What is Data Analytics?
Data Analytics is the scientific study of analyzing data in order to enhance the growth of the business. The technique and tools used to extract data from various sources to analyze the behavioral patterns of the audience depends upon the needs and requirements of the organizations. Trends and metrics are revealed in Data Analytics which would otherwise be lost in the mass of information. Therefore, in order to increase the overall efficiency of a business or system this information can further be used to optimize processes. You must be aware of the term ‘Data Analysis’ as it is also used as a term for Data Analytics.
Types of Data Analytics
The following are the four primary types of Data Analytics. Each is different from one another and holds a different position in Data Analytics.
- Descriptive analytics
Answer to the question such as ‘what happened’ is answered with the help of Data Analytics. This summarizes large datasets to describe outcomes to stakeholders. These strategies can help track successes or failures by developing key performance indicators (KPIs).
- Diagnostic analytics
Answer to the question such as ‘why things happened’ is answered with the help of Diagnostic Analytics. Diagnostic Analysis takes the findings from descriptive analytics as well as finds the cause. It is further investigated that, why things got worse or better.
- Predictive analytics
Answer to the question such as ‘what will happen’ is answered with the help of Predictive Analytics. In order to identify trends and determine if they are likely to recur, historical data is used by these techniques. It is the technique that provides insight into what may happen in the future.
- Prescriptive analytics
Answer to the question such as ‘what should be done’ is answered with the help of Prescriptive Analytics. It is the technique that relies on machine learning strategies in order to find patterns in large datasets.
Top Data Analytics Tools
R programming is a Data Analytic tool used for statistics & data modeling. Various platforms such as UNIX, Windows, and Mac OS support R programming. In order to automatically install all packages as per user-requirement various tools are provided by R programming.
It is an open-source & object-oriented programming language. Python is preferred as it is easy to read, write and maintain. Various machine learning and visualization is provided by it.
It is a free software that allows the public to publish interactive data visualizations to the web.
QlikView is the Data Analytic tool that provides in-memory data processing with the end-users receiving the results quickly.
SAS is a Data Analytics tool which is used as it is easily accessible and can analyze data from different sources. SAS provides an environment for data manipulation and analytics.
Applications of Data Analytics
One of the finest example is online shopping. When you search for an alternate options while shopping online, data analytics provides access of your preferences, location & purchase history to the online sites and therefore they offers you items which you are likely to go for in your suggestions. This helps the organizations to make better decisions by gaining the knowledge of the customer’s behavior.
The dislikes, the relationships, and the likes of the users are all known by the gaming companies through Data Analytics. Data Analytics plays a major role in collecting data to optimize and spend within as well as across games.
Healthcare is one of the best use case of Data Analytics. The hospitals faces the challenge to cure as many patients as possible keeping in mind the improvement of the quality of care with cost efficiency at the same time. With the help of Data Analytics, Instrument and machine data are used in order to track as well as optimize patient flow, treatment, and equipment used in the hospitals.
Data Analytics helps the Education centers to learn about the students preferences and assessing their needs. Data Analytics even help the education centers evaluate their students’ talents and scores.
Online location tracking is the use case of Data Analytics. While you order food online and track the location of the food from being dispatched till the delivery status, you use Data Analytics which captures data in real-time. It helps the online retailers to improve your user experience.
Advantages of Data Analytics
Detection as well as correction of errors
Through data cleansing, Data Analytics serves to detect and correct the errors from data sets which consequently improves the quality of data benefiting both customers and institutions including banks, insurance and finance companies and several others.
Saves Memory spaces
Data Analytics helps to eradicate duplicate data sets thereby saving large amount of memory space which helps the organizations to save their costs. It is a cost effective technique used by the companies.
Data Analytics serves the online shopping websites by applying Machine learning algorithms in order to display relevant advertisements to the users based on their historic data and purchase behavior.
Data Analytics serves the banking sector by reducing the fraudulent customers by identifying them based on the analysis of their historic data. This helps them to identify whether to issue loan or credit cards to the applicants or not.
Data Analytics is used by security agencies for surveillance and monitoring purpose which helps them to prevent any crime or calamity.
Optimize the customers experience
Organizations are shared with information or data by its users in order to know them in a better manner and make decisions to maintain a long-lasting customer-company relationship. A seamless experience is provided by the companies to its users through these data. These data are extracted through data analytics by the organizations.
Delivering products in real-time
Data analytics helps the companies to stay competitive during the development of new technologies and the meet the demands of the market by providing the product in real-time even before it is requested.
Personalization in multi-channel service
It has become extremely difficult and exhausting to deal with the rapid evolvement of the customers nowadays within the era of digital technology. This the phase when data analytics comes into play. Data Analytics helps the companies to build loyal customers by making them feel valued and react in no time. Data Analytics provides deliver personalization in a multi-channel service environment in order to help the organizations understand the customer’s behavior and their need in real-time.
Optimize and improve the efficiency
Businesses uses advanced analytics techniques in order to improve their productivity, efficiency, optimize as well as improve the operational efficiency of their workforce. The usage of Data Analytics helps the organizations with designing and controlling the process and making sure that the expectations and demands of the customers are being achieved.