Can You Think? Can You Make a Decision? If your answer is yes for both these questions ... Well you are an expert in Analytics

Analytics?
Analytics is nothing but thinking about all the scenarios that have happened, are happening, and might happen

Analytics also entails applying data patterns towards effective decision-making. In other words, we can say Analytics is a connection between data and decision making, which our mind has been doing since we were born. It is especially valuable in areas rich with recorded information and it relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

Look, we can make small decisions by analyzing small data in mind as well, but when we have to consider parameters that range from dozens to thousands and even lakhs, then we require specialized tools which can help us use simple formulas on a much larger scale.
Organizations may apply analytics to business data to describe, predict, and improve business performance.

Specifically, areas within analytics include Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics. Away from these we also have Enterprise Decision Management, Cognitive Analytics, Big Data Analytics, and Web Analytics, etc.

Analytics vs Analysis:
The analysis is focused on understanding the past; what happened and why it happened. Analytics focuses on why it happened and what will happen in the future.

Applications:
1. MARKETING ANALYTICS: Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting.

2. PEOPLE ANALYTICS: People Analytics uses behavioral data to understand how people work and how companies are managed. It is sometimes also known as Workforce Analytics, HR Analytics, Talent Analytics, People Insights, Talent Insights, Colleague Insights, Human Capital Analytics, and HRIS Analytics.
HR Analytics is the application of analytics to help companies manage human resources. Additionally, it has become a strategic tool in analyzing and forecasting Human Related trends in the changing labor markets, using various Analytical Tools.
It is becoming increasingly important to understand what kind of behavioral profiles would succeed and fail. However, there are key differences between People Analytics and HR Analytics. 'People Analytics solve business problems while HR Analytics solve HR problems/issues. People Analytics looks at the work and its social organization, while HR Analytics measures and integrates data about HR Administrative processes.

3. PORTFOLIO ANALYTICS: A common example of people analytics is portfolio analytics. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status (wealthy, middle-class, or poor) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan.
The least risk may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand, there are many poor that can be lent to, but at greater risk. Some balance must be maintained that maximizes returns and minimizes risks. The analytics solution may combine time series analysis with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment.

4. RISK ANALYTICS: Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict an individual's delinquency behavior and widely used to evaluate the creditworthiness of each applicant.

5. DIGITAL ANALYTICS: Digital Analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analysis, recommendations, optimizations, predictions, and promotions. This also includes SEO (Search Engine Optimization) where the keyword search is tracked and that data is used for marketing purposes.

6. HEALTHCARE ANALYTICS: This basically is a new field altogether, where analytical modeling might be used to predict an upcoming Pandemic or to track and predict an outbreak. For Example, we have seen various Covid19 trackers that have also started predicting what will be the future in say 3 months from today or what might happen if the exponential growth was not stopped.

Challenges:
As we all know, the data in today's scenario is dynamic and constantly changing with time. When we talk about Big Data Analytics, there is a need for better computing powers as well. We cannot create models using huge data on a computer with 2GB RAM.
The analysis of Unstructured Data Types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation.

Risks:
There might be a risk that a developer could profit from the ideas of work done by others. This can happen because there is no proper law regarding the ownership of data. Also, the data that we are providing to the companies and government can also be used for profit-making. We share so much data over the internet, which huge companies use to track us and sell our data to big marketing firms, which usually use that data to target products and services.