Applications of Big Data Technology to Business

1606 words (6 pages) Business Assignment

14th Oct 2020 Business Assignment Reference this

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Subject

  1. How integrating Big Data will help this company become more effective with operations.
  2. What Big Data is and how it will be used.
  3. Why using big data elements would be important to business profitability
  4. How the use of Big Data will help with web development specifically in value and in profits.
  5. Comparison of tools and methodologies

How integrating Big Data will help this company become more effective with operations

Using and understanding big data can change the way this business compete and operate. Such data combined with analytics allows for competitive advantages in terms of cost reductions, time reductions, new product development, optimized offerings, and smart decision making. This will enable our business to discover hidden insights that our competitors don’t have access to. Big Data will become more effective in our daily operations with automation, in-depth insights, and data-driven decision making in obtaining enumerable benefits. Decisions can be made smarter in a structured data driven approach.

What Big Data is and how it will be used

Big data refers to gathering and storing huge amount of data for analytical purposes in deriving real-time business insights. Big Data is used to track consumers, risk, profit, performance, productivity in an effort to have a better understanding of the business as a whole in terms of customers and their behaviors.

It is typically characterized by the four “V’s”:

  • Volume: scale of data
  • Variety: different forms of data
  • Velocity: continuous stream of data being analyzed
  • Veracity: uncertainty of data that needs to be tested for quality of the data.

Figure 1: The four V's (EY. 2014, April)

                     

Why using big data elements would be important to business probability

Big Data plays a valuable role in strategy and planning in the business probability to being able to see the bigger picture. Simply put, owners and managers can be better informed in decision making to increase profit and to really understand our customers.

Figure 2: Analytics can enhance all areas of operations (Romanenko, A., & Artamonov, A., 2014, May/June)

 

How the use of Big Data will help with web development specifically in value and in profits

Big data sets the tone for web development and the impact it will have in this company is huge in value and in profits. When the two is interwoven it provides better customer service in the following ways:

       Monitoring of user engagement to get insights on how our customers behave online

       Development of enhanced graphic designs with the aid of AI

       Ensuring ease of operations of the website

       Using predictive analytics in enhancing digital properties for future trends

Big data is changing the future of web development! With these two combinations our business can create a data driven web design for gains in customers and leads from big data to big profits.

Comparison of tools and methodologies

The importance of data is tremendous so the question of the day is, which tools to use? Tool are vital in collecting, sorting, and analyzing data to provide the business with information and predictions for future trends. Let’s compare and contrast the big data tools and methodologies used to integrate Big Data into the company’s operations in retrospect to web development (Unknown. 2013, June 03):

Apache Hadoop

  • Free Java based frameworkthat runs in parallel on a cluster with the ability to allow user to process data across all nodes.
  • Hadoop distributed File System aka storage that splits big data and distribute across many nodes in a cluster.
  • Replicates data in a cluster providing high availability

Microsoft HDInsight

  • Big Data solution powered by Hadoop as a service on the cloud.
  • HDInsight utilizes Azure Blob storage
  • Provides high availability

NoSQL

  • The databases store unstructured data w/no particular schema
  • Provides better performance in storing massive amount of data
  • Many open-source NoSQL DBS

Hive

  • Distributed data management for Hadoop
  • Supports SQL-like query option HiveSQL to accessing big data
  • Primarily used for Data mining
  • Runs on top of Hadoop

Sqoop

  • Connects Hadoop w/various relational DBS for transferring of data
  • Used to transfer structured data to Hadoop or Hive

PolyBase

  • Works on top of SQL Server 2012 Parallel Data Warehouse to access data stored in PDW.
  • PDW built in for processing any volume of relational data to provide and integrate with Hadoop allowing users to access non-relational data.

Big data in EXCEL

  • Connect data stored in Hadoop with Excel 2013
  • Hortonworks (providing Enterprise Apache Hadoop) to access big data stored in their Hadoop platform using Excel 2013.
  • Utilize Power View feature of Excel 2013 for summarization of data.

Presto

  • Built to handle petabytes of data developed by Face Book as an open-sourced (SQL-on-Hadoop)
  • Does not depend on MapReduce technique
  • Can quickly retrieve data

Tools for Data mining (Gulipalli, G., 2015, November 18)

Rapid Miner (erstwhile YALE):

  • Open source & no coding needed for advanced analytics
  • Written in Java that incorporates multifaceted data mining functions like data preprocessing, visualization, predictive analysis
  • Integrated w/ WEKA & R-tool to directly give models from scripts written in the former two.

WEKA:

  • Java based & free
  • Features include visualization, predictive analysis, modeling techniques, clustering, association, regression, and classification.

R-Programming Tool: 

  • Written in C and FORTRAN for data miners to write scripts
  • Used for statistical and analytical software data mining
  • Supports graphical analysis modeling, classification, clustering and time based analysis.

Python based Orange and NTLK: 

  • Orange is an open source written in Python w/useful data analytics, text analysis, & machine learning features embedded in visual programming interface.
  • NTLK composed in Python for processing data mining tools that consists of data mining, machine learning, and data scraping features.

Knime: 

  • Data Preprocessing for data extractions, transformation and loading
  • GUI that shows network of data nodes
  • Comes with a modular data pipe lining, leveraging machine learning, and data mining concepts.

The tools and methodologies mentioned above are useful in integrating Big Data. Such tools are vital more than ever for all businesses, big or small. These tools and methodologies can leverage our existing data stores to make informed business decisions that will give this company a competitive edge in transforming this business into a big web development company.

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