SlideShare a Scribd company logo
1 of 5
Download to read offline
Automated Data Analytics: How, When &
Why?
Do you find repetitive and mundane work preventing your data analysts, engineers,
and analysts from performing their best? Then, think about automatizing your data
analytics to let them be free of mundane tasks. 
Automated analytics: What are they? 
Automated analytics involves the use of computers to produce analytical products
using very little or no human interaction. 
Through automating analytics, you create systems that can automate a portion or even
the whole data flow that brings a data-related product to life, from automating
dashboards for business intelligence to self-governing machine-learning models based
on data models. 
You can try automated analytics with freemium versions of various tools available in
the market. 
What are some examples of automated analysis? 
Each step in the pipeline of data may be automated using data analysis as per your
requirements: 
1. Data collection. Before you can analyze data, it is necessary to have the data.
However, obtaining the data could be lengthy. Finding the scattered Excel files to
create an app that retrieves data from third-party Apps and gathering the data required
to conduct analysis could be a long process. Automating data collection can help to
speed up the time it takes to deliver an analysis of data. Use software to automatically
collect information from different sources and put them in a timetable to keep the data
you receive current. 
2. Dashboards. Imagine what it will take to create an effective dashboard that
monitors the company's KPIs. It is essential to gather the relevant data, then analyze it
to extract metrics (with Excel, Python, R or whatever your preferred language is) and
then display the results on a graph that decision-makers can view. The entire process
from beginning to end is lengthy. Automating scripts to analyze the data or using tools
like Looker or Metabase that automatically display data could reduce the time when
creating dashboards. 
3. Business intelligence. The process of establishing business intelligence excellence
requires far more than just dashboards. It would help to look at various metrics and
breakdowns for the various business units to gain new data. Are monthly orders rising
within the EMEA region, however not so in APAC? Automate the process of creating
the different breakdowns through an automated method of preparing data. Create
cubes that combine data broken down according to other dimensions in your
warehouse. For instance, the total amount of transactions (aggregation) by geographic
location (dimension). Or the average order size (aggregation) by customer segment
(dimension). You can then take a look at the cubes and gain conclusions from the
automated analysis. 
4. Models of machine learning and automation of big data. The experts in machine
learning develop statistical models that surpass humans in a variety of tasks. For
instance, a machine-learning model is far more adept in forecasting which ads will
receive clicks than humans. However, building a predictive click model on its own
won't cut it. Consumers' preferences are changing and what they buy on the internet is
changing too. Unlike data scientists or machine-learning engineers creating models
every couple of weeks, you can automate model creation and selection. With the help
of automation, you can construct diverse models by choosing various parameters
based on diverse data combinations. You can then automatically select which model
has the highest predictive score for clicks and apply it to production. Automation isn't
only for the development of models. Financial institutions and banks employ
advanced anomaly detection algorithms that look for signs that could indicate
fraudulent transactions. When the signal is above an amount, the models initiate an
account audit through alerts sent to human inspections. 
The possibilities for automated data analysis are limitless, and that's why you should
take the initiative? 
What are the advantages of automated data analysis? 
There are four primary benefits of automating your analytics. 
1. Increased/ speed of analytics. The time it takes from request to delivery of the
analytic report is reduced when you automate (part of) the process for creating the
analytical report.
 
