Categories
Data

Data Analytics in Healthcare Sector

Healthcare data, when used with data analytics, provides valuable insights for the hospitals to improve their quality and patient welfare. Hospitals have long been using descriptive analytics to diagnose the patient based on their medical record. Doctors also use descriptive analytics to find the current health state of the patients.

But descriptive is only the tip of the value that data analytics provides. The real use of analytics comes by using predictive and prescriptive analytics. Hospitals in developed and developing countries have started to use predictive analytics only recently aided by the vast collection of medical records and increasing computer resources.

The predictive analytics brings much value to the hospital than the descriptive. Instead of just analyzing the past data and presenting the information by the descriptive, prescriptive analytics predicts the future outcome by analyzing the past data. It gives the hospital a great advantage by staying one step ahead.

Here are some of the ways in which hospitals use predictive analytics.

Predicting the risk of illness

Healthcare organizations use predictive analytics to find the probability of a person getting a medical condition in the future. With the use of the patient’s medical data and the historical record of that particular medical condition, predictive analytics can find the probability for the person diagnosing with the medical condition.

The healthcare organization can also find the progressiveness of a medical condition in a person given the medical record. This process of predicting the risk of a medical condition for the patient helps the doctor get ahead of the condition and cure it.

Avoid patient readmission

In addition to finding the risk of a medical condition for a patient, predictive analytics can find whether a patient is about readmitted to the hospital. After being cured by a medical condition, there is always a chance for a patient to be admitted to the hospital again for the same condition or a related one. That is, the condition reappears for some patients. Data analytics can accurately predict whether the patient is about to readmit to the hospital. This helps the doctor to fully address the medical condition of the patient and avoid readmission.

Manage Supply Chain

Hospitals require a variety of medical and non-medical products each day for smoothly running the operation. The stock and the supply chain should be managed efficiently so that no product is limited or much.

This is where predictive analytics comes in. The demand for medical products can be found with a good probability by using predictive analytics. And using the result, the hospital can stock up their products. Also, the supply chain process can be optimized with analytics to save time and money. Optimizing the supply chain process has been used in many sectors successfully for many years.

Boosting patient and hospital satisfaction

Data analytics is also used to predict when the hospital might get busy or when a patient might skip an appointment. This can be found by analyzing the previous record of the hospital and patient to find a pattern.

This could help the patient to know the busy time at the hospital and avoid it. It also helps the hospital about the patient’s appointment and schedule based on it. On the whole, this process of analytics helps better satisfy hospital staff and patients.

Developing new medicines faster

Developing a new drug is a time-consuming process. There is a chance that the current drug variant might fail. If it succeeds, then animal trials must proceed with success. Then human trial and finally, the FDA approval.

With the introduction of the big data approach to the drug trial, the drug trial process can be accelerated. The predictive analytics helps in finding whether the current variant of the drug will succeed provided the relevant data and thereby saving valuable time.

Analytics also helps in finding new insights with medicine. For eg: aspirin, a pain reliever is found to have the property to treat colorectal cancer by using different analytic techniques. There is a huge potential for analytics to play in the field of medical science.

Conclusion

Analytics is transforming the Healthcare sector in a good way and it is a good example of true value in using data analytics. In this decade, we will see hospitals using predictive analytics often to improve patient care.

Categories
Data

How Predictive Analytics is transforming the insurance sector?

Data analytics is not only used in the technology sector now. It has permeated through every industry. Each industry is implementing data analytics in its own way and finding value in it. Insurance Industry is one such were data analytics plays a big role.

To be fair, Insurance companies have long used analytics to find the loss in property or to analyze the damage. But, with enormous computing resources and data resources available right now, companies can do much more with data analytics than just analyzing risk and damage.

Predictive analytics is the process of analyzing the past data to find potential future outcomes in the scenario based on the data. In insurance companies, predictive analytics is the most used.

Predictive Analytics in the Insurance Industry.

The predictive analytics is extensively used in the Insurance Industry for various purposes. It is used to identify customers who are at the risk of canceling insurance. Using predictive analytics with the customer data, we can identify customers who are likely to lower the coverage or cancel the insurance. It gives insurers the knowledge to give special attention to retaining customers.

