Data Munging - Revision history

User at 17:45, 15 March 2023

2023-03-15T17:45:02Z

User at 18:05, 12 January 2023

2023-01-12T18:05:40Z

User at 18:04, 12 January 2023

2023-01-12T18:04:53Z

User at 15:32, 26 October 2022

2022-10-26T15:32:06Z

User at 15:52, 17 October 2022

2022-10-17T15:52:18Z

User at 15:51, 17 October 2022

2022-10-17T15:51:11Z

User at 15:10, 17 October 2022

2022-10-17T15:10:32Z

User at 14:58, 17 October 2022

2022-10-17T14:58:56Z

User at 07:14, 16 October 2022

2022-10-16T07:14:43Z

User at 23:37, 15 October 2022

2022-10-15T23:37:03Z

@@ Line 1: / Line 1: @@
-===What is Data Munging?===
+== What is Data Munging? ==
 '''Data Munging''' is the process of cleaning and transforming raw data into its desired format, usually to facilitate further analysis or visualization. Data munging can be done in Python or R, but it can also be performed with a spreadsheet program. The primary purpose of data munging is to take raw data and prepare it for use in an analysis. It helps to prepare data sets so that they can be leveraged by reporting tools or machine learning algorithms. Data Munging is a pre-processing step in the data-mining process. Data Munging is also referred to as Data Wrangling.
@@ Line 5: / Line 5: @@
-====Automating Data Munging<ref>[https://www.lido.app/post/what-is-data-munging Automating Data Munging]</ref>====
+=== Automating Data Munging<ref>[https://www.lido.app/post/what-is-data-munging Automating Data Munging]</ref> ===
 Data munging is not a pretty process; in fact, it is a fairly routine but time-intensive task. Data munging accounts for 50% to 80% of work by data analysts! There is therefore a strong push to automate data munging.
@@ Line 20: / Line 20: @@
 *Finding a tool that does not require heavy set-up/maintenance help from our engineers
 *Ability to connect to the sources we use and could potentially want
-These are essentially what make a good tool for data munging.
+These are essentially what makes a good tool for data munging.
-===Stages of Data Munging<ref>[https://www.astera.com/type/blog/what-is-data-munging/ Different Stages of Data Munging]</ref>===
+== Stages of Data Munging<ref>[https://www.astera.com/type/blog/what-is-data-munging/ Different Stages of Data Munging]</ref> ==
 *Data Discovery: Everything begins with a defined goal, and the data analysis journey isn’t an exception. Data discovery is the first stage of data munging, where data analysts define the data’s purpose and how to achieve it through data analytics. The goal is to identify the potential uses and requirements of data.
 **At the discovery stage, the focus is more on business requirements related to data rather than technical specifications. For instance, data analysts focus on what key performance indicators or metrics will be helpful to improve the sales cycle instead of how to get the relevant numbers for analytics.
@@ Line 44: / Line 44: @@
-===The Importance of Data Munging<ref>[https://www.integrate.io/glossary/what-is-data-munging/ Why Use Data Munging?]</ref>===
+== The Importance of Data Munging<ref>[https://www.integrate.io/glossary/what-is-data-munging/ Why Use Data Munging?]</ref> ==
 Most organizations have multiple, disparate sources of incoming data. These sources will all have different standards for validating data and catching errors. Some may simply output the data “as-is.”
@@ Line 55: / Line 55: @@
 == Data Munging Process<ref>[https://www.talend.com/resources/what-is-data-munging/ The data munging process: An overview]</ref> ==
 With the wide variety of verticals, use cases, types of users, and systems utilizing enterprise data today, the specifics of munging can take on myriad forms.
 *Data exploration: Munging usually begins with data exploration. Whether an analyst is merely peeking at completely new data in initial data analysis (IDA), or a data scientist begins the search for novel associations in existing records in exploratory data analysis (EDA), munging always begins with some degree of data discovery.
@@ Line 64: / Line 63: @@
-===Example of Data Munging<ref>[https://www.experian.co.uk/business/glossary/data-munging/ What is an example of Data Munging?]</ref>===
+== Example of Data Munging<ref>[https://www.experian.co.uk/business/glossary/data-munging/ What is an example of Data Munging?]</ref> ==
 A specific example of data munging might be used in Machine Learning, in order to restructure data in a way that could be used by a learning algorithm.
@@ Line 72: / Line 71: @@
-===Benefits of Data Munging<ref>[https://www.voxco.com/blog/data-munging-the-process-to-clean-and-prepare-data/ Benefits of Data Munging]</ref>===
+== Benefits of Data Munging<ref>[https://www.voxco.com/blog/data-munging-the-process-to-clean-and-prepare-data/ Benefits of Data Munging]</ref> ==
 Data munging will help to prepare and manipulate data before data scientists go through rigorous analysis. There are many benefits of data munging such as
 *It increases efficiency,
@@ Line 80: / Line 79: @@
-===Challenges to Data Munging<ref>[https://www.lido.app/post/what-is-data-munging Challenges to Data Munging]</ref>===
+== Challenges to Data Munging<ref>[https://www.lido.app/post/what-is-data-munging Challenges to Data Munging]</ref> ==
 There are generally three main challenges that affect the ETL processes, which include data munging:
 *Scaling. The sheer volume of raw data you need to process grows over time even with no addition to the number of data sources. A good data munging setup will be able to adjust to increases in the volume of raw data to process.
@@ Line 89: / Line 88: @@
 == See Also ==
-<div style="column-count:2;-moz-column-count:4;-webkit-column-count:4">
+*[[Data]]
 *[[Cloud Computing]]
-</div>
+*[[Extract, Transform, Load (ETL)]]
 == References ==
 <references />
 __NOTOC__