2. Reduce time and costs. Automating (part or all of) these pipelines reduce
scientists', analysts' and engineers' working hours. This also means that you have less
to pay for rota-related tasks as computers take care of them. 
3. Let your time be used to do creative work. Data experts don't need to work on
tweaking pipelines manually, and they are free to solve the tough business issues and
come up with innovative ways to generate revenue. 
4. Enhanced processes and systems. Conducting analytics by hand often requires
complicated procedures. Who do your analysts have to speak to, making a note of all
the requirements for clearing the database before analysis, as well as multiple
coordination meetings to transfer the data across departments and staff. When you
automatize analytics, you can skip those parts that are susceptible to human errors.
When you spot a mistake in the automated processes, you just need to fix it only once.
Automated processes help you create future-proof systems and processes. 
What is the best time to automate the process of data
analysis? 
Some tasks in analytics are not perfect to be automated. Therefore, before you begin
automating, ensure: 
1. The proposed task is of significant significance. Automating the job can either
address the business issue (delayed insights result in missed opportunities) and
improve on the overall financial bottom of the line (the efficiency gains translate into
actual cost savings) or even provide the possibility of business expansion (the
automated analytics uncovers new revenue sources as well as cost reductions). 
2. The task that is being considered for the candidate is not a one-time task. If
you create the report once, then there is no reason to automate the process. It's often
because of repetition that we learn what parts of the process are simpler to automate.
For instance, making the identical KPI dashboards every three weeks in succession
turns around the light bulb above our heads. The dashboards utilize similar data
sources, and so automation of data extraction can aid in speeding up the reporting with
help of business intelligence tools. 
3. Automation can save time by decreasing the chance of errors. An automated
system usually costs more but it is the best option as it will lessens the chance of
errors that are caused due to operating manually. One can also automate data
validation to detect typos, flag and impute missing values. This type of data analysis
automation not only streamlines data modeling processes, and also enables adherence
to models by automatically transforming data.
4. You're ready to improve continuously. The automated system cannot be perfect
the first time. You must be prepared to continually enhance the efficiency of your data
analytics system for success. There are two implications: (1) you must adopt a growth
mindset in which you think about ways to improve the efficiency of your system. (2)
you must create a set of criteria and monitoring to determine if the system is
performing efficiently. 
How do you automate the process of data analysis? 
The way you implement data analytics will depend on the degree of automation you're
taking into consideration: 
1. Partial automation. Partial automation is a way to automate existing processes but
eliminates some of the manual labor. For instance, your team of data analysts would
create scripts that accelerate certain aspects of their work. 
2. Final-to-last production. Automation is set up from end to end, and computers
create information products for humans to examine and decide on. Automation, for
instance, produces KPI dashboards or alerts about fraud without the employee
handling anything. 
3. Complete automation. Complete automation can make business decisions in near-
real-time with no human involvement. For instance, an AI algorithm automatically
determines if the information is sufficient to buy and sell the assets. 
As you progress towards complete automation, the greater the benefit of automation
increases from merely creating time savings to having independent effects on the
company's bottom line. 
How do you begin automatizing your data analysis? 
1. Find analytical tasks that can be automated. 
The ideal task that should be chosen for automation should pass the following
checks:  
a) It should have business value  
b) Is repetitive  
c) Time saving  
d) Reduces errors  
e) Can optimize the current process further 
2. Set expectations through formalizing the guidelines to ensure success. In the
beginning, automation is an opportunity to consolidate processes and reduce time. Be
clear about what you're looking for. Begin with a small task, such as automating just
one process of data pipeline. 
3. Make use of dedicated instruments and platforms to accelerate
automation. Your engineers can write the SQL procedures and Python scripts to
automate codes, but using specific tools and platforms can save time in creating
automatized pipelines. 
4. Repeat and analyze. As you automatize part of the processes for data analytics and
products, you should evaluate them against the criteria for success that you have set
before. If successful, automate more. 
CONCLUSION -  
Companies that deal with big data may benefit from automating a portion of their data
analytics infrastructure. Data lakes are stuffed with unstructured data that machines
can analyze faster than humans. In addition, today's data warehouses are characterized
by strict requirements for data modeling and processing, which can easily be
streamlined by automated data analytics. 

More Related Content

Similar to Automated Data Analytics How When & Why.pdf

Order vs Chaos: Taming Digital Analytics Complexity With Automation
Order vs Chaos: Taming Digital Analytics Complexity With AutomationOrder vs Chaos: Taming Digital Analytics Complexity With Automation
Order vs Chaos: Taming Digital Analytics Complexity With AutomationObservePoint
 
Intro of Key Features of Auto eCAAT Ent Software
Intro of Key Features of Auto eCAAT Ent SoftwareIntro of Key Features of Auto eCAAT Ent Software
Intro of Key Features of Auto eCAAT Ent Softwarerafeq
 
The Best Process Automation Software for Business Owners
The Best Process Automation Software for Business OwnersThe Best Process Automation Software for Business Owners
The Best Process Automation Software for Business OwnersKashish Trivedi
 
Intelligent Process Management
Intelligent Process ManagementIntelligent Process Management
Intelligent Process ManagementDafna Levy
 
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...Agile Testing Alliance
 
Test Data Management: The Underestimated Pain
Test Data Management: The Underestimated PainTest Data Management: The Underestimated Pain
Test Data Management: The Underestimated PainChelsea Frischknecht
 
Different types of data processing
Different types of data processingDifferent types of data processing
Different types of data processingShyam Sunder Budhwar
 
Why Big Data Automation is Important for Your Business.pdf
Why Big Data Automation is Important for Your Business.pdfWhy Big Data Automation is Important for Your Business.pdf
Why Big Data Automation is Important for Your Business.pdfData Science Council of America
 