Fraud in insurance companies is always a thing. Though there are various measures taken by these companies to control it, it is not successful. Predictive analytics and Prescriptive analytics can help companies to find malicious customers who are likely to commit fraud. With the customer data from various sources like social media, internet, etc, the companies can find potential fraud with a high probability.

Insurance companies are using analytics to find potential targets to advertise. With data about the demographics and people, the companies can find the market to target their insurance for the people. Previously, the companies had to use manual advertisements without any real knowledge about the outcome. Now, they can target specific demographics of people with the help of analytics to get better results.

Not only identify potential customers, but the companies can also give a personalized experience to the customers using the analytics. With descriptive and predictive analytics, insurance companies can find the behavior of the customers and can anticipate their needs. With this result, they can provide more personalized service to them. This makes the customers be loyal to the company and in turn increase the profit of the insurance companies.

With the analytics, the insurance companies can get ahead of their competitors. By finding new trends using predictive analytics, insurance companies can create new insurance plans, services, or products. The analytics companies can also optimize their pricing for the insurance plans using analytics to give better plans to the customers and in turn, increase their profit. This gives a serious edge for the companies to grow and be top in their domain.

The analytic companies can also identify potential customers for risk before providing insurance services to them. With the data about the customer from the banking sector, and online, the insurance companies can find the customers who might be a risk to provide insurance with a certain probability. This helps the companies to make better decisions when it comes to providing insurance.

Conclusion

As you can see, the insurance companies are using predictive analytics in many ways to improve their business and provide better service to the customers. In this decade, as the data grow tremendously, we will see huge opportunities for not only the insurance companies but also all the organizations that use data analytics.

Categories
Data

Different open-source data analytic tools

Data Analytics is a growing field that is becoming a significant part of any business field. Due to this popularity, there are lots of tools and software that are available for you to do analytics. Some of them are open-sourced and some of them are proprietary. But, both have their own advantages and disadvantages.

When it comes to analytic tools, open-source tools are more popular than the proprietary ones. The reasons are it doesn’t have any lock-in by the vendor, free to use, and a large community of fellow developers to help. The open-source tools also have good if not better documentation and update support than the proprietary ones.

Here are the major open-source analytic tools that you can invest to improve your career.

Apache Hadoop

It is the go-to tool for processing a large volume of data. Hadoop is a Big data framework developed by Apache Foundation. Hadoop can process data efficiently with low hardware requirements. It has its own file system called HDFS (Hadoop Distributed File System) that handles the data storage process.

The Hadoop leverages the parallel processing by using Map Reduce programming. Hadoop is usually used in conjunction with the Apache Spark. It is a high performing memory processing engine.

To write the processing program in Hadoop, you can use any languages like c++, python, java, etc. This property gives Hadoop a great flexibility for developers. Used in more than half of the fortune 500 companies, Apache Hadoop is a must-learn for aspiring data scientists.

Mongo DB

The main function of MongoDB is for data storage and not analytics. But it does have some features that make it a good choice for real-time analytics. MongoDB is a NoSQL based database storage system. It means that you store data that has no structure in it and manipulate them using queries. Most of the data that you’ll be analyzing in real-time will have no structure. So, it makes MongoDB a perfect fit.

MongoDB can also be deployed in the cloud. It offers great flexibility of configuration. The main reason why MongoDB is used real-time analytics is that it stores data in the form of JSON objects. Most of the real-time analytics are written in Java or similar language. These applications can easily convert the JSON object to Java objects. The unstructured data can be accessed quickly in MongoDB, and it makes it a good candidate for dealing with real-time analytics.

MongoDB is the most popular data storage tool used in companies like Google, Flipkart, Amazon, and Facebook where real-time analytics is a major work.

R Environment

The R environment is a suite of software and libraries that are specifically used for data analytics. You will be working with the R-studio tool that uses the R programming language for working with your data. It has support for data storage, graphical facilities for data visualization, a variety of data analytics packages, etc. Most of the popular machine learning algorithms that you wish to deploy are present as a package in the R. It makes the work of data analyst easy as you can just install and work with the algorithms.

