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Question
Asked 15 May 2017
What are the differences between Analysis and Processing Big data?
Hi all,
I want to know the differences between analysis and processing big data because I got confused when I read
Most recent answer
The main difference between data analysis and analytics lies in their approach, as analysis looks towards the past while analytics towards the future. That’s the basic difference, let’s dig further to get in-depth knowledge about data analysis vs. analytics and fully understand both approaches and how these are helpful for the businesses.
Other Key Difference
- Data analysis is a process involving the collection, manipulation, and examination of data for getting a deep insight. Data analytics is taking the analyzed data and working on it in a meaningful and useful way to make well-versed business decisions.
- Data analysis helps design a strong business plan for businesses, using its historical data that tell about what worked, what did not, and what was expected from a product or service.
- Data analytics helps businesses in utilizing the potential of the past data and in turn identifying new opportunities that would help them plan future strategies. It helps in business growth by reducing risks, costs, and making the right decisions.
The most important thing to remember is that the accuracy of the analytics is based on the underlying data set. If there are inconsistencies or errors in the dataset, it will result in inefficiencies or outright incorrect analytics.
The primary goal of data analytics is to help individuals or organizations to make informed decisions based on patterns, behaviors, trends, preferences, or any type of meaningful data extracted from a collection of data.
Thanks
@Lissa-Coffey-2
All Answers (6)
University of Aberdeen
Hi Taghreed
Big data is data which is collected at scale. Often, this data will be captured raw, which means it will likely be highly unstructured. Unstructured data ia usually ignored for the purpose of normal Business Intelligence (BI) software, as it does not know how to deal with it. The business is unable to extract much value from the data at this stage.
As a general rule, this raw data needs to be put through a pre-processing operation, whereby the data becomes more structured in order for business systems to be able to analyse it properly. This is the point at which big data processing takes place.
Once the pre-processing is completed, then the data will be in a format that can be used by data analytics software, which can resolve more complex questions about the business. This is the point at which big data analytics can be performed, which allows the business to extract more meaning from the data they have harvested.
I hope that helps.
Regards
Bob
1 Recommendation
University of Mysore,Mysore, India
Thank you so much Sir
I got it , that means the methods and the techniques that are used in pre-processing and analysis are different ? also what is the differences between pre-processing and processing ?
Regards
Robert Gordon University
Data processing has to do with the conversion of data from one form to another. It could be post-processing, online processing or pre-processing. If the processing happens after analysis it is called post-processing, if it is in parallel with analysis it is online processing or real-time processing while if it happens before analysis it is called pre-processing. Data pre-processing could, in other words, be called data preparation. In Big data analytics, just as Bob Duncan had said, there is a need for data pre-processing before analysis because data are often unstructured, inconsistent, ambiguous, contain missing values and values may be in different scales. So, to improve the performance of data analytics algorithm to be used, data pre-processing is paramount.
Best regards!
Pascal
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University of Kurdistan Hewlêr (UKH)
Big Data processing deals with the data collection and preparation for later analysis. For example, collecting unstructured, structured, and semi-structured and using Hadoop and Map-Reduce techniques in order to prepare data for analysis. This involves understanding the problem from SCV (Speed, Consistency, and Volume) perspective and if the data is accessed in realtime or batch to define the proper "workloads" and to distribute the tasks properly.
The analysis, on the other hand, focuses on the prepared data in a form that lets different techniques such as machine learning techniques to be applied in order to understand the patterns, trends, relationships and such among the data.
There is also another term, data analytics, that is more widely used in Big Data, which refers to the management of a combination of data processing and data analysis methods development. In fact, in data analytics, data scientists try to develop scientific methods that would be used in data analysis.
A very good resource that you can cosult with is: "Big Data Fundamentals" by Erl et al.
Near Miss Management, LLC; Operations and Safety Solutions, LLC
Briefly, data processing is changing the form that the data is in, or making the data easier to view. Data analysis addresses deriving additional intelligence from the data itself - creating insight and new information on which actions can be taken.
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Yahoo
The main difference between data analysis and analytics lies in their approach, as analysis looks towards the past while analytics towards the future. That’s the basic difference, let’s dig further to get in-depth knowledge about data analysis vs. analytics and fully understand both approaches and how these are helpful for the businesses.
Other Key Difference
- Data analysis is a process involving the collection, manipulation, and examination of data for getting a deep insight. Data analytics is taking the analyzed data and working on it in a meaningful and useful way to make well-versed business decisions.
- Data analysis helps design a strong business plan for businesses, using its historical data that tell about what worked, what did not, and what was expected from a product or service.
- Data analytics helps businesses in utilizing the potential of the past data and in turn identifying new opportunities that would help them plan future strategies. It helps in business growth by reducing risks, costs, and making the right decisions.
The most important thing to remember is that the accuracy of the analytics is based on the underlying data set. If there are inconsistencies or errors in the dataset, it will result in inefficiencies or outright incorrect analytics.
The primary goal of data analytics is to help individuals or organizations to make informed decisions based on patterns, behaviors, trends, preferences, or any type of meaningful data extracted from a collection of data.
Thanks
@Lissa-Coffey-2