Why Big Data Automation is Important for Your Business.pdf
Why Big Data Automation is Important for Your Business.pdfWhy Big Data Automation is Important for Your Business.pdf
Why Big Data Automation is Important for Your Business.pdfData Science Council of America
 
Intro of Key Features of Auto eCAAT Pro Software
Intro of Key Features of  Auto eCAAT Pro SoftwareIntro of Key Features of  Auto eCAAT Pro Software
Intro of Key Features of Auto eCAAT Pro Softwarerafeq
 
Monitoring and Measuring SharePoint to Guarantee Your ROI
Monitoring and Measuring SharePoint to Guarantee Your ROIMonitoring and Measuring SharePoint to Guarantee Your ROI
Monitoring and Measuring SharePoint to Guarantee Your ROIChristian Buckley
 
Data Processing and its Types
Data Processing and its TypesData Processing and its Types
Data Processing and its TypesMuhammad Zubair
 
Efficiently Detecting and Analyzing Spam Reviews Using Live Data Feed
Efficiently Detecting and Analyzing Spam Reviews Using Live Data FeedEfficiently Detecting and Analyzing Spam Reviews Using Live Data Feed
Efficiently Detecting and Analyzing Spam Reviews Using Live Data FeedIRJET Journal
 
Module 1 introduction to web analytics
Module 1   introduction to web analyticsModule 1   introduction to web analytics
Module 1 introduction to web analyticsGayathri Choda
 
Module 1 introduction to web analytics
Module 1   introduction to web analyticsModule 1   introduction to web analytics
Module 1 introduction to web analyticsGayathri Choda
 
Control-M - Guide to Selecting a Workflow Orchestration Product
Control-M - Guide to Selecting a Workflow Orchestration ProductControl-M - Guide to Selecting a Workflow Orchestration Product
Control-M - Guide to Selecting a Workflow Orchestration ProductIT Central Station
 
Test automation: Are Enterprises ready to bite the bullet?
Test automation: Are Enterprises ready to bite the bullet?Test automation: Are Enterprises ready to bite the bullet?
Test automation: Are Enterprises ready to bite the bullet?Aspire Systems
 
KETL Quick guide to data analytics
KETL Quick guide to data analytics KETL Quick guide to data analytics
KETL Quick guide to data analytics KETL Limited
 

Similar to Automated Data Analytics How When & Why.pdf (20)

Order vs Chaos: Taming Digital Analytics Complexity With Automation
Order vs Chaos: Taming Digital Analytics Complexity With AutomationOrder vs Chaos: Taming Digital Analytics Complexity With Automation
Order vs Chaos: Taming Digital Analytics Complexity With Automation
 
IBM Planning Analytics
IBM Planning AnalyticsIBM Planning Analytics
IBM Planning Analytics
 
Intro of Key Features of Auto eCAAT Ent Software
Intro of Key Features of Auto eCAAT Ent SoftwareIntro of Key Features of Auto eCAAT Ent Software
Intro of Key Features of Auto eCAAT Ent Software
 
The Best Process Automation Software for Business Owners
The Best Process Automation Software for Business OwnersThe Best Process Automation Software for Business Owners
The Best Process Automation Software for Business Owners
 
Intelligent Process Management
Intelligent Process ManagementIntelligent Process Management
Intelligent Process Management
 
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
#ATAGTR2021 Presentation : "Use of AI and ML in Performance Testing" by Adolf...
 
Test Data Management: The Underestimated Pain
Test Data Management: The Underestimated PainTest Data Management: The Underestimated Pain
Test Data Management: The Underestimated Pain
 
Different types of data processing
Different types of data processingDifferent types of data processing
Different types of data processing
 
Why Big Data Automation is Important for Your Business.pdf
Why Big Data Automation is Important for Your Business.pdfWhy Big Data Automation is Important for Your Business.pdf
Why Big Data Automation is Important for Your Business.pdf
 
Why Big Data Automation is Important for Your Business.pdf
Why Big Data Automation is Important for Your Business.pdfWhy Big Data Automation is Important for Your Business.pdf
Why Big Data Automation is Important for Your Business.pdf
 
Intro of Key Features of Auto eCAAT Pro Software
Intro of Key Features of  Auto eCAAT Pro SoftwareIntro of Key Features of  Auto eCAAT Pro Software
Intro of Key Features of Auto eCAAT Pro Software
 
Monitoring and Measuring SharePoint to Guarantee Your ROI
Monitoring and Measuring SharePoint to Guarantee Your ROIMonitoring and Measuring SharePoint to Guarantee Your ROI
Monitoring and Measuring SharePoint to Guarantee Your ROI
 