Other advantages of using R framework are it can run in an SQL server, has support for Apache Hadoop, cross-platform, highly scalable, and easily portable. Companies like Microsoft, Google, Amazon, etc are using R for the analysis of data.

Python

Python is not a tool but a programming language. It is currently the most popular programming language for data analytics. The popular packages in python for data analytics are Pandas, Numpy, Scikit Learn, SciPy, and Matplotlib. These packages are used in implementing machine learning algorithms seamlessly and without much work. Python also offers data visualization capabilities in the form of the Matplotlib package.

Some other advantages of learning python are its simple syntax, large developer community, cross-platform functionality, etc. Almost all of the tech companies are using python for data analysis. So, it is a good place to start for aspiring data analyst.

Conclusion

With analytics gaining significance day by day, there are a variety of analytic tools available for developers and analysts. It is essential to select and learn about a good tool to advance your career in data analytics.

Categories
Data

Learn analytics and become a data analyst.

Data Analyst is one of the high paying, in-demand jobs right now. Every company despite its field is becoming data-driven. They are trying to find value with their data. They can outsource the job to other companies or hire their own analyst team. Whatever the decision may be, a data analyst is the one who finds the value in data. Naturally, the demand for data analysts is booming. IBM predicts the demand for analysts will soar 28% by 2020.

If you want to know how to become a data analyst, then you have come to the right place. First, let me quickly give out the definition for the data analysis. As an analyst, you’ll be working with a huge volume of data to derive valuable insights for the organization using some analytic technique. This process is data analysis. The analyst role is not only exclusive to the data. There are a few types of analyst role present,

Data Analyst – As I said, the role of the data analyst is to retrieve valuable information from the data.

Business Analyst – It is closely related to the data analyst, but business analyst retrieves useful information that drives the performance of the business. They find insights on how to refine the day to day operations, mitigate risk, etc. They are more related to the business side than the analytics side.

Quantitative Analyst – They work in the financial industry to predict the stock or bond price, find any potential risk in investment, etc.

As you can see, there is a variety of data analyst role. Whatever you decide to become, you should have the following capabilities.

Have strong knowledge in Mathematics

Though there are tools and software that executes the mathematical algorithms, it is vital for a data analyst to have strong mathematical skills. Whatever analysis you may do with the data, it all has math at its core.

Data Cleaning uses basic statistics like mean, mode, median, etc. Linear algebra like matric manipulation is used in the development of neural networks. Gradient descent in the regression algorithm uses calculus techniques. Graphs and tree structures in discrete mathematics are used in the optimization algorithms.

The analysis process literally touches every area of mathematics. It is easy to get disappointed to see the hurdle in the form of maths. But as I said, there are many tools and software that mask the difficulties in implementing the algorithms. Scikit-learn, TensorFlow are free popular machine learning frameworks that don’t require any mathematical skills to work. But if you want to become a top person in the analytical field, you should take the difficult path and have the knowledge of mathematics.

Know your technologies

You should not only have mathematical skills but know how to deploy the maths using technology. You will work with any one of the popular technologies like excel, python, SQL, Tableau, Hadoop, etc in your company environment. These technologies help you in executing the analytical process like cleaning, visualizing, analyzing, etc seamlessly. So, have working knowledge in all the process of analytics from cleaning to visualization using a popular technology to land a role as a data analyst.

Data Analysts should also have knowledge of programming. All the popular tools like TensorFlow, Hadoop, spark are based on coding. So learning a programming language is a must. For analyzing and visualizing data, Python, and R is a popular programming language to learn.

Learning by doing

Learning by doing is the best way to learn anything. So after you master your skills in mathematics and analytics technology, practice your skills by solving real-world problems. You can check my previous post on a “data analytics project to advance your career” to have an idea about it, or there are a number of projects hosted on the Kaggle website that you can work on. Having a working knowledge will go a long way in grabbing the role of the data analyst.

Conclusion

Learning about analytics may seem difficult as it involves mathematics and programming. But once you start learning about it, you’ll be amazed by the potential of it. It is the most popular domain right now, and analysts are in high demand. There are numerous sources available on the internet to start your learning. It is never too late. Start now and become a part of this wonderful domain.

