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Data Analytics helps to gain\u00a0actionable insights for\u00a0smart decisions and\u00a0strategic business outcomes. <\/span><\/p>\n While there are hundreds of terms in analytics, this blog aims to decode just a few of them that many students or wannabe analytics professionals find puzzling.<\/span><\/p>\n #1 Data vs. Information<\/span><\/b><\/p>\n Data is unprocessed material or facts, in the form of statistics, figures, images or documents that is more like \u2018raw material\u2019. By itself or in the abstract, Data does not have meaning or significance. <\/span><\/p>\n Information is Data that is processed for gaining insight and to bring value to the business. Surprised? <\/span><\/p>\n \u00a0# 2 Analysis vs. Analytics<\/b><\/span><\/p>\n Analysis breaks down a problem or topic for detailed examination to establish results and relationships.<\/span><\/p>\n Analytics makes use of mathematics, statistics, descriptive techniques, predictive models and machine learning to gain insights into the data. Analytics is more of a multi-disciplinary science that uses logic to discover meaningful patterns and trends for actionable decisions. It includes supporting technology and tools.<\/span><\/p>\n # 3 Analytics firm vs. Analytics industry<\/span><\/b><\/p>\n Analytics firm is a single unit or company that offers ready solutions, customised products and services in the field of analytics. It may cater to multiple analytics solutions or niche analytics like Marketing or Mobile Analytics.<\/span><\/p>\n Analytics industry refers to the aggregate of business enterprise in the field of analytics. It encompasses the endeavours, activities, standardizations, tools, software and research in the filed of Analytics, as well as the Analytic professionals working within this specialized domain.<\/span><\/p>\n # 4 Descriptive vs. Prescriptive Analytics<\/span><\/b><\/p>\n Descriptive Analytics is a type of post-mortem analysis that looks at historical data to develop insights into successes or failures. <\/span><\/p>\n Prescriptive Analytics synergises\u00a0data, mathematical sciences,\u00a0data mining, business rules and\u00a0machine learning\u00a0to make predictions; suggesting actions based on the predictions, as well as implications of each decision option. <\/span><\/p>\n While Descriptive Analytics analyses past events to suggest how to approach the future; Prescriptive Analytics anticipates what will happen, when it will happen, and also why it will happen.\u00a0<\/span><\/p>\n #5 BPO vs. KPO<\/span><\/b><\/p>\n Although both are part of the global outsourcing sector, BPO (Business Process Outsourcing) is a larger domain of outsourcing business functions to third parties \/ firms for cost benefits. BPO is back-end office operations when related to finance or HR, or front office services when dealing with client interaction and customer support. <\/span><\/p>\n KPO (Knowledge Process Outsourcing) is a part of the BPO industry which handles outsourced core functions of a parent company, with the purpose of adding value. The underlying objective is to offer solutions that are not available in-house, not necessarily cost benefit. As the term suggests, this involves operations that are more specialised and knowledge based, as in legal or fraud analytics.<\/span><\/p>\n As an industry requiring high-skilled professionals, KPO is a fast developing industry in India with a tremendous career scope. <\/span><\/p>\n #6 Actuarial Science vs. Risk Analytics Actuarial Science is a discipline which applies mathematics and statistics to assess risks in finance, insurance, credit and other sectors.\u00a0 Actuarial Science in insurance traditionally involves analysing mortality and producing life tables for interest calculation. <\/span><\/p>\n Risk analytics employs sophisticated statistical techniques for risk assessment, modeling or risk-based pricing for valuable business insights.<\/span><\/p>\n The foundation for both is mathematics. A professional in actuarial science is called an \u2018actuary\u2019, for which he may need to get through qualifying examinations. While an actuary usually works in the insurance sector, a risk analyst works in banks or finance, although sometimes the lines may be blurred.<\/span><\/p>\n #7 Open Source vs. Proprietary software<\/span><\/b><\/p>\n Open Source Software (OSS) is software whose code is made freely available on the internet. In other words, it is free to download and use, without any license fees. It can also be modified to improve capabilities. <\/span><\/p>\n Whereas Proprietary Software is commercial with the source codes closely guarded. So it is available only at cost, often involving licensing fees too. <\/span><\/p>\n While Open Source Software has a vibrant developer community that keeps improvising on the product, Proprietary Software has in-house developing team, with updated releases from time to time.<\/span><\/p>\n #8\u00a0 Data Science vs. Data Analytics<\/span><\/b><\/p>\n Data Science is the science of extracting knowledge from huge amount of data, applying mathematical and statistical techniques, machine learning programming, data warehousing, analytic functions and specialised tools. A Data scientist is a PhD (mathematics, Statistics, Machine learning, Computer Science) or B.E.\/B.Tech, with extensive knowledge in data engineering to provides deep insights for business decisions.<\/span><\/p>\n Data Analytics is the method applied to discover trends and patterns for meaningful insights, based on which decisions may be taken. While this too involves simultaneous application of statistics, mathematics and computer science, knowledge of business process and niche areas like marketing and web are preferred add-on skills.<\/span><\/p>\n Although often used interchangeably, data science calls for expertise in working with voluminous data, with focus on programming, data mining and data warehousing know-how; while data analytics requires business knowledge and skills with analytics tools.<\/span><\/p>\n #9\u00a0 Data Mining vs. Data Warehousing<\/span><\/b><\/p>\n Both are tools used for Business Intelligence, with differences in techniques applied.<\/span><\/p>\n While Data Mining \u2018mines\u2019 or extracts meaningful insights from data; Data Warehousing compiles data from multiple sources or systems into a single centralized repository, system or \u2018warehouse\u2019.<\/span><\/p>\n Data Mining applies tools of statistical analysis, whereas Data Warehousing involves designing the DBMS (Database Management System) method for necessary data mining and analytics.<\/span><\/p>\n #10\u00a0 Business Analytics vs Business Intelligence (BI) Business analytics refers to the analytic techniques and tools applied for deriving insights to make predictions or business decisions. <\/span><\/p>\n Business intelligence is more of an umbrella term focusing on tools, infrastructure, applications, online or real-time analytical processing for business insights. The BI environment handles voluminous data, with greater corporate reporting capability. Very often it refers to a customised proprietary solution develop by a big data vendor. <\/span><\/p>\n They are distinct but connected tools, that together drive bottomless insight into business outcomes. For a student or professional wanting to chart a career in analytics, here is a list of most commonly used terms decoded.<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[236,237,232,243,233,235,241,244,230,240,239,231,245,234,238,242,229],"class_list":["post-4851","post","type-post","status-publish","format-standard","hentry","category-data-analytics","tag-actuarial-science-vs-risk-analytics","tag-actuary","tag-analysis-vs-analytics","tag-analytic-terms","tag-analytics-firm-vs-analytics-industry","tag-bpo-vs-kpo","tag-business-analytics-vs-business-intelligence","tag-career-in-analytics","tag-data-analytics","tag-data-mining-vs-data-warehousing","tag-data-science-vs-data-analytics","tag-data-vs-information","tag-decoded","tag-descriptive-vs-prescriptive-analytics","tag-open-source-vs-proprietary-software","tag-top-10-pairs","tag-unplugged"],"yoast_head":"\n
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