Data Processing and its Types
Data Processing and its TypesData Processing and its Types
Data Processing and its Types
 
Efficiently Detecting and Analyzing Spam Reviews Using Live Data Feed
Efficiently Detecting and Analyzing Spam Reviews Using Live Data FeedEfficiently Detecting and Analyzing Spam Reviews Using Live Data Feed
Efficiently Detecting and Analyzing Spam Reviews Using Live Data Feed
 
Module 1 introduction to web analytics
Module 1   introduction to web analyticsModule 1   introduction to web analytics
Module 1 introduction to web analytics
 
Module 1 introduction to web analytics
Module 1   introduction to web analyticsModule 1   introduction to web analytics
Module 1 introduction to web analytics
 
Control-M - Guide to Selecting a Workflow Orchestration Product
Control-M - Guide to Selecting a Workflow Orchestration ProductControl-M - Guide to Selecting a Workflow Orchestration Product
Control-M - Guide to Selecting a Workflow Orchestration Product
 
Test automation: Are Enterprises ready to bite the bullet?
Test automation: Are Enterprises ready to bite the bullet?Test automation: Are Enterprises ready to bite the bullet?
Test automation: Are Enterprises ready to bite the bullet?
 
Algo and flowchart
Algo and flowchartAlgo and flowchart
Algo and flowchart
 
KETL Quick guide to data analytics
KETL Quick guide to data analytics KETL Quick guide to data analytics
KETL Quick guide to data analytics
 

More from Data Science Council of America

The Simple 5-Step Process for Creating a Winning Data Pipeline.pdf
The Simple 5-Step Process for Creating a Winning Data Pipeline.pdfThe Simple 5-Step Process for Creating a Winning Data Pipeline.pdf
The Simple 5-Step Process for Creating a Winning Data Pipeline.pdfData Science Council of America
 
The Value of Data Visualization for Data Science Professionals.pdf
The Value of Data Visualization for Data Science Professionals.pdfThe Value of Data Visualization for Data Science Professionals.pdf
The Value of Data Visualization for Data Science Professionals.pdfData Science Council of America
 
Data Science - The New Skill for Today’s Entrepreneurs.pdf
Data Science - The New Skill for Today’s Entrepreneurs.pdfData Science - The New Skill for Today’s Entrepreneurs.pdf
Data Science - The New Skill for Today’s Entrepreneurs.pdfData Science Council of America
 
Know How to Create and Visualize a Decision Tree with Python.pdf
Know How to Create and Visualize a Decision Tree with Python.pdfKnow How to Create and Visualize a Decision Tree with Python.pdf
Know How to Create and Visualize a Decision Tree with Python.pdfData Science Council of America
 
Pandas vs. SQL – Tools that Data Scientists use most often.pdf
Pandas vs. SQL – Tools that Data Scientists use most often.pdfPandas vs. SQL – Tools that Data Scientists use most often.pdf
Pandas vs. SQL – Tools that Data Scientists use most often.pdfData Science Council of America
 
Is Data Visualization Literacy Part of Your Company Culture.pdf
Is Data Visualization Literacy Part of Your Company Culture.pdfIs Data Visualization Literacy Part of Your Company Culture.pdf
Is Data Visualization Literacy Part of Your Company Culture.pdfData Science Council of America
 
Maximize Your D&A Strategy The Role Of A Citizen Data Scientist.pdf
Maximize Your D&A Strategy The Role Of A Citizen Data Scientist.pdfMaximize Your D&A Strategy The Role Of A Citizen Data Scientist.pdf
Maximize Your D&A Strategy The Role Of A Citizen Data Scientist.pdfData Science Council of America
 
Data Observability- The Next Frontier of Data Engineering Pdf.pdf
Data Observability- The Next Frontier of Data Engineering Pdf.pdfData Observability- The Next Frontier of Data Engineering Pdf.pdf
Data Observability- The Next Frontier of Data Engineering Pdf.pdfData Science Council of America
 
How To Transform Your Analytics Maturity Model Levels, Technologies, and Appl...
How To Transform Your Analytics Maturity Model Levels, Technologies, and Appl...How To Transform Your Analytics Maturity Model Levels, Technologies, and Appl...
How To Transform Your Analytics Maturity Model Levels, Technologies, and Appl...Data Science Council of America
 