Categories
Data

Trends in Data Analytics

Data Analytics has seen tremendous growth in the last 5 years. Data Analytics is now not just a luxury item that only big organizations can deploy. In the last decade, the availability of cloud, open-source frameworks, and tools made it possible for even small companies to deploy data analytics. With many small to medium businesses adopting it, the Data Analytics will continue to grow and become ubiquitous. In this post let’s see some of the trends in analytics that will shape up the future of modern business in this decade.

Augmented Analytics

A new step in improving data analytics, Augmented analytics is the process of using AI and ML to automate and enhance the data analytic process. In today’s analytics, data extraction, preparation, and analysis are done by data scientists. It is a tedious task and consumes lots of man-hours that could be spent elsewhere.

By integrating ML algorithms and NLP models, these tasks could be automated and data scientists could concentrate on more valuable tasks. The integration of ML in the data extraction and preparation also reduces the error when comparing it with manual data preparation.

The NLP models are used here to interact and interpret the data and result in the data analysis. When the data is generated on a large scale, it takes valuable human hours to analyze it. Whereas, a high functioning NLP can do it in less time with fewer errors. This is how Augmented Analytics enhances the data analytics process. It is predicted to grow significantly in this decade.

Embedded Analytics

The traditional analytics takes time in extracting data from other applications, analyzing it, and giving valuable insights. What if the analytic process is embedded into the user application so everything happens in real-time. It also gives insights in real-time, so decisions could be made faster.

For a long time spreadsheets have been hindering the process of visualizing the data. If the analytic tools can be integrated into the user application, it takes care of all the processing and visualizes the result that could be understood by humans rather than presenting as a number.

The embedded analytics technology has already been implemented in a lot of applications like google analytics, salesforce analytics platform, and amazon e-commerce site. These platforms are good examples of embedding analytics as they provide real-time information about the product to the customers. This trend will grow significantly and impact the process of decision making in the next 2 to 3 years.

Blockchain in Data Analytics

Whenever you are dealing with a large volume of data, you are likely to be faced with a major issue called data privacy. The privacy of user data is currently the nightmare for all tech companies right now. When you have complete control of data as an organization, people will get concerned and privacy issues will grow. This problem can be solved by using Blockchain.

A blockchain-based data storage system provides complete transparency and security for all the data stored in it. It is the reason why the cryptocurrency is becoming popular than the traditional centralized currency. But realistically, the blockchain-based data storage system hasn’t matured enough to be implemented with a data analytics system. But still, it has a very high chance to be successful in the near future.

The use of predictive and prescriptive analytics

In the current data analytics world, the use of predictive and prescriptive analytics is not popular. Most organizations are still using descriptive and diagnostic analytics to give a summary of their past performance and find any problems in it. They have not touched the full potential of the analytics which is predictive and prescriptive.

The predictive analytics is used to analyze the past to predict the future based on the scenario. The prescriptive does one more than that by presenting the organization with the best possible way to take to improve the business. This type of analytics will see a big growth in the next few years as companies start to realize the value in it.

Collaborative Business Intelligence

Today the business managers who take decisions and the analyst who create the result to take decisions are working in a different environment. There is a gap between. It is not essentially a big problem, but there is room to be improved here. Collaborative business intelligence is the integration of analytic tools and the collaborative tools to make sharing the results easier and faster.

The collaborative tools are nothing but social media tools to share the information but to the respective manager. It makes the decision making process faster and improves the performance of the business. This trend is sure to grow in this decade as companies start to see the value in collaborative business analytics.

Conclusion

I have listed only some of the trends in data analytics that will affect the organization in this decade. As data analytics is a vast domain, there are many trends and varieties of analytics that will become possible. Each organization is different and each organization use the data analytics differently according to their work. The analytics platform is evolving day by day. This is the reason why we will see many trends in the near future.

Categories
Data

The strategy behind Data Analytics.

If you’ve been reading my post, then you’d know that data analytics is the most disruptive technology impacting all the organizations. Everywhere around the world, Businesses are trying to become data-driven. That is, they are using the data to improve their performance.

