called Echo Look that helps you with your wardrobe advice. It uses a depth-sensing camera to analyze you and your dresses, perform complex computations based on massive data it possesses and then recommends you dress choices. Buying online clothes has not been the same as buying groceries or electronics or any other item. People still need to feel and touch the material. They still need to check if the dress fits them well. Introducing intelligent systems will definitely solve some of these concerns to a great extent if not fully.

Amazon Echo Look. Credits: https://images-na.ssl-images-amazon.com/images/I/51DlndxsD2L._SX679_.jpg

Data Science finds its use in inventory management too. Kroger, an American retail company used predictive analytics for this purpose. Manufacturers need to keep a track of the inventory of components in order to ensure a smooth manufacturing process. Manufacturers must be fully aware of the conditions of various facilities in their production plant. Which parts are subject to breakdown or failure. This is where Data Science can step in to help. Using exploratory and predictive analytics, the company can analyze future demand of key components, their prices and strategize their purchases, maintenance schedules accordingly. In one of the many cases, airplane parts manufacturers were able to achieve cost savings of more than $1.8 million using these techniques.

Conclusion:

It’s not even a question that how data science is changing the way we make business decisions and the way we think about building products. This field is in its early stage right now but, it is moving pretty fast. If you wish to make launch your career in Data Science, you need the right mentor and the right program.