Importance of Data-Driven Storytelling Data Analysis &amp Visual Narratives.pdf
Importance of Data-Driven Storytelling Data Analysis &amp Visual Narratives.pdfImportance of Data-Driven Storytelling Data Analysis &amp Visual Narratives.pdf
Importance of Data-Driven Storytelling Data Analysis &amp Visual Narratives.pdfData Science Council of America
 
Top Trends & Predictions That Will Drive Data Science in 2022.pdf
Top Trends & Predictions That Will Drive Data Science in 2022.pdfTop Trends & Predictions That Will Drive Data Science in 2022.pdf
Top Trends & Predictions That Will Drive Data Science in 2022.pdfData Science Council of America
 
Essential capabilities of data scientist to have in 2022
Essential capabilities of data scientist to have in 2022Essential capabilities of data scientist to have in 2022
Essential capabilities of data scientist to have in 2022Data Science Council of America
 

More from Data Science Council of America (20)

The Simple 5-Step Process for Creating a Winning Data Pipeline.pdf
The Simple 5-Step Process for Creating a Winning Data Pipeline.pdfThe Simple 5-Step Process for Creating a Winning Data Pipeline.pdf
The Simple 5-Step Process for Creating a Winning Data Pipeline.pdf
 
Why Data Scientists Should Learn Machine Learning.pdf
Why Data Scientists Should Learn Machine Learning.pdfWhy Data Scientists Should Learn Machine Learning.pdf
Why Data Scientists Should Learn Machine Learning.pdf
 
The Value of Data Visualization for Data Science Professionals.pdf
The Value of Data Visualization for Data Science Professionals.pdfThe Value of Data Visualization for Data Science Professionals.pdf
The Value of Data Visualization for Data Science Professionals.pdf
 
Top 3 Interesting Careers in Big Data.pdf
Top 3 Interesting Careers in Big Data.pdfTop 3 Interesting Careers in Big Data.pdf
Top 3 Interesting Careers in Big Data.pdf
 
Achieving Business Success with Data.pdf
Achieving Business Success with Data.pdfAchieving Business Success with Data.pdf
Achieving Business Success with Data.pdf
 
Data Science - The New Skill for Today’s Entrepreneurs.pdf
Data Science - The New Skill for Today’s Entrepreneurs.pdfData Science - The New Skill for Today’s Entrepreneurs.pdf
Data Science - The New Skill for Today’s Entrepreneurs.pdf
 
Know How to Create and Visualize a Decision Tree with Python.pdf
Know How to Create and Visualize a Decision Tree with Python.pdfKnow How to Create and Visualize a Decision Tree with Python.pdf
Know How to Create and Visualize a Decision Tree with Python.pdf
 
Pandas vs. SQL – Tools that Data Scientists use most often.pdf
Pandas vs. SQL – Tools that Data Scientists use most often.pdfPandas vs. SQL – Tools that Data Scientists use most often.pdf
Pandas vs. SQL – Tools that Data Scientists use most often.pdf
 
Augmented Analytics The Future Of Data & Analytics.pdf
Augmented Analytics The Future Of Data & Analytics.pdfAugmented Analytics The Future Of Data & Analytics.pdf
Augmented Analytics The Future Of Data & Analytics.pdf
 
Is Data Visualization Literacy Part of Your Company Culture.pdf
Is Data Visualization Literacy Part of Your Company Culture.pdfIs Data Visualization Literacy Part of Your Company Culture.pdf
Is Data Visualization Literacy Part of Your Company Culture.pdf
 
Maximize Your D&A Strategy The Role Of A Citizen Data Scientist.pdf
Maximize Your D&A Strategy The Role Of A Citizen Data Scientist.pdfMaximize Your D&A Strategy The Role Of A Citizen Data Scientist.pdf
Maximize Your D&A Strategy The Role Of A Citizen Data Scientist.pdf
 
Data Observability- The Next Frontier of Data Engineering Pdf.pdf
Data Observability- The Next Frontier of Data Engineering Pdf.pdfData Observability- The Next Frontier of Data Engineering Pdf.pdf
Data Observability- The Next Frontier of Data Engineering Pdf.pdf
 
How To Transform Your Analytics Maturity Model Levels, Technologies, and Appl...
How To Transform Your Analytics Maturity Model Levels, Technologies, and Appl...How To Transform Your Analytics Maturity Model Levels, Technologies, and Appl...
How To Transform Your Analytics Maturity Model Levels, Technologies, and Appl...
 