@@ Line 90: / Line 90: @@
 == See Also ==
 <div style="column-count:2;-moz-column-count:4;-webkit-column-count:4">
+*[[Cloud Computing]]
 </div>

@@ Line 90: / Line 90: @@
 == See Also ==
 <div style="column-count:2;-moz-column-count:4;-webkit-column-count:4">
-*[[Data]]
 </div>

@@ Line 90: / Line 90: @@
 == See Also ==
 <div style="column-count:2;-moz-column-count:4;-webkit-column-count:4">
-[[Data Compatibility]]<br />
+*[[Data]]
-[[Data Access]]<br />
+*[[Data Access]]
-[[Data Analysis]]<br />
+*[[Data Analysis]]
-[[Data Analytics]]<br />
+*[[Data Analytics]]
-[[Data Architecture]]<br />
+*[[Data Architecture]]
-[[Data Asset Framework (DAF)]]<br />
+*[[Data Asset Framework (DAF)]]
-[[Data Buffer]]<br />
+*[[Data Buffer]]
-[[Data Center]]<br />
+*[[Data Center]]
-[[Data Center Infrastructure]]<br />
+*[[Data Center Infrastructure]]
-[[Data Center Infrastructure Management (DCIM)]]<br />
+*[[Data Center Infrastructure Management (DCIM)]]
-[[Data Cleansing]]<br />
+*[[Data Cleansing]]
-[[Big Data]]<br />
+*[[Data Compatibility]]
-[[Big Data Integration]]<br />
+*[[Data Governance]]
-[[Big Data Maturity Model (BDMM)]]<br />
+*[[Data Integration]]
-[[Metadata]]<br />
+*[[Data Management]]
-[[Data Collection]]<br />
+*[[Data Mining]]
-[[Data Compatibility]]<br />
+*[[Data Monitoring]]
-[[Data Consolidation]]<br />
+*[[Data Portability]]
-[[Data Deduplication]]<br />
+*[[Data Quality]]
-[[Data Delivery Platform (DDP)]]<br />
+*[[Data Reference Model (DRM)]]
-[[Data Description (Definition) Language (DDL)]]<br />
+*[[Data Security]]
-[[Data Dictionary]]<br />
+*[[Data Transformation]]
-[[Data Discovery]]<br />
+*[[Data Visualization]]
-[[Data Driven Organization]]<br />
+*[[Data Warehouse]]
 </div>

