Data Analytics - Revision history

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User: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns but techniques may vary

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Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns but techniques may vary

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← Older revision		Revision as of 23:54, 22 March 2024
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	== See Also ==		== See Also ==
−	*[[Predictive Analytics]]	+	Data analytics encompasses a broad spectrum of techniques and processes aimed at enhancing business performance by converting raw data into meaningful insights. It involves the systematic computational analysis of data or statistics, enabling organizations to make data-driven decisions.
		+
		+	*Descriptive Analytics: Discussing the use of data to understand past and present trends and performances. This type of analytics helps businesses understand what has happened and what is happening in their operations.
		+	*[[Predictive Analytics]]: Focusing on forecasting future probabilities and trends by using statistical models and machine learning techniques on historical and current data.
		+	*Prescriptive Analytics: Covering analytics that uses optimization and simulation algorithms to advise on possible outcomes and answer "What should we do?" It aims to prescribe actions based on predictive analytics findings.
		+	*[[Data Mining]]: Exploring the process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the Internet, and other data repositories.
		+	*[[Big Data]]: Discussing the technologies and challenges associated with analyzing extremely large datasets. Big data analytics leverages advanced computing techniques to process and analyze big data.
		+	*[[Machine Learning]]: Covering algorithms and statistical models that computer systems use to perform tasks without explicit instructions, relying on patterns and inference instead. Machine learning is a key tool in advanced analytics techniques.
		+	*[[Data Visualization]]: Discussing the graphical representation of information and data. Using visual elements like charts, graphs, and maps, data visualization tools help to understand trends, outliers, and patterns in data.
		+	*[[Business Intelligence\|Business Intelligence (BI)]]: Focusing on technologies, applications, strategies, and practices used to collect, integrate, analyze, and present business information. BI and data analytics often work hand-in-hand to support decision-making processes.
		+	*[[Statistical Analysis]]: Exploring the collection, analysis, interpretation, presentation, and organization of data using statistical methods and techniques. This includes hypothesis testing, regression analysis, and variance analysis.
		+	*[[Data Governance]]: Highlighting the management of the availability, usability, integrity, and security of the data used in an organization, including policies, procedures, and technologies used to manage and protect data.
		+	*Cloud Analytics: Discussing the use of cloud services for performing data analytics. Cloud analytics platforms can offer scalable resources for processing and analyzing large datasets.
		+	*Ethics in Data Analytics: Covering ethical considerations in data analytics, including data privacy, confidentiality, and the responsible use of data analytics to make decisions that affect individuals and communities.

← Older revision		Revision as of 12:05, 24 February 2024
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	== See Also ==		== See Also ==
−	*[[Data]]
	*[[Predictive Analytics]]		*[[Predictive Analytics]]

@@ Line 27: / Line 27: @@
 == Data Analytics vs. Business Analytics<ref>[https://www.capella.edu/blogs/cublog/what-is-data-analytics/ what are the differences between data analytics and business analytics?]</ref> ==
-Business analytics and data analytics are often used interchangeably throughout the industry. There is great debate and much diversity of opinion about the distinctions because there is great similarity between the two functions. Both business analytics and data analytics involve the collection and analysis of data, and both conduct their analysis in order to improve an organization’s effectiveness and decision-making. However, the business analysis focuses much more on the functions, processes, operations, and overall architecture of an enterprise. It is the practice of enabling change by identifying business needs and determining solutions to those business problems. Business analysts work across all levels of the organization and can be found focusing on things such as strategic planning, translating project requirements, and developing policy all in an effort to support an organization’s ongoing technology and process maintenance. Data analysis focuses on the process of collecting, cleaning, analyzing, reporting, and presenting data. Analysts in this area break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. Data analysts focus on the tools and statistical methodology and can often be found working with management, translating data, interpreting results, and testing hypotheses.
