What is Data Analytics?
Data analytics is an autonomous or semi-autonomous inspection of data or content using sophisticated techniques and tools beyond those of traditional business intelligence (BI), to uncover deeper insights, make predictions, or produce recommendations. Techniques include data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.
Data analytics involves applying an algorithmic or mechanical process to derive insights and is used in various industries to enable organizations and companies to find answers in their data as well as verify (or disprove) existing theories and models. The focus of data analytics is the process of deriving conclusions that are strictly based on information a researcher already has. The core value proposition of analytics is the ability to provide insights that deeply impact the momentum and trajectory of a business.
Benefits of Data Analytics
- Create personalized customer experiences.
- Make operational excellence the new normal.
- Transform finance to boost the bottom line.
- Innovate products before competitors.
- Identify and mitigate serious business risks.
- Optimize the value of critical business assets.
What’s the difference between data analysis, data analytics, and data science?
The three are related, but distinct from each other. The focus of data analysis is processes and function. Data analytics, on the other hand, involves information, dashboards, and reporting. And, finally, data science touches data analysis but also includes data cleaning (or data cleansing) and preparation.
Why is big data analytics important?
When a business captures and analyzes data it can identify new opportunities and, in turn, make better business decisions, operate more efficiently, produce higher profits, and satisfy customers. Big data analytics helps companies in a variety of ways, including:
- Saving time and money: data analytics brings significant cost advantages to storing large amounts of data and the ability to identify more efficient ways of doing business.
- Enabling faster, more accurate decisions: speed and in-memory analytics, combined with the ability to analyze new sources of data, allows businesses to analyze information in real time and make decisions based on what’s learned.
- Enhancing product and service development: by gauging customer needs and satisfaction using analytics manufacturers and service providers have the ability to address specific customer wants and needs.
Types of Data Analytics
- Prescriptive Analytics: type or extension of predictive analytics used to recommend or prescribe specific actions when certain information states are reached or conditions are met.
- Predictive Analytics: analysis of big data to make predictions and determine the likelihood of future outcomes, trends or events.
- Diagnostic Analytics: looking into the past and determining why a certain thing happened; typically involves working on a dashboard.
- Descriptive Analytics: involves breaking down big data into smaller chunks of usable information so companies can understand what happened with a specific operation, process or set of transactions.
Data Analytics Applications by Industry
Hospitals and healthcare providers are faces with the competing challenges of containing costs while simultaneously improving patient care. Employing data and analytics can be used to track and optimize patient flow, treatment, and hospital equipment use and, ultimately, improve both efficiency and costs.
Data analytics is improving travel both for consumers and the companies providing travel service and accommodations by optimizing the buying experience through the mobile/ weblog and the social media data analysis, and personalized travel recommendations are now delivered by data analytics based on social media data.
Computer gaming companies use data analytics to gain an understanding of how users form relationships, interact, and use features within games.
Utility and energy providers turn to data analytics for smart-grid management, energy optimization, energy distribution, and building automation in utility companies. Utilities can integrate millions of data points in the network and lets the engineers use the analytics for network monitoring.