For a new business trying to adopt data analytics, a strategy must be created before delving into it. Without a strategy, a variety of things could go wrong implementing the data analytics. Companies could get stuck in one phase of the analytic process, or they could get drowned in the overwhelming amount of data, or they may choose a wrong algorithm for their use case. To avoid these scenarios, a business must have a data analytics strategy in place to govern the process.

Necessities required before developing an analytics strategy.

Before developing a strategy, an organization should assess if they have the following capabilities.

The business needs to make sure that they have the analytic capabilities to do the analytic process. Capabilities like tools, analyst team, data, etc should be present in the business. The infrastructure within the organization should support the analytic process so that every person in the organization gets access to the analytic tools. The culture within the organization also should enable the employees to know the value of analytics and to contribute to it.

Analytic Strategy

After a company verifies that it has the necessities required for developing a data analytics strategy, it can start forming the strategy. A good way to develop a strategy is to answer the following questions.

What problem are you trying to solve?

A company should clearly identify the problem before starting to collect the data. Everything in data analytics from data selection to algorithm selection depends upon it. Data analytics can improve a business in a number of ways like identifying potential customers, predicting performance, etc. It may look silly but a company should have a clear goal in their objectives and set their priorities right.

What data do you need?

After identifying the goal, determine the data that you need for achieving your goal. It is one of the daunting tasks in the analytics process. Identifying data and make it cleansed is not easy even for a big analytic team. This task should be done carefully as it affects the further process if not done right.

Wrong data or noisy data significantly affects the quality of the results you get. However good maybe your algorithm or analytic process, if the data is not good then your result will be not.

How will you analyze the data?

Once you identify your goal and the data to achieve the goal, you need to define how you are going to convert the data into valuable insights. You should define the tools, algorithms, and necessary process. This is the core part of the strategy build and an easy part too.

The analytic process may seem daunting but there are various tools and software that abstracts the complexities present in the analysis. The only difficulty here is to choose the right algorithm and tool for analyzing the data you have to meet your goal.

How will you present the result?

Getting the required result is only half the story. The rest is how you present them. Mostly the results from the analytic process will be in mathematical form. This cannot be easily understood by the management team. So, you need to pick a visualization technique to present your result.

There are lots of tools available for this step. These tools are mostly integrated with analytic tools itself. So, this process will be easy once you identify the way you should present your results.

Conclusion

The Strategy building task is the reverse of the analytics process. You identify the goal first and work your strategy upon that. After creating the strategy for your analytic process, you need to create an action plan and execute it. In addition to forming your strategy, keep key decision-makers of your organization involved in the critical stages of the analytic process. It helps them make a better decision.

Every organization implementing an analytic task should have a strategy. It lets them execute the analytic process seamlessly. Keep in mind that the action may not go as planned in the strategy. You may not meet your goal, or the result may lead you to a different path. If that happens, examine your strategy again to get the desired result.

Categories
Data

Why Customer Data Analytics matters?

Customer Data Analytics is the process of analyzing the customer data by a business to create a profile about the customer. With that profile, a business can predict user behavior and can target personalized services for them.

The business has long understood the value of customer data. By creating a personalized experience with the customer data, they can increase their loyalty with the customer and thereby increase their revenue.

Businesses use various analytic techniques to analyze customer data from descriptive to prescriptive. Each analytic gives different information about the customer. Descriptive analytics is done to get the overall information about the customer. Predictive and Prescriptive analytics is done to find the customers future decisions.

How does Business get customer data to analyze?

There are various ways in which a business collect data about their consumer. The first and the most fruitful way is,

They simply ask for your data

Whenever you buy a product or service from a business. The company directly or indirectly asks permission to collect your information. When you sign up for a new service, through your Facebook or Gmail profile, you give your information to the service provider. When you accept the terms and conditions, you agree to provide your information. These are the direct ways of accessing your data.

Some companies may also illegally procure your data without your consent. They use tracking software to collect information without your consent. It is illegal as it invades your privacy. User privacy is a huge issue that the tech world is dealing with right now.

Procuring you data from data companies

This is probably the easiest way for a company to get your data. There are companies whose only job is to collect and sell consumer data. These companies are called data companies. Google and Facebook are good examples of this. They give out free services and in turn, they collect your data. These data are used by them to target ads and are sold to other companies for a price.