+Business analytics and data analytics are often used interchangeably throughout the industry. There is great debate and much diversity of opinion about the distinctions because there is great similarity between the two functions. Both business analytics and data analytics involve the collection and analysis of data, and both conduct their analysis in order to improve an organization’s effectiveness and decision-making. However, business analysis focuses much more on the functions, processes, operations, and overall architecture of an enterprise. It is the practice of enabling change by identifying business needs and determining solutions to those business problems. Business analysts work across all levels of the organization and can be found focusing on things such as strategic planning, translating project requirements, and developing policy all in an effort to support an organization’s ongoing technology and process maintenance. Data analysis focuses on the process of collecting, cleaning, analyzing, reporting, and presenting data. Analysts in this area break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. Data analysts focus on the tools and statistical methodology and can often be found working with management, translating data, interpreting results, and testing hypotheses.
 == See Also ==
-[[Data Mining]]<br />
+*[[Data]]
-[[Data Management]]<br />
+*[[Predictive Analytics]]
@@ Line 48: / Line 42: @@
 *[http://onlinelibrary.wiley.com/book/10.1002/9781119173601 Internet of Things and Data Analytics Handbook]
 *[https://www.forbes.com/sites/forbesinsights/2017/06/12/data-analytics-is-no-longer-a-nice-option-its-the-core-of-the-enterprise/#448958d477ec Data Analytics Is No Longer A Nice Option -- It's The Core Of The Enterprise]
-*http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx The Age of Analytics: Competing in a Data-driven World ]
+*[http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx The Age of Analytics: Competing in a Data-driven World ]
 *[http://www.datascienceassn.org/sites/default/files/Data%20Analytics%20Methods%20and%20Techniques%202014.pdf Data Analytics Methods and Techniques]
 __NOTOC__

@@ Line 1: / Line 1: @@
-===What is Data Analytics?===
+== What is Data Analytics? ==
-'''Data Analytics''' refers to qualitative and quantitative techniques and processes used to enhance productivity and [[business]] gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is sometimes also referred to as data analysis.<ref>[https://www.techopedia.com/definition/26418/data-analytics Definition of Data Analytics]</ref>
+'''Data Analytics''' refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is sometimes also referred to as data analysis.<ref>[https://www.techopedia.com/definition/26418/data-analytics Definition of Data Analytics]</ref>
-Data analytics is the pursuit of extracting meaning from raw data using specialized [[computer]] systems. These systems transform, organize, and [[model]] the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have strong expertise in statistics, are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.<ref>[https://www.informatica.com/services-and-training/glossary-of-terms/data-analytics-definition.html#fbid=5rB_26rQpX6 What is Data Analytics?]</ref>
+Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have strong expertise in statistics, are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.<ref>[https://www.informatica.com/services-and-training/glossary-of-terms/data-analytics-definition.html#fbid=5rB_26rQpX6 What is Data Analytics?]</ref>
-===Types of Data Analytics<ref>[https://www.kdnuggets.com/2017/07/4-types-data-analytics.html What are the Different Types of Data Analytics]</ref>===
+== Types of Data Analytics<ref>[https://www.kdnuggets.com/2017/07/4-types-data-analytics.html What are the Different Types of Data Analytics]</ref> ==
 There are four types of Data Analytics as illustrated in the figure below<br />
@@ Line 12: / Line 12: @@
-===Big Data Analytics - How it Works and Key Technologies<ref>[https://www.sas.com/en_us/insights/analytics/big-data-analytics.html#dmtechnical Big Data Analytics - How it Works and Key Technologies]</ref>===
+== Big Data Analytics - How it Works and Key Technologies<ref>[https://www.sas.com/en_us/insights/analytics/big-data-analytics.html#dmtechnical Big Data Analytics - How it Works and Key Technologies]</ref> ==
-There’s no single technology that encompasses big data analytics. Of course, there is advanced analytics that can be applied to big data, but in reality, several types of technology work together to help you get the most [[value]] from your information. Here are the biggest players:
+There’s no single technology that encompasses big data analytics. Of course, there is advanced analytics that can be applied to big data, but in reality, several types of technology work together to help you get the most value from your information. Here are the biggest players:
-*[[Data Management]]. Data needs to be high [[quality]] and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an [[organization]], it's important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.
+*Data Management. Data needs to be high quality and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an organization, it's important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.
-*Data mining. Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining [[software]], you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.
+*Data mining. Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.
-*[[Hadoop]]. This open-source software [[framework]] can store large amounts of data and run applications on clusters of commodity [[hardware]]. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open-source framework is free and uses commodity hardware to store large quantities of data.
