Analytics

Analytics and Analysis
– Data analysis focuses on examining past data through various processes like business understanding, data preparation, modeling, and evaluation.
– Data analytics is a subset of data analysis that focuses on understanding why an event happened and predicting future outcomes based on previous data.
– Data analytics is used to make larger organizational decisions.
– Data analytics is a multidisciplinary field that involves computer skills, mathematics, statistics, and the use of descriptive techniques and predictive models.
– Advanced analytics includes techniques like machine learning, neural networks, decision trees, and cluster analysis.

Applications
– Marketing optimization: Marketing organizations use analytics to determine the outcomes of campaigns and guide investment decisions. Techniques like demographic studies, customer segmentation, and conjoint analysis help understand consumer behavior. Marketing analytics involves qualitative and quantitative data to drive strategic decisions and improve performance. Web analytics provide insights on website interactions, referrers, search keywords, and visitor activities. Analysis techniques in marketing include marketing mix modeling, pricing and promotion analysis, sales force optimization, and customer analytics.
– People analytics: People analytics uses behavioral data to understand work patterns and improve management. It is also known as workforce analytics, HR analytics, talent analytics, and human capital analytics. HR analytics helps analyze and forecast human-related trends and make informed HR decisions. People analytics can be used to analyze employee turnover and identify strategic issues. There is a debate on whether people analytics should be a separate discipline or part of HR.
– Portfolio analytics: Portfolio analysis is a common application of business analytics. It involves analyzing a collection of accounts with varying value and risk. Banks and lending agencies use portfolio analytics to assess social status, creditworthiness, and risk. Portfolio analytics helps in making informed decisions about loan approvals and managing risk. It is a valuable tool for financial institutions to optimize their portfolios and maximize returns.

Tools and Technologies
– Analytics requires the use of algorithms and software to process and analyze data.
– The field of analytics leverages the latest methods in computer science, statistics, and mathematics.
– Big data technologies are used to handle large volumes of data in analytics.
– Popular analytics tools include Google Analytics for web analytics and various software platforms for advanced analytics.
– The analytics software market has experienced significant growth, with billions of dollars spent globally.

Benefits and Impact
– Analytics helps organizations discover meaningful patterns in data and make informed decisions.
– It can improve business performance by describing, predicting, and improving various aspects of operations.
– Analytics is applied in various fields such as marketing, management, finance, information security, and software services.
– The global spending on big data and business analytics solutions is estimated to reach billions of dollars.
– Analytics plays a crucial role in optimizing marketing campaigns, improving customer targeting, and driving revenue outcomes.

Subtopics
– Risk analytics: Predictive models in the banking industry are used to ensure certainty in risk scores for individual customers. Credit scores predict delinquency behavior and evaluate the creditworthiness of applicants. Risk analyses are conducted in the scientific world and insurance industry. Risk analytics is extensively used in financial institutions, such as online payment gateway companies, to detect fraud. Transaction history of customers is used to analyze if a transaction was genuine or fraudulent.
– Digital analytics: Digital analytics involves activities that define, create, collect, verify, and transform digital data. SEO (search engine optimization) tracks keyword searches for marketing purposes. Banner ads and clicks are part of digital analytics. Many brands and marketing firms rely on digital analytics for their digital marketing assignments. MROI (Marketing Return on Investment) is an important KPI in digital analytics.
– Security analytics: Security analytics uses IT to gather and analyze security events that pose the greatest risk. Products in this area include security information and event management and user behavior analytics.
– Software analytics: Software analytics involves collecting information about the usage and production of software. It helps in understanding how software is used and produced.
– Challenges: Analyzing massive, complex datasets (big data) is a challenge in commercial analytics software. Unstructured data analysis is gaining attention as the format varies widely and requires data transformation. Unstructured data, like emails and word processor documents, is becoming a relevant source of business intelligence. Educators face challenges in using analytics to understand student performance and predict graduation likelihood. Over-the-counter data formats are used to improve educators’ understanding and use of analytics.
– Risks: Risks in analytics include discrimination based on gender, skin color, ethnic origin, or political opinions. Cognitive analytics combines AI and data analytics. Global spending on big data and analytics solutions is projected to reach $215.7 billion in 2021. Big data and business analytics revenue is expected to grow in 2022. The data and analytics software market share is an important metric in the industry.
– Subtopic 1: People Analytics: Used in human resources to analyze employee turnover. Provides insights for theory and practice. Offered as a course by the University of Pennsylvania on Coursera. Discussed in the International Journal of Manpower. Covered in an article by HR Examiner.
– Predictive Analytics: Applied in the insurance industry for risk assessment. Includes types, tools, and future implications. Explored in an article on Maryville Online. Examined in the Information Technology and Management journal. Discussed in a Forbes Advisor article on mobile credit card alerts.
– Security Analytics: Used for breach detection and prevention. Provides hope for improving security measures. Discussed in an article on Enterprise Innovation. Explored in the book ‘Information Security Analytics’. Covered in an overview on ScienceDirect Topics.
– Big Data Analytics: Described as the next frontier for innovation and competition. Involves analyzing large volumes of data. Explored in a report by The Economist. Discussed in the Journal of Systems and Software. Applied in crop mapping and machine learning.
– Data Analytics in Education: Used for data-informed decision making in schools. Can either propagate or fight data analysis errors. Explored in a systematic review on discrimination and big data. Discussed in a presentation on technology in education leadership. Covered in a report by the U.S. Department of Education Source:  https://en.wikipedia.org/wiki/Data_analytics