Analytics is the discovery and communication of meaningful patterns in data. Especially, valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming, and operation research to qualify performance. Analytics often favors data visualization to communicate insight.
Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Especially, areas within include predictive analytics, enterprise decision management, etc. Since analytics can require extensive computation(because of big data), algorithms and software harness the most current methods in computer science.
In a nutshell, analytics is the scientific process of transforming data into insight for making better decisions. Data Analytics aims to get actionable insights resulting in smarter decisions and better business outcomes.
It is critical to design and built a data warehouse or Business Intelligence(BI) architecture that provides a flexible, multi-faceted analytical ecosystem, optimized for efficient ingestion and analysis of large and diverse data sets.
What is Data Analytics?
In this new digital world, data is being generated in an enormous amount which opens new paradigms. As we have high computing power as well as a large amount of data we can make use of this data to help us make data-driven decision making. The main benefits of data-driven decisions are that they are made up by observing past trends which have resulted in beneficial results.
In short, we can say that data analytics is the process of manipulating data to extract useful trends and hidden patterns which can help us derive valuable insights to make business predictions.
Data Analytics technique and Life Cycle
Use of Data Analytics
There are some key domains and strategic planning techniques in which the Data ANlaytics has played a very important role:
- Improved Decision-Making – If we will have supporting data in favor of a decision that then we will be able to implement them with even more success probability. For example, if a certain decision or plan has to lead to better outcomes then there will be no doubt in implementing them again.
- Better Customer Service – Churn modeling is the best example of this in which we try to predict or identify what leads to customer churn and change those things accordingly so, that the attrition of the customers is as low as possible which is a most important factor in any organization.
- Efficient Operations – Data Analytics can help us understand what is the demand of the situation and what should be done to get better results then we will be able to streamline our processes which in turn will lead to efficient operations.
- Effective Marketing – Market segmentation techniques have been implemented to target this important factor only in which we are supposed to find the marketing techniques which will help us increase our sales and leads to effective marketing strategies.
Types of Data Analytics
There are four major types of data analytics:
- Predictive (forecasting)
- Descriptive (business intelligence and data mining)
- Prescriptive (optimization and simulation)
- Diagnostic analytics
Data Analytics and its Types
Predictive analytics turn the data into valuable, actionable information. predictive analytics uses data to determine the probable outcome of an event or a likelihood of a situation occurring. Predictive analytics holds a variety of statistical techniques from modeling, machine learning, data mining, and game theory that analyze current and historical facts to make predictions about a future event. Techniques that are used for predictive analytics are:
Basic Corner Stones of Predictive Analytics
- Predictive modeling
- Decision Analysis and optimization
- Transaction profiling
Descriptive analytics looks at data and analyze past event for insight as to how to approach future events. It looks at past performance and understands the performance by mining historical data to understand the cause of success or failure in the past. Almost all management reporting such as sales, marketing, operations, and finance uses this type of analysis.
The descriptive model quantifies relationships in data in a way that is often used to classify customers or prospects into groups. Unlike a predictive model that focuses on predicting the behavior of a single customer, Descriptive analytics identifies many different relationships between customer and product.
Common examples of Descriptive analytics are company reports that provide historic reviews like:
- Data Queries
- Descriptive Statistics
- Data dashboard
Prescriptive Analytics automatically synthesize big data, mathematical science, business rule, and machine learning to make a prediction and then suggests a decision option to take advantage of the prediction.
Prescriptive analytics goes beyond predicting future outcomes by also suggesting action benefits from the predictions and showing the decision maker the implication of each decision option. Prescriptive Analytics not only anticipates what will happen and when to happen but also why it will happen. Further, Prescriptive Analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option.
For example, Prescriptive Analytics can benefit healthcare strategic planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demography, etc.
In this analysis, we generally use historical data over other data to answer any question or for the solution of any problem. We try to find any dependency and pattern in the historical data of the particular problem.
For example, companies go for this analysis because it gives a great insight into a problem, and they also keep detailed information about their disposal otherwise data collection may turn out individual for every problem and it will be very time-consuming. Common techniques used for Diagnostic Analytics are:
- Data discovery
- Data mining
Future Scope of Data Analytics
- Retail: To study sales patterns, consumer behavior, and inventory management, data analytics can be applied in the retail sector. Data analytics can be used by retailers to make data-driven decisions regarding what products to stock, how to price them, and how to best organize their stores.
- Healthcare: Data analytics can be used to evaluate patient data, spot trends in patient health, and create individualized treatment regimens. Data analytics can be used by healthcare companies to enhance patient outcomes and lower healthcare expenditures.
- Finance: In the field of finance, data analytics can be used to evaluate investment data, spot trends in the financial markets, and make wise investment decisions. Data analytics can be used by financial institutions to lower risk and boost the performance of investment portfolios.
- Marketing: By analyzing customer data, spotting trends in consumer behavior, and creating customized marketing strategies, data analytics can be used in marketing. Data analytics can be used by marketers to boost the efficiency of their campaigns and their overall impact.
- Manufacturing: Data analytics can be used to examine production data, spot trends in production methods, and boost production efficiency in the manufacturing sector. Data analytics can be used by manufacturers to cut costs and enhance product quality.
- Transportation: To evaluate logistics data, spot trends in transportation routes, and improve transportation routes, the transportation sector can employ data analytics. Data analytics can help transportation businesses cut expenses and speed up delivery times.