DATA CAPTURING TRAINING_FINAL.pptx

HOW TO EFFECTIVELY CAPTURE FIELD DATA TO BE
USED IN BUILDING INVENTORY SOLUTION

MODULE 1
Lesson1: Data Capturing
Lesson 2: Data Capturing Processes
MODULE 2
Lesson1: Methods of Capturing data
Lesson 2: Modern technologies and data
capturing
MODULE 3
Lesson1: Analyzing the captured data
Lesson 2: Preserving the captured data

MODULE 1 LESSON 1: DATA CAPTURING
Definition of data
Different sources of data
Importance of data and data capturing
Difference between data and information
Different types of data
Formats and representations of data
Structured and unstructured data

Definition of data
Data is a collection of facts, such as numbers,
words, measurements, observations or just
descriptions of things.
 Data can be qualitative or quantitative.
❖Qualitative data is descriptive information (it
describes something)
❖Quantitative data is numerical information
(numbers)

Definition of data - 2
Quantitative data can be Discrete or
Continuous:
Discrete data can only take certain values
(like whole numbers)
Continuous data can take any value (within a
range)
Put simply: Discrete data is counted,
Continuous data is measured

Definition of data - 3
Source: https://www.mathsisfun.com/data/data.html

Different Sources of data
Primary data
The data which is Raw, original, and extracted
directly from the official sources is known as primary
data. This type of data is collected directly by
performing techniques such as questionnaires,
interviews, and surveys.
❖Interview method
❖Survey method
❖Observation method
❖Experimental method

Different Sources of data - 2
Secondary data
Secondary data is the data which has already been
collected and reused again for some valid purpose.
❖Internally Sourced
❖Externally Sourced

Other Sources of data
Sensors data: With the advancement of IoT devices, the sensors
of these devices collect data which can be used for sensor data
analytics to track the performance and usage of products.
Satellites data: Satellites collect a lot of images and data in
terabytes on daily basis through surveillance cameras which can
be used to collect useful information.
Web traffic: Due to fast and cheap internet facilities many
formats of data which is uploaded by users on different platforms
can be predicted and collected with their permission for data
analysis.

IoT Data

Satellite Data
Himawari 8
GOES-R
Produces
144 images
Per day

Web Data
Scrapping
API

Importance of data and data capturing
Improve People’s Lives
Make Informed Decisions
Get The Results You Want
Find Solutions To Problems
Back Up Your Arguments
Stop The Guessing Game
Be Strategic In Your Approaches
Know What You Are Doing Well
Keep Track Of It All

Difference between data and information
Data is information collected Information is data processed
Data doesn’t depend on Information can’t exist
information. without data.
Data is raw and doesn’t
contain any meaning unless
analyzed.
Information is data collated
and produced to further a
logical meaning.
Data doesn’t serve any Data when interpreted and
assigned with some meaning
derived out of it, gives
information.
purpose unless given to.

Different types of Data
 Continuous Data: Continuous data is of the type that must be
measured as against the type that we can count.
 Discrete Data: Discrete data is the data that needs to be
counted as opposed to being measured.
 Binary: The data in such cases needs to be entered in one of
the two categories like true or false.
 Ordered Categories(Ordinal): The data in these cases needs to
be entered in one of the multiple categories that are ranked.
 Unordered Categories(Nominal): The data in these cases is
entered in one of the multiple categories that need not be
ranked.
 Count: This is simple counting of data without any categorization
involved.

Formats and representation of Data

Formats and representation of Data - 2
 The file format you choose can affect who you can share your
data with and whether or not your data will be useable in the
future. It is best to choose a format that is open and sustainable.
Formats likely to be accessible in the future are:
❖Non-proprietary
❖Open, with documented standards
❖In common usage by the research community
❖Using standard character encodings (