What is Data Analytics?

Data Analytics is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to make more informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

Data Analytics refers to an assortment of applications from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it is similar to business analytics, another umbrella term for approaches to analyzing data - with the difference that the latter is oriented to business uses, while data analytics has a broader focus.

Advantages of Data Analytics

The advantages of Data Analytics are in boosting business performance by:-

• Increasing Business Revenues
• Improve Operational Efficiency
• Optimize Marketing Campaigns
• Customer Service Efforts
• Respond more quickly to emerging market trends
• Gain a competitive edge over rivals

Forms of data used for Data Analytics:-

• Historical Data
• Real-time Data

Data Source Types:-

• A mix of Internal Systems
• External Data Source

Data Analytics Applications can be divided into two categories:-

1. Exploratory Data Analysis (EDA), which aims to find patterns and relationships in data
2. Confirmatory Data Analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false

We can also consider this another view for categorization

1. Quantitative Data Analysis, which involves analysis of numerical data with quantifiable variables that can be compared or measured statistically.
2. Qualitative Data Analysis, which is more interpretive - it focuses on understanding the context of non-numerical data like text, images, audio and video including common phrases, themes and points of view.

More advanced types of Data Analytics include:

1. Data Mining, which involves sorting through large data sets to identify trends, patterns and relationships
2. Predictive Analytics, which seeks to predict customer behaviour, equipment failure and other future events.
3. Machine Learning, an artificial intelligence technique that uses automated algorithms to churn through data sets more quickly than data scientists can do via conventional data modelling.

Applications where these Advanced Data Analytics are applied:-

• Big Data Analytics, where data mining, predictive modelling and machine learning are applied to sets of big data that often contain structured and unstructured data.
• Text Mining - It provides a means of analyzing documents, emails and other text-based content.

Usages or examples of Data Analytics:-

• Fraud and Identity Theft Protection - It is done by banks and credit card companies by analyzing withdrawal and spending patterns
• Clickstream Analysis - It is done by e-commerce companies and marketing service providers to identify website visitors who are more likely to buy a particular product or service based on navigation and page viewing patterns
• Forecast churn - Mobile network operators examine customer data to forecast churn  or movement of customers to competitors so that they can take steps to prevent defections to business rivals
• CRM Analytics - Companies engage in CRM Analytics to segment customers for marketing campaigns and equip call centre workers with up to date information about callers.
• Health Records Mining - Health Care Organisations mine patient data to evaluate the effectiveness of treatment for cancer and other diseases.