Importance of Data-Driven Storytelling Data Analysis &amp Visual Narratives.pdf
Importance of Data-Driven Storytelling Data Analysis &amp Visual Narratives.pdfImportance of Data-Driven Storytelling Data Analysis &amp Visual Narratives.pdf
Importance of Data-Driven Storytelling Data Analysis &amp Visual Narratives.pdf
 
Top Trends & Predictions That Will Drive Data Science in 2022.pdf
Top Trends & Predictions That Will Drive Data Science in 2022.pdfTop Trends & Predictions That Will Drive Data Science in 2022.pdf
Top Trends & Predictions That Will Drive Data Science in 2022.pdf
 
Essential capabilities of data scientist to have in 2022
Essential capabilities of data scientist to have in 2022Essential capabilities of data scientist to have in 2022
Essential capabilities of data scientist to have in 2022
 
Senior Data Scientist
Senior Data ScientistSenior Data Scientist
Senior Data Scientist
 
Senior Big Data Analyst
Senior Big Data AnalystSenior Big Data Analyst
Senior Big Data Analyst
 
Associate Big Data Analyst | ABDA
Associate Big Data Analyst | ABDAAssociate Big Data Analyst | ABDA
Associate Big Data Analyst | ABDA
 
Senior Big Data Engineer Certification
Senior Big Data Engineer CertificationSenior Big Data Engineer Certification
Senior Big Data Engineer Certification
 

Recently uploaded

Introduction-to-Wazuh-and-its-integration.pptx
Introduction-to-Wazuh-and-its-integration.pptxIntroduction-to-Wazuh-and-its-integration.pptx
Introduction-to-Wazuh-and-its-integration.pptxmprakaash5
 
Landscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfLandscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfAarwolf Industries LLC
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
 
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkReact Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkPixlogix Infotech
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Mark Goldstein
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...itnewsafrica
 
JET Technology Labs White Paper for Virtualized Security and Encryption Techn...
JET Technology Labs White Paper for Virtualized Security and Encryption Techn...JET Technology Labs White Paper for Virtualized Security and Encryption Techn...
JET Technology Labs White Paper for Virtualized Security and Encryption Techn...amber724300
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Kaya Weers
 
Automation Ops Series: Session 3 - Solutions management
Automation Ops Series: Session 3 - Solutions managementAutomation Ops Series: Session 3 - Solutions management
Automation Ops Series: Session 3 - Solutions managementDianaGray10
 
Software Security in the Real World w/Kelsey Hightower
Software Security in the Real World w/Kelsey HightowerSoftware Security in the Real World w/Kelsey Hightower
Software Security in the Real World w/Kelsey HightowerAnchore
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesBernd Ruecker
 
Women in Automation 2024: Career session - explore career paths in automation
Women in Automation 2024: Career session - explore career paths in automationWomen in Automation 2024: Career session - explore career paths in automation
Women in Automation 2024: Career session - explore career paths in automationDianaGray10
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFMichael Gough
 
Transcript: New from BookNet Canada for 2024: BNC SalesData and LibraryData -...
Transcript: New from BookNet Canada for 2024: BNC SalesData and LibraryData -...Transcript: New from BookNet Canada for 2024: BNC SalesData and LibraryData -...
Transcript: New from BookNet Canada for 2024: BNC SalesData and LibraryData -...BookNet Canada
 
Bitdefender-CSG-Report-creat7534-interactive
Bitdefender-CSG-Report-creat7534-interactiveBitdefender-CSG-Report-creat7534-interactive
Bitdefender-CSG-Report-creat7534-interactivestartupro
 
Dynamical Context introduction word sensibility orientation
Dynamical Context introduction word sensibility orientationDynamical Context introduction word sensibility orientation
Dynamical Context introduction word sensibility orientationBuild Intuit
 
Which standard is best for your content?
Which standard is best for your content?Which standard is best for your content?
Which standard is best for your content?Rustici Software
 
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdfHCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdfROWELL MARQUINA
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentPim van der Noll
 

Recently uploaded (20)

Introduction-to-Wazuh-and-its-integration.pptx
Introduction-to-Wazuh-and-its-integration.pptxIntroduction-to-Wazuh-and-its-integration.pptx
Introduction-to-Wazuh-and-its-integration.pptx
 
Landscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdfLandscape Catalogue 2024 Australia-1.pdf
Landscape Catalogue 2024 Australia-1.pdf
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
 
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkReact Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App Framework
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...Abdul Kader Baba- Managing Cybersecurity Risks  and Compliance Requirements i...
Abdul Kader Baba- Managing Cybersecurity Risks and Compliance Requirements i...
 