@@ Line 1: / Line 1: @@
 ===What is Data Munging?===
-'''Data Munging''' is the process of cleaning and transforming raw data into its desired format, usually to facilitate further analysis or visualization. Data munging can be done in Python or R, but it can also be performed with a spreadsheet program. The primary purpose of data munging is to take raw data and prepare it for use in an analysis. It helps to prepare data sets so that they can be leveraged by reporting tools or machine learning algorithms. Data Munging is a pre-processing step in the data-mining process.
+'''Data Munging''' is the process of cleaning and transforming raw data into its desired format, usually to facilitate further analysis or visualization. Data munging can be done in Python or R, but it can also be performed with a spreadsheet program. The primary purpose of data munging is to take raw data and prepare it for use in an analysis. It helps to prepare data sets so that they can be leveraged by reporting tools or machine learning algorithms. Data Munging is a pre-processing step in the data-mining process. Data Munging is also referred to as Data Wrangling.
 The process of manual data cleansing prior to analysis is known as data munging. This process can be a laborious task without the right tools. The common interface used for data munging is often Excel, which lacks the sophistication for collaboration and automation to make the process efficient. 80% of the time spent on data analytics is allocated to data munging, where IT manually cleans the data to pass over to business users who perform analytics. Data munging can be a time-consuming and disjointed process that can get in the way of extracting true value and potential from data.<ref>[https://www.trifacta.com/data-munging/ From Data Munging to Data Wrangling]</ref>

@@ Line 1: / Line 1: @@
-===What is Data Munging?<ref>[https://www.trifacta.com/data-munging/ Defining Data Munging]</ref>===
+===What is Data Munging?===
-The process of manual data cleansing prior to analysis is known as '''data munging'''. This process can be a laborious task without the right tools. The common interface used for data munging is often Excel, which lacks the sophistication for collaboration and automation to make the process efficient. 80% of the time spent on data analytics is allocated to data munging, where IT manually cleans the data to pass over to business users who perform analytics. Data munging is a time-consuming and disjointed process that gets in the way of extracting true value and potential from data.
+'''Data Munging''' is the process of cleaning and transforming raw data into its desired format, usually to facilitate further analysis or visualization. Data munging can be done in Python or R, but it can also be performed with a spreadsheet program. The primary purpose of data munging is to take raw data and prepare it for use in an analysis. It helps to prepare data sets so that they can be leveraged by reporting tools or machine learning algorithms. Data Munging is a pre-processing step in the data-mining process.
@@ Line 50: / Line 70: @@
 JohnDOTdoeATJohnDoeDOTcom or John(dot)doe(at)John(dot)doe(dot)com

← Older revision		Revision as of 07:14, 16 October 2022
Line 6:		Line 6:

	== See Also ==		== See Also ==
−	*[[Data]]	+
−	*[[Business_Strategy\|Business Strategy]]
−	*[[IT_Strategy_(Information_Technology_Strategy)\|IT Strategy]]
−	*[[E-Strategy\|e-Strategy]]
−	*[[IT_Governance\|IT Governance]]
−	*[[Enterprise_Architecture\|Enterprise Architecture]]
−	*[[IT_Sourcing_(Information_Technology_Sourcing)\|IT Sourcing]]
−	*[[IT Operations_(Information_Technology_Operations)\|IT Operations]]
−	*[[Chief_Information_Officer_(CIO)\|CIO]]

← Older revision		Revision as of 23:37, 15 October 2022
Line 1:		Line 1:
	'''Content Coming Soon'''		'''Content Coming Soon'''
		+
		+
		+
		+
		+
		+	== See Also ==
		+	*[[Data]]
		+	*[[Business_Strategy\|Business Strategy]]
		+	*[[IT_Strategy_(Information_Technology_Strategy)\|IT Strategy]]
		+	*[[E-Strategy\|e-Strategy]]
		+	*[[IT_Governance\|IT Governance]]
		+	*[[Enterprise_Architecture\|Enterprise Architecture]]
		+	*[[IT_Sourcing_(Information_Technology_Sourcing)\|IT Sourcing]]
		+	*[[IT Operations_(Information_Technology_Operations)\|IT Operations]]
		+	*[[Chief_Information_Officer_(CIO)\|CIO]]
		+
		+
		+
		+
		+	== References ==
		+	<references />