+*Hadoop. This open-source software framework can store large amounts of data and run applications on clusters of commodity hardware. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open-source framework is free and uses commodity hardware to store large quantities of data.
 *In-memory analytics. By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it's not only an easy way for organizations to stay agile and make better business decisions, but it also enables them to run iterative and interactive analytics scenarios.
-*Predictive analytics. Predictive analytics technology uses data, statistical algorithms, and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing the best assessment of what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, [[risk]], operations, and [[marketing]].
+*Predictive analytics. Predictive analytics technology uses data, statistical algorithms, and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing the best assessment of what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations, and marketing.
 *Text mining. With text mining technology, you can analyze text data from the web, comment fields, books, and other text-based sources to uncover insights you hadn't noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence, and more – to help you analyze large amounts of information and discover new topics and term relationships.
-===Data Analytics vs. [[Data Analysis]]<ref>[https://www.getsmarter.com/career-advice/industry-advice/difference-data-analytics-data-analysis Data Analytics vs. Data Analysis]</ref>===
+== Data Analytics vs. Data Analysis<ref>[https://www.getsmarter.com/career-advice/industry-advice/difference-data-analytics-data-analysis Data Analytics vs. Data Analysis]</ref> ==
-The difference between data analysis and data analytics is that data analytics is a broader term in which data analysis forms a subcomponent. Data analysis refers to the [[process]] of compiling and analyzing data to support decision-making, whereas data analytics also includes the tools and techniques used to do so.
+The difference between data analysis and data analytics is that data analytics is a broader term in which data analysis forms a subcomponent. Data analysis refers to the process of compiling and analyzing data to support decision-making, whereas data analytics also includes the tools and techniques used to do so.
-'''Data Analytics vs. [[Business Analytics]]'''<ref>[https://www.capella.edu/blogs/cublog/what-is-data-analytics/ what are the differences between data analytics and business analytics?]</ref><br />
+== Data Analytics vs. Business Analytics<ref>[https://www.capella.edu/blogs/cublog/what-is-data-analytics/ what are the differences between data analytics and business analytics?]</ref> ==
-Business analytics and data analytics are often used interchangeably throughout the [[industry]]. There is great debate and much diversity of opinion about the distinctions because there is great similarity between the two functions. Both business analytics and data analytics involve the collection and analysis of data, and both conduct their analysis in order to improve an organization’s effectiveness and decision-making. However, the business analysis focuses much more on the functions, processes, operations, and overall [[architecture]] of an enterprise. It is the practice of enabling change by identifying business needs and determining solutions to those business problems. Business analysts work across all levels of the organization and can be found focusing on things such as strategic planning, translating project requirements, and developing [[policy]] all in an effort to support an organization’s ongoing technology and process maintenance. Data analysis focuses on the process of collecting, cleaning, analyzing, reporting, and presenting data. Analysts in this area break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. Data analysts focus on the tools and statistical [[methodology]] and can often be found working with management, translating data, interpreting results, and testing hypotheses.
+Business analytics and data analytics are often used interchangeably throughout the industry. There is great debate and much diversity of opinion about the distinctions because there is great similarity between the two functions. Both business analytics and data analytics involve the collection and analysis of data, and both conduct their analysis in order to improve an organization’s effectiveness and decision-making. However, the business analysis focuses much more on the functions, processes, operations, and overall architecture of an enterprise. It is the practice of enabling change by identifying business needs and determining solutions to those business problems. Business analysts work across all levels of the organization and can be found focusing on things such as strategic planning, translating project requirements, and developing policy all in an effort to support an organization’s ongoing technology and process maintenance. Data analysis focuses on the process of collecting, cleaning, analyzing, reporting, and presenting data. Analysts in this area break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. Data analysts focus on the tools and statistical methodology and can often be found working with management, translating data, interpreting results, and testing hypotheses.