I said this is easy cause the data companies collect and process your data to a structured form. They eliminate outliers and noisy data to store it in a structure that is easy to analyze. For a business that needs customer data, it is easy to procure it from these data companies for a price.

The other important ways of acquiring customer information are e-mail tracking, web crawling, and from companies own records.

What type of customer data do companies use?

There are many customer data available. To choose the right kind of data for the analytic process is vital to get valuable information.

Customer data from their words,

These are the data about the impression of a product or service by the consumer. The consumer usually posts this information on the social media, forms, and blog posts. This information gives the business the idea of the customer’s reaction to their product. The business can improve its product or service by using this type of data.

Data from the website,

These data are procured from the customer’s interaction on their website. Data like time spent on a page, link clicks, etc are acquired from this type. It gives the company an idea about how engaging their services are and where to improve. It also gives an idea of what interests the consumer. Major e-commerce sites are using this data to identify user behavior and recommend products.

Data from a transaction,

This type of data is particularly useful for retail companies. For every transaction, the customer makes buying goods/services from a company, it increases the understanding of the consumer by the company. By better understanding, a company can better recommend products/services to the customers.

How a company implements consumer analytics?

Major tech companies have their own analytics team for analyzing consumer data. This trend is also followed in major companies in all the domains. They have the resources and knowledge to create their own team of analysts to perform consumer analytics.

Small to Medium companies that don’t have the knowledge or resources outsource the analytics process to other analytics companies. Sigma data systems, Diceus, LatentView Analytics are some of the major analytic companies.

Nowadays, tools and Softwares are available for companies to make the analytics task easier. The analytic tools are now integrated with the CRM tools. Salesforce CRM and ZOHO CRM are good examples of this type.

Conclusion

Consumer data is becoming more and more valuable for a company. It lets the company know about their customer and interact with them. By understanding, the companies can in turn increase customer retention, & customer conversion, improve sales, and increase brand loyalty. So, in today’s world, analyzing customer data is a must for a business in order to stay above the competition.

Categories
Data

How data analytics brings success to a company?

There is no denying that businesses profit from using data analytics. In today’s competitive world, satisfying customers and increasing revenue are becoming a priority for any business. Data analytics helps the business just to that. As businesses become aware of the value of data analytics, they tend to gravitate towards it.

Here I’ve consolidated some of the benefits that business gets from using data analytics.

Better Decision making

With the vast amount of business data available these days, a business can make use of it to make better decisions. Prescriptive Analytics is a part of data analytics that analyzes the past data for a scenario and guides the business towards the best course of action. It helps the business in avoiding risk and make better decisions.

Traditionally companies have used intuition and basic research to make critical business decisions. But with the amount of data available now, it is appropriate for a business to use analytics to help them make better decisions. It also helps the companies to avoid potential risks that may happen when making a decision. Prescriptive analytics with predictive analytics analyze the past to find insights and make the companies better in the future by making better decisions.

Prescriptive analytics can be especially useful in the healthcare industry to evaluate the data and make better decisions. IBM Decision Optimization software does exactly this by helping many healthcare organizations to make optimized decisions.

Improving Performance

Analytics helps to optimize the process in every area of business from stock management to sales. Delivering a service or product is a routine task for a company. But with analytics, a company can measure if they are able to meet customer demand. Also, they can find future demand for a service or product. With this measurement, they can better adjust their operations to meet the demand.

Also, by finding the demand for a product, a company can better optimize their inventory and manufacturing process. This measurement also helps the business or a company to better optimize the prize of a product throughout its lifecycle to increase the profit. The analytics is even applied to the supply chain management and manufacturing process of a business. It helps in optimizing the process by driving down the cost of manufacturing and supply chain.

Data analytics is popular in the retail business where it has increased the performance significantly. Walmart is one such company to use data analytics to increase their sales and optimize their process.

Personalized service

Customer satisfaction is one of the things that all the business are trying to achieve. Better customer satisfaction leads to better revenue. Data analytics helps to create a personalized experience for different customers using customer data.