JET Technology Labs White Paper for Virtualized Security and Encryption Techn...
JET Technology Labs White Paper for Virtualized Security and Encryption Techn...JET Technology Labs White Paper for Virtualized Security and Encryption Techn...
JET Technology Labs White Paper for Virtualized Security and Encryption Techn...
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)
 
Automation Ops Series: Session 3 - Solutions management
Automation Ops Series: Session 3 - Solutions managementAutomation Ops Series: Session 3 - Solutions management
Automation Ops Series: Session 3 - Solutions management
 
Software Security in the Real World w/Kelsey Hightower
Software Security in the Real World w/Kelsey HightowerSoftware Security in the Real World w/Kelsey Hightower
Software Security in the Real World w/Kelsey Hightower
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
 
Women in Automation 2024: Career session - explore career paths in automation
Women in Automation 2024: Career session - explore career paths in automationWomen in Automation 2024: Career session - explore career paths in automation
Women in Automation 2024: Career session - explore career paths in automation
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDF
 
Transcript: New from BookNet Canada for 2024: BNC SalesData and LibraryData -...
Transcript: New from BookNet Canada for 2024: BNC SalesData and LibraryData -...Transcript: New from BookNet Canada for 2024: BNC SalesData and LibraryData -...
Transcript: New from BookNet Canada for 2024: BNC SalesData and LibraryData -...
 
Bitdefender-CSG-Report-creat7534-interactive
Bitdefender-CSG-Report-creat7534-interactiveBitdefender-CSG-Report-creat7534-interactive
Bitdefender-CSG-Report-creat7534-interactive
 
Dynamical Context introduction word sensibility orientation
Dynamical Context introduction word sensibility orientationDynamical Context introduction word sensibility orientation
Dynamical Context introduction word sensibility orientation
 
Which standard is best for your content?
Which standard is best for your content?Which standard is best for your content?
Which standard is best for your content?
 
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdfHCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
HCI Lesson 1 - Introduction to Human-Computer Interaction.pdf
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
 