-===See Also===
+== See Also ==
 [[Data Mining]]<br />
 [[Data Management]]<br />
@@ Line 41: / Line 41: @@
-===References===
+== References ==
 <references/>
-===Further Reading===
+== Further Reading ==
 *[http://onlinelibrary.wiley.com/book/10.1002/9781119173601 Internet of Things and Data Analytics Handbook]
 *[https://www.forbes.com/sites/forbesinsights/2017/06/12/data-analytics-is-no-longer-a-nice-option-its-the-core-of-the-enterprise/#448958d477ec Data Analytics Is No Longer A Nice Option -- It's The Core Of The Enterprise]
-*[http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx The Age of Analytics: Competing in a Data-driven World ]
+*http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx The Age of Analytics: Competing in a Data-driven World ]
 *[http://www.datascienceassn.org/sites/default/files/Data%20Analytics%20Methods%20and%20Techniques%202014.pdf Data Analytics Methods and Techniques]
 __NOTOC__

← Older revision		Revision as of 22:47, 29 November 2022
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	'''Data Analytics''' refers to qualitative and quantitative techniques and processes used to enhance productivity and [[business]] gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is sometimes also referred to as data analysis.<ref>[https://www.techopedia.com/definition/26418/data-analytics Definition of Data Analytics]</ref>		'''Data Analytics''' refers to qualitative and quantitative techniques and processes used to enhance productivity and [[business]] gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is sometimes also referred to as data analysis.<ref>[https://www.techopedia.com/definition/26418/data-analytics Definition of Data Analytics]</ref>

−	Data analytics is the pursuit of extracting meaning from raw data using specialized [[computer]] systems. These systems transform, organize, and [[model]] the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have strong expertise in [[statistics]], are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.<ref>[https://www.informatica.com/services-and-training/glossary-of-terms/data-analytics-definition.html#fbid=5rB_26rQpX6 What is Data Analytics?]</ref>	+	Data analytics is the pursuit of extracting meaning from raw data using specialized [[computer]] systems. These systems transform, organize, and [[model]] the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have strong expertise in statistics, are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.<ref>[https://www.informatica.com/services-and-training/glossary-of-terms/data-analytics-definition.html#fbid=5rB_26rQpX6 What is Data Analytics?]</ref>

@@ Line 1: / Line 1: @@
-[[Data]] [[analytics]] refers to qualitative and quantitative techniques and processes used to enhance [[productivity]] and [[business]] gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is sometimes also referred to as [[Data_Analysis|data analysis]].<ref>Definition of Data Analytics [https://www.techopedia.com/definition/26418/data-analytics Techopedia]</ref>
+===What is Data Analytics===
-Data analytics is the pursuit of extracting meaning from raw data using specialized [[computer]] systems. These systems transform, organize, and [[model]] the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have a strong expertise in [[statistics]], are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.<ref>What is Data Analytics [https://www.informatica.com/services-and-training/glossary-of-terms/data-analytics-definition.html#fbid=5rB_26rQpX6 Informatica]</ref>
+Data analytics is the pursuit of extracting meaning from raw data using specialized [[computer]] systems. These systems transform, organize, and [[model]] the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have strong expertise in [[statistics]], are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.<ref>[https://www.informatica.com/services-and-training/glossary-of-terms/data-analytics-definition.html#fbid=5rB_26rQpX6 What is Data Analytics?]</ref>
-'''Types of Data Analytics'''<ref>What are the Different Types of Data Analytics [https://www.kdnuggets.com/2017/07/4-types-data-analytics.html Thomas Maydon]</ref><br />
+===Types of Data Analytics<ref>[https://www.kdnuggets.com/2017/07/4-types-data-analytics.html What are the Different Types of Data Analytics]</ref>===
 There are four types of Data Analytics as illustrated in the figure below<br />
 [[File:Data Analytics.png|450px|Types of Data Analytics]]<br />
-source: [https://www.kdnuggets.com/2017/07/4-types-data-analytics.html Principa]
+source: Principa
-'''[[Big Data]] Analytics - How it Works and Key Technologies'''<ref>Big Data Analytics - How it Works and Key Technologies [https://www.sas.com/en_us/insights/analytics/big-data-analytics.html#dmtechnical SAS]</ref><br />
+===Big Data Analytics - How it Works and Key Technologies<ref>[https://www.sas.com/en_us/insights/analytics/big-data-analytics.html#dmtechnical Big Data Analytics - How it Works and Key Technologies]</ref>===
-There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most [[value]] from your information. Here are the biggest players:
+There’s no single technology that encompasses big data analytics. Of course, there is advanced analytics that can be applied to big data, but in reality, several types of technology work together to help you get the most [[value]] from your information. Here are the biggest players:
-*Data [[management]]. Data needs to be high [[quality]] and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an [[organization]], it's important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.