Businesses are trying to personalize the experience throughout the customer’s journey of interactions with them. From clicking the website to buying a product, the customer’s journey is personalized. It is possible with the help of predictive analytics. Finding patterns between the customers can lead the business to predict the potential customer’s interaction with them. Using this insight, a business can successfully personalize the experience for a customer within the business.

In today’s world, almost all companies using data analytics are giving a personalized experience to the customer. It is the main reason why companies like Spotify, Netflix, and Amazon are on top of their game. They were the first in their domain to extensively use data analytics to give a personalized recommendation to the customers.

Customers are expecting companies to know them and give them personalized recommendations even though it may lead to companies owning customer data. So, it has become a necessity for all the business to use data analytics in order to stay above the competition.

Conclusion

I’ve just listed the primary benefits of data analytics to a company. Apart from this, data analytics helps in creating quality data and making it easy to access it, increases the quality of services offered, implements preventive measures for potential risks, and much more. Many companies have used data analytics only on a small scale. Still, there is room left for data analytics to grow and be useful in many ways to an organization.

Business is important to a country for various reasons like boosting the economy, increasing jobs, etc. And Business needs some strategy to improve its performance and growth. This is where data analytics comes in. Instead of just wasting the data, a company can use it in much more valuable ways using analytics. In my opinion, Analytics is the biggest change for growth that happened to any business in the past decade. With small to medium businesses becoming aware of it, analytics is only poised to grow in this decade.

Categories
Data

The cools ways in which Spotify recommends music to you.

Today, Spotify is the world’s leading music streaming company with over 150 million years. When it was launched in 2008, streaming music via the internet was already there. But what made Spotify unique was its use of data analytics to curate playlist.

Spotify generates an astonishing amount of data each day. It uses these data effectively to generate insights. With these insights, it creates a personalized playlist for each user based on their interest. This efficient use of data made Spotify different from iTunes, Pandora, and other services present at that time.

How does Spotify use analytics? What kind of analytics does it use on what type of data? Let’s see.

Natural Language Processing to identify popular music

If you’ve been using Spotify, then you know it creates a new playlist every week for you called Discover weekly. The playlist has songs that you like but you’ve never heard of. How does Spotify find new songs that you like?

One way is it uses Natural Language Processing. We know NLP is how the computer understands human language. Spotify uses NLP to crawl through the web and blogpost to understand what songs or artists do users talk about. The exact mechanism of how Spotify uses NLP is beyond the scope of this post.

After identifying new songs or artists, Spotify then assigns weight to it based on the popularity that changes each day. Then, it uses other techniques like collaborative filtering or audio analysis to recommend the new songs to the respective users.

Audio Analysis to find similar songs

Spotify finds new songs using NLP but how does it actually recommend to the users?

For every song present, Spotify uses neural network analysis to find different characteristics about it. Data such as tempo, loudness, time, etc about every song are present in the Spotify database.

For every new song that comes into Spotify, it uses the same mechanism to find the characteristics of the song. If the characteristic of the new song matches the characteristic of the song that you liked or saved, then Spotify recommends the song to you.

For this process to work, Spotify needs to collect data about you. Data such as the songs you’ve liked, saved, listened to repeatedly, artists you’ve visited are collected and used to find similar songs.

In this process convolutional neural network plays a big role. It analyses the raw audio data to find various characteristics about it. Convolutional Neural Network is a type of deep learning algorithm that is primarily used to analyze the visual data. Here, Spotify modified the algorithm to work with audio data.

Netflix is another big player that uses this kind of analytics technique to recommend similar movies.

Collaborative Filtering

Analyzing raw data using neural networks is just one of the processes to find similar songs. Collaborative filtering is another mechanism to find the songs that the user may like.

It is a cool technique that finds the common songs that different users like. Say you like songs a, b, c, and your friend likes songs c, b, e. Then, it is highly likely that you like the song e cause there is a high similarity with your friend. This is how collaborative filtering works. But Spotify uses this mechanism on all the songs and users present.

This is a highly complex task on a large scale. Spotify uses various data mining tools and algorithms to find similar songs between different users and recommend to them.

These are the main ways in which Spotify uses data analytics in recommending new songs. There is also another important way Spotify uses analytics to provide value. It is in the Spotify artist app.

