The Master guide which gives you an idea about Data Analytics and its use in real-time industry
How did Data analytics evolve? In the current century, most of the organizations have a very huge amount of data than ever at its disposal. But deriving meaningful insights from it to improve operational efficiency remains a great Challenge. Data Analytics appears to be a practical solution to this problem.
What is Data Analytics? Data Analytics is the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision making.
It is a trending practice that many companies are embracing and adapting to gain competitive advantages over business rivals and drive new revenue. However, it is first essential to first understand its landscape (types, challenges, and opportunities) before putting it into the application.
From a market perspective, it’s necessary to choose the right type of Data Analytics tools for data analysis.
Data Analytics Tools can be distinguished into 2 basic types:
Simple Data analytics: Mainly focuses on the description of an event that has already occurred in the past, finding its root causes and offering insights.
Complex Data Analytics: It can be further sub-categorized into.
Predictive Analytics: Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques such as data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future.
There are several steps involved in the predictive Analytics and they are described below.
Define Project: Define the project outcomes, deliverables, efforts required, business objectives, identify the data sets which are going to be used.
Data collection: Data mining for predictive analytics prepares data from multiple sources for analysis.
Data Analysis: This involves the process of inspecting, cleaning, Transforming, Modelling data with an objective of discovering meaning information, arriving at conclusions.
Statistics: Statistical analysis enables us to validate the assumptions, hypotheses and test them using standard statistical models.
Modeling: Predictive Modelling provides the ability to automatically create accurate predictive models about the future. There are also options to choose the best solution with multi-model evaluation.
Deployment: Predictive Model Deployment provides the option to deploy the analytical results into the everyday decision-making process to get results, reports, and output by automating the decisions based on the modeling.
Model Monitoring: Models are managed and monitored to review the model performance to ensure that it is providing the results expected.
Prescriptive Modelling – subsumes the results of predictive analytics to suggest a corrected course of action that can be used to take advantage of the predicted scenarios.
Now let’s talk about a few o real-time use cases of predictive analytics in the industry:
Churn prevention: When a business loses customers, it needs to bring new customers in to replace the loss in revenue. And that can get very expensive because the costs of new customer acquisition are usually much more expensive than existing customer retention. Predictive analytics help to prevent churn in your customer base, by identifying signs of dissatisfaction among your customers, and identify those customers or customer segments that are at the most risk for leaving. Using that information, companies can then make the necessary changes to keep those customers happy and protect their revenue
Customer relationship management (CRM): Predictive analysis applications are used to achieve CRM objectives such as marketing campaigns, sales, and customer services. Analytical customer relationship management can be applied throughout the customer’s life cycle, right from acquisition, relationship growth, retention, and win back.
Health Care: Predictive analysis applications in health care can determine the patients who are at the risk of developing certain conditions such as diabetes, asthma and other lifetime illnesses. The clinical decision support systems incorporate predictive analytics to support medical decision making at the point of care.
Fraud detection: Can be used to find inaccurate credit applications, fraudulent transactions both done online and offline, identity thefts and false insurance claims.
Sentiment Analysis: It’s very difficult to be everywhere at all times, especially in the online world. Likewise, capturing and reviewing everything that’s said about your company or organization is virtually impossible. However, by combining web search and crawling tools with customer feedback and posts, you can create analytics that gives you a picture of your organization’s reputation within your key markets and demographics and provide you with proactive recommendations as to the best ways to enhance that reputation.
Customer Lifetime Value: One of the more difficult things to do in marketing is to identify those customers that are going to spend the most money, in the most consistent way and over the longest period of time. This kind of insight allows companies to optimize their marketing to increase their share of that segment of the business, and gain those customers that will have the greatest lifetime value to your company.
Conclusion: In a nutshell, I believe an individual with a proper understanding of tools with their use case along with the Data visualization skills would definitely excel in the field of Data analytics.