+*[[Data Management]]. Data needs to be high [[quality]] and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an [[organization]], it's important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.
 *Data mining. Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining [[software]], you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.
-*[[Hadoop]]. This open source software [[framework]] can store large amounts of data and run applications on [[clusters]] of commodity [[hardware]]. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open source framework is free and uses commodity hardware to store large quantities of data.
+*[[Hadoop]]. This open-source software [[framework]] can store large amounts of data and run applications on clusters of commodity [[hardware]]. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open-source framework is free and uses commodity hardware to store large quantities of data.
-*In-memory analytics. By analyzing data from [[system]] memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it's not only an easy way for organizations to stay agile and make better business decisions, it also enables them to run iterative and interactive analytics scenarios.
+*In-memory analytics. By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it's not only an easy way for organizations to stay agile and make better business decisions, but it also enables them to run iterative and interactive analytics scenarios.
-*Predictive analytics. Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing a best assessment on what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, [[risk]], operations and [[marketing]].
+*Predictive analytics. Predictive analytics technology uses data, statistical algorithms, and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing the best assessment of what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, [[risk]], operations, and [[marketing]].
-*Text mining. With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn't noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.
+*Text mining. With text mining technology, you can analyze text data from the web, comment fields, books, and other text-based sources to uncover insights you hadn't noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence, and more – to help you analyze large amounts of information and discover new topics and term relationships.
-'''Data Analytics vs. [[Data Analysis]]'''<ref>Data Analytics vs. Data Analysis [https://www.getsmarter.com/career-advice/industry-advice/difference-data-analytics-data-analysis GetSmarter]</ref><br />
+===Data Analytics vs. [[Data Analysis]]<ref>[https://www.getsmarter.com/career-advice/industry-advice/difference-data-analytics-data-analysis Data Analytics vs. Data Analysis]</ref>===
-The difference between data analysis and data analytics is that data analytics is a broader term of which data analysis forms a subcomponent. Data analysis refers to the [[process]] of compiling and analyzing data to support decision making, whereas data analytics also includes the tools and techniques use to do so.
+The difference between data analysis and data analytics is that data analytics is a broader term in which data analysis forms a subcomponent. Data analysis refers to the [[process]] of compiling and analyzing data to support decision-making, whereas data analytics also includes the tools and techniques used to do so.
-'''Data Analytics vs. [[Business Analytics]]'''<ref>What are the differences between data analytics and business analytics? [https://www.capella.edu/blogs/cublog/what-is-data-analytics/ capella.edu]</ref><br />
+'''Data Analytics vs. [[Business Analytics]]'''<ref>[https://www.capella.edu/blogs/cublog/what-is-data-analytics/ what are the differences between data analytics and business analytics?]</ref><br />
-Business analytics and data analytics are often used interchangeably throughout the [[industry]]. There is great debate and much diversity of opinion about the distinctions because there is great similarity between the two functions. Both business analytics and data analytics involve the collection and analysis of data, and both conduct their analysis in order to improve an organization’s effectiveness and decision-making. However, business analysis focuses much more on the functions, processes, operations, and overall [[architecture]] of an enterprise. It is the practice of enabling change by identifying business needs and determining solutions to those business problems. Business analysts work across all levels of the organization and can be found focusing on things such as strategic planning, translating project requirements, and developing [[policy]] all in an effort to support an organization’s ongoing technology and process maintenance. Data analysis focuses on the process of collecting, cleaning, analyzing, reporting, and presenting data. Analysts in this area break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. Data analysts focus on the tools and statistical [[methodology]] and can often be found working with management, translating data, interpreting results, and testing hypotheses.
+Business analytics and data analytics are often used interchangeably throughout the [[industry]]. There is great debate and much diversity of opinion about the distinctions because there is great similarity between the two functions. Both business analytics and data analytics involve the collection and analysis of data, and both conduct their analysis in order to improve an organization’s effectiveness and decision-making. However, the business analysis focuses much more on the functions, processes, operations, and overall [[architecture]] of an enterprise. It is the practice of enabling change by identifying business needs and determining solutions to those business problems. Business analysts work across all levels of the organization and can be found focusing on things such as strategic planning, translating project requirements, and developing [[policy]] all in an effort to support an organization’s ongoing technology and process maintenance. Data analysis focuses on the process of collecting, cleaning, analyzing, reporting, and presenting data. Analysts in this area break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. Data analysts focus on the tools and statistical [[methodology]] and can often be found working with management, translating data, interpreting results, and testing hypotheses.
 ===See Also===
-[[Data_Mining|Data Mining]]<br />
+[[Data Mining]]<br />
-[[Data_Management|Data Management]]<br />
+[[Data Management]]<br />
-[[Data_Warehouse|Data Warehouse]]<br />
+[[Data Warehouse]]<br />
-[[Customer_Data_Management_(CDM)|Customer Data Management (CDM)]]<br />
+[[Customer Data Management (CDM)]]<br />
-[[Big_Data|Big Data]]<br />
+[[Big Data]]<br />
-[[Business_Intelligence|Business Intelligence]]<br />
+[[Business Intelligence]]<br />
-[[Data_Analysis|Data Analysis]]<br />
+[[Data Cleansing]]<br />
-[[Data_Cleansing|Data Cleansing]]<br />
+[[Predictive Analytics]]
@@ Line 46: / Line 46: @@
 ===Further Reading===
-*[[Internet]] of Things and Data Analytics Handbook [http://onlinelibrary.wiley.com/book/10.1002/9781119173601 Hwaiyu Geng]
+*[http://onlinelibrary.wiley.com/book/10.1002/9781119173601 Internet of Things and Data Analytics Handbook]
-*Data Analytics Is No Longer A Nice Option -- It's The Core Of The Enterprise [https://www.forbes.com/sites/forbesinsights/2017/06/12/data-analytics-is-no-longer-a-nice-option-its-the-core-of-the-enterprise/#448958d477ec Forbes]
+*[https://www.forbes.com/sites/forbesinsights/2017/06/12/data-analytics-is-no-longer-a-nice-option-its-the-core-of-the-enterprise/#448958d477ec Data Analytics Is No Longer A Nice Option -- It's The Core Of The Enterprise]
-*The Age of Analytics: Competing in a Data-driven World [http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx McKinsey]
+*[http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx The Age of Analytics: Competing in a Data-driven World ]
-*Data Analytics Methods and Techniques [http://www.datascienceassn.org/sites/default/files/Data%20Analytics%20Methods%20and%20Techniques%202014.pdf Martin Hahmann, Gunnar Schröder, Phillip Grosse]
+*[http://www.datascienceassn.org/sites/default/files/Data%20Analytics%20Methods%20and%20Techniques%202014.pdf Data Analytics Methods and Techniques]

← Older revision		Revision as of 18:11, 19 December 2018
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−	'''~~Content Coming Soon~~'''	+	Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. Data analytics is sometimes also referred to as [[Data_Analysis\|data analysis]].<ref>Definition of Data Analytics [https://www.techopedia.com/definition/26418/data-analytics Techopedia]</ref>
		+
		+	Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns. While data analytics can be simple, today the term is most often used to describe the analysis of large volumes of data and/or high-velocity data, which presents unique computational and data-handling challenges. Skilled data analytics professionals, who generally have a strong expertise in statistics, are called data scientists. The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. For that, they employed data warehouses, but data warehouses generally cannot handle the scale of big data cost-effectively. While data warehouses are certainly a relevant form of data analytics, the term data analytics is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.<ref>What is Data Analytics [https://www.informatica.com/services-and-training/glossary-of-terms/data-analytics-definition.html#fbid=5rB_26rQpX6 Informatica]</ref>
		+
		+
		+	'''Types of Data Analytics'''<ref>What are the Different Types of Data Analytics [https://www.kdnuggets.com/2017/07/4-types-data-analytics.html Thomas Maydon]</ref><br />
		+	There are four types of Data Analytics as illustrated in the figure below<br />
		+
		+	[[File:Data Analytics.png\|450px\|Types of Data Analytics]]<br />
		+	source: [https://www.kdnuggets.com/2017/07/4-types-data-analytics.html Principa]
		+
		+
		+	'''Big Data Analytics - How it Works and Key Technologies'''<ref>Big Data Analytics - How it Works and Key Technologies [https://www.sas.com/en_us/insights/analytics/big-data-analytics.html#dmtechnical SAS]</ref><br />
		+	There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your information. Here are the biggest players:
		+	*Data management. Data needs to be high quality and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an organization, it's important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.
		+	*Data mining. Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.
		+	*Hadoop. This open source software framework can store large amounts of data and run applications on clusters of commodity hardware. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open source framework is free and uses commodity hardware to store large quantities of data.
		+	*In-memory analytics. By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it's not only an easy way for organizations to stay agile and make better business decisions, it also enables them to run iterative and interactive analytics scenarios.
		+	*Predictive analytics. Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing a best assessment on what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing.
		+	*Text mining. With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn't noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.
		+
		+
		+	'''Data Analytics vs. Data Analysis'''<ref>Data Analytics vs. Data Analysis [https://www.getsmarter.com/career-advice/industry-advice/difference-data-analytics-data-analysis GetSmarter]</ref><br />
		+	The difference between data analysis and data analytics is that data analytics is a broader term of which data analysis forms a subcomponent. Data analysis refers to the process of compiling and analyzing data to support decision making, whereas data analytics also includes the tools and techniques use to do so.
		+
		+
		+	'''Data Analytics vs. Business Analytics'''<ref>What are the differences between data analytics and business analytics? [https://www.capella.edu/blogs/cublog/what-is-data-analytics/ capella.edu]</ref><br />
		+	Business analytics and data analytics are often used interchangeably throughout the industry. There is great debate and much diversity of opinion about the distinctions because there is great similarity between the two functions. Both business analytics and data analytics involve the collection and analysis of data, and both conduct their analysis in order to improve an organization’s effectiveness and decision-making. However, business analysis focuses much more on the functions, processes, operations, and overall architecture of an enterprise. It is the practice of enabling change by identifying business needs and determining solutions to those business problems. Business analysts work across all levels of the organization and can be found focusing on things such as strategic planning, translating project requirements, and developing policy all in an effort to support an organization’s ongoing technology and process maintenance. Data analysis focuses on the process of collecting, cleaning, analyzing, reporting, and presenting data. Analysts in this area break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. Data analysts focus on the tools and statistical methodology and can often be found working with management, translating data, interpreting results, and testing hypotheses.
		+
		+
		+	===See Also===
		+	[[Data_Mining\|Data Mining]]<br />
		+	[[Data_Management\|Data Management]]<br />
		+	[[Data_Warehouse\|Data Warehouse]]<br />
		+	[[Customer_Data_Management_(CDM)\|Customer Data Management (CDM)]]<br />
		+	[[Big_Data\|Big Data]]<br />
		+	[[Business_Intelligence\|Business Intelligence]]<br />
		+	[[Data_Analysis\|Data Analysis]]<br />
		+	[[Data_Cleansing\|Data Cleansing]]<br />
		+	[[Predictive_Analytics\|Predictive Analytics]]
		+
		+
		+	===References===
		+	<references/>
		+
		+
		+	===Further Reading===
		+	*Internet of Things and Data Analytics Handbook [http://onlinelibrary.wiley.com/book/10.1002/9781119173601 Hwaiyu Geng]
		+	*Data Analytics Is No Longer A Nice Option -- It's The Core Of The Enterprise [https://www.forbes.com/sites/forbesinsights/2017/06/12/data-analytics-is-no-longer-a-nice-option-its-the-core-of-the-enterprise/#448958d477ec Forbes]
		+	*The Age of Analytics: Competing in a Data-driven World [http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx McKinsey]
		+	*Data Analytics Methods and Techniques [http://www.datascienceassn.org/sites/default/files/Data%20Analytics%20Methods%20and%20Techniques%202014.pdf Martin Hahmann, Gunnar Schröder, Phillip Grosse]