Spotify artist application

Spotify has a different app just for the artist creating music. The app gives real-time statistics about how their music is performing. Statistics like the number of downloads, number of users listening, likes, etc about their songs are analyzed and displayed to the artists.

The application is like Google Analytics but for Spotify and songs.

The future

With yearly roundup music, variety of curated playlist, songs based on weather, mood, and condition developed using analytics, Spotify is purely a data-driven company. With more users joining Spotify, and new songs created each day, the use of data analytics is only going to grow in the future for Spotify.

Spotify has won the battle in the music streaming thanks to data analytics. People think that Spotify knows more about their taste that they do. Now it is time to win the battle of Podcast for Spotify. The popularity of podcasts has grown significantly over the last five years. Knowing this, Spotify has invested hugely in the podcast industry. Spotify has recently started to recommend the podcasts for its user’s using analytics. They’ve even created podcasts for your pets. Yes! you’ve heard me right. Podcast for your pets, and music for you.

The future is bright for Spotify. It all happened because Spotify was one of the first to understand the value of analytics and use it on their platform.

Categories
Data

Data Analytics in Retail Industry.

The competition in the retail market is really high. With the need to satisfy customers and increase profit, the retail companies are using data analytics in all stages of the retail process.

The online retail companies like Amazon, e-bay were one of the first to use data analytics in the retail industry. Data Analytics made online retail companies grow exponentially. Seeing the success and the value in the analytics, offline retail industries like Walmart, Target have started to use data analytics to fend off the competition.

There are a number of ways in which both the online and offline retail industries are using data analytics. Let’s see how a few works.

Demand Prediction

Data analytics is used to predict future demand for a product. Using historical customer buying data, predictive analytics can be used to predict the demand.

It is a very good inventory management technique. Previously, the retailers had to guess based on season and other unreliable techniques. But now, with data analytics, the retail companies can better predict how a product could perform in the future.

The demand prediction also gives the retail industry an idea about how to manage their inventory. When a product sale is projected to grow, then the retail could pile up the product and vice versa.

Price Optimization

When you predict the demand and have the inventory numbers, you can optimize the price. This process is popularly known as Markdown optimization. Previously, the companies had to guess to set the price for a product based on the season, demand, and inventory stock. But now, data analytics helps to set the price of a product at the right time to maximize sales.

Personalized Recommendation

To increase revenue, retail companies are using analytics to give personalized customer experience. Based on the customer’s buying pattern, analytics is used to recommend products to the customers.

Along with providing special discounts for individual customers, personalized recommendations increase sales and revenue in the retail industry. It also increases customer retention and customer conversion rate. Moreover, today’s customers like to have a personalized experience when buying. Data Analytics makes it happen.

Amazon has had tremendous success by using personal recommendations with the help of data analytics.

Optimize supply chain and operations

Another area where data analytics gives its contribution to the retail industry is optimizing supply chain and operations. The retail companies have a trove of data from log files, sensors, and machines. Using this data, a company can optimize its supply chain management with the help of analytics. The transportation of goods, stock management, and manufacturing can be optimized using analytics provided with right data.

In the same way, companies can use data analytics to improve their operations. With data analytics, hidden patterns or insights can be recovered that will be useful to the retail companies to make better decisions to improve their operations.

Increase Return Of Investment

Retail companies use predictive analytics to asses their current state, optimize operations, and predict future performance. By accurately predicting future events, the companies can use it to increase their Return Of Investment.

Increasing the return is the main value provided by the analytics to the retail companies. It is the main reason why all the companies are using analytics and it will be the reason that drives the growth of data analytics in this decade.

Conclusion

As you can see, data analytics is now deeply integrated into all the processes in retail industry from stock management to sales. And it is providing great value to the industries. The retail industry mainly outsources the analytic process to other companies. For eg: Microsoft is the main provider of big data solutions to the retail giant Walmart.

To get an advantage over their competition, retail companies are increasingly using data analytics. In this decade, it is necessary for even small to medium-sized retail companies to integrate data analytics. It is unsurprising to see the change that has happened in the retail industry in the past decade. With data growing exponentially, it’s safe to say that data analytics is going to play a big role in the retail industry in the coming decade.