Data Analytics and Applications
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- Disciplines in Data Analytics
Dan Ariely, a well-known Duke economics professor, once said about big data: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” This concept applies to a great deal of data terminology. While many people toss around terms like “data science,” “data analysis,” “big data,” and “data mining,” even the experts have trouble defining them.
Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.
Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more.
- Big Data Analytics
The Harvard Business Review once called the role of the Data Scientist "The Sexiest Job of the 21st Century." That’s because it takes someone with the skills of a scientist to make something useful out of big data.
There are two basic types of big data analytics - synchronous and asynchronous - but both have big data storage appetites and specialized needs. Synchronous analytics and asynchronous analytics are distinguished by the way they process data. But they both have big data storage appetites and specialized needs.
Big data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional BI and analytics programs. This encompasses a mix of semi-structured and unstructured data - for example, Internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT).
The value of the data is tied to comparing, associating or referencing it with other data sets. Analysis of big data usually deals with a very large quantity of small data objects with a low tolerance for storage latency.
- Big Data and Big Data Analytics
Big data analytics is probably the fastest evolving issue in the IT world now. New tools and algorithms are being created and adopted swiftly. Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.
There are two basic types of big data analytics - synchronous and asynchronous - but both have big data storage appetites and specialized needs. Synchronous analytics and asynchronous analytics are distinguished by the way they process data. But they both have big data storage appetites and specialized needs.
Big data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional BI and analytics programs. This encompasses a mix of semi-structured and unstructured data - for example, Internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT).
The value of the data is tied to comparing, associating or referencing it with other data sets. Analysis of big data usually deals with a very large quantity of small data objects with a low tolerance for storage latency.
- Data Analysts and Data Scientists
While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.
The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, etc.
Data analysts have a range of fields and titles, including (but not limited to) database analyst, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.
[More to come ...]