Automated Data Analytics How When & Why.pdf

  • 1. Automated Data Analytics: How, When & Why? Do you find repetitive and mundane work preventing your data analysts, engineers, and analysts from performing their best? Then, think about automatizing your data analytics to let them be free of mundane tasks.  Automated analytics: What are they?  Automated analytics involves the use of computers to produce analytical products using very little or no human interaction.  Through automating analytics, you create systems that can automate a portion or even the whole data flow that brings a data-related product to life, from automating dashboards for business intelligence to self-governing machine-learning models based on data models.  You can try automated analytics with freemium versions of various tools available in the market.  What are some examples of automated analysis?  Each step in the pipeline of data may be automated using data analysis as per your requirements:  1. Data collection. Before you can analyze data, it is necessary to have the data. However, obtaining the data could be lengthy. Finding the scattered Excel files to create an app that retrieves data from third-party Apps and gathering the data required to conduct analysis could be a long process. Automating data collection can help to speed up the time it takes to deliver an analysis of data. Use software to automatically collect information from different sources and put them in a timetable to keep the data you receive current.  2. Dashboards. Imagine what it will take to create an effective dashboard that monitors the company's KPIs. It is essential to gather the relevant data, then analyze it to extract metrics (with Excel, Python, R or whatever your preferred language is) and then display the results on a graph that decision-makers can view. The entire process from beginning to end is lengthy. Automating scripts to analyze the data or using tools like Looker or Metabase that automatically display data could reduce the time when creating dashboards.  3. Business intelligence. The process of establishing business intelligence excellence requires far more than just dashboards. It would help to look at various metrics and breakdowns for the various business units to gain new data. Are monthly orders rising
  • 2. within the EMEA region, however not so in APAC? Automate the process of creating the different breakdowns through an automated method of preparing data. Create cubes that combine data broken down according to other dimensions in your warehouse. For instance, the total amount of transactions (aggregation) by geographic location (dimension). Or the average order size (aggregation) by customer segment (dimension). You can then take a look at the cubes and gain conclusions from the automated analysis.  4. Models of machine learning and automation of big data. The experts in machine learning develop statistical models that surpass humans in a variety of tasks. For instance, a machine-learning model is far more adept in forecasting which ads will receive clicks than humans. However, building a predictive click model on its own won't cut it. Consumers' preferences are changing and what they buy on the internet is changing too. Unlike data scientists or machine-learning engineers creating models every couple of weeks, you can automate model creation and selection. With the help of automation, you can construct diverse models by choosing various parameters based on diverse data combinations. You can then automatically select which model has the highest predictive score for clicks and apply it to production. Automation isn't only for the development of models. Financial institutions and banks employ advanced anomaly detection algorithms that look for signs that could indicate fraudulent transactions. When the signal is above an amount, the models initiate an account audit through alerts sent to human inspections.  The possibilities for automated data analysis are limitless, and that's why you should take the initiative?  What are the advantages of automated data analysis?  There are four primary benefits of automating your analytics.  1. Increased/ speed of analytics. The time it takes from request to delivery of the analytic report is reduced when you automate (part of) the process for creating the analytical report.   2. Reduce time and costs. Automating (part or all of) these pipelines reduce scientists', analysts' and engineers' working hours. This also means that you have less to pay for rota-related tasks as computers take care of them.  3. Let your time be used to do creative work. Data experts don't need to work on tweaking pipelines manually, and they are free to solve the tough business issues and come up with innovative ways to generate revenue. 
  • 3. 4. Enhanced processes and systems. Conducting analytics by hand often requires complicated procedures. Who do your analysts have to speak to, making a note of all the requirements for clearing the database before analysis, as well as multiple coordination meetings to transfer the data across departments and staff. When you automatize analytics, you can skip those parts that are susceptible to human errors. When you spot a mistake in the automated processes, you just need to fix it only once. Automated processes help you create future-proof systems and processes.  What is the best time to automate the process of data analysis?  Some tasks in analytics are not perfect to be automated. Therefore, before you begin automating, ensure:  1. The proposed task is of significant significance. Automating the job can either address the business issue (delayed insights result in missed opportunities) and improve on the overall financial bottom of the line (the efficiency gains translate into actual cost savings) or even provide the possibility of business expansion (the automated analytics uncovers new revenue sources as well as cost reductions).  2. The task that is being considered for the candidate is not a one-time task. If you create the report once, then there is no reason to automate the process. It's often because of repetition that we learn what parts of the process are simpler to automate. For instance, making the identical KPI dashboards every three weeks in succession turns around the light bulb above our heads. The dashboards utilize similar data sources, and so automation of data extraction can aid in speeding up the reporting with help of business intelligence tools.  3. Automation can save time by decreasing the chance of errors. An automated system usually costs more but it is the best option as it will lessens the chance of errors that are caused due to operating manually. One can also automate data validation to detect typos, flag and impute missing values. This type of data analysis automation not only streamlines data modeling processes, and also enables adherence to models by automatically transforming data. 4. You're ready to improve continuously. The automated system cannot be perfect the first time. You must be prepared to continually enhance the efficiency of your data analytics system for success. There are two implications: (1) you must adopt a growth mindset in which you think about ways to improve the efficiency of your system. (2) you must create a set of criteria and monitoring to determine if the system is performing efficiently.  How do you automate the process of data analysis? 
  • 4. The way you implement data analytics will depend on the degree of automation you're taking into consideration:  1. Partial automation. Partial automation is a way to automate existing processes but eliminates some of the manual labor. For instance, your team of data analysts would create scripts that accelerate certain aspects of their work.  2. Final-to-last production. Automation is set up from end to end, and computers create information products for humans to examine and decide on. Automation, for instance, produces KPI dashboards or alerts about fraud without the employee handling anything.  3. Complete automation. Complete automation can make business decisions in near- real-time with no human involvement. For instance, an AI algorithm automatically determines if the information is sufficient to buy and sell the assets.  As you progress towards complete automation, the greater the benefit of automation increases from merely creating time savings to having independent effects on the company's bottom line.  How do you begin automatizing your data analysis?  1. Find analytical tasks that can be automated.  The ideal task that should be chosen for automation should pass the following checks:   a) It should have business value   b) Is repetitive   c) Time saving   d) Reduces errors   e) Can optimize the current process further  2. Set expectations through formalizing the guidelines to ensure success. In the beginning, automation is an opportunity to consolidate processes and reduce time. Be clear about what you're looking for. Begin with a small task, such as automating just one process of data pipeline.  3. Make use of dedicated instruments and platforms to accelerate automation. Your engineers can write the SQL procedures and Python scripts to automate codes, but using specific tools and platforms can save time in creating automatized pipelines. 
  • 5. 4. Repeat and analyze. As you automatize part of the processes for data analytics and products, you should evaluate them against the criteria for success that you have set before. If successful, automate more.  CONCLUSION -   Companies that deal with big data may benefit from automating a portion of their data analytics infrastructure. Data lakes are stuffed with unstructured data that machines can analyze faster than humans. In addition, today's data warehouses are characterized by strict requirements for data modeling and processing, which can easily be streamlined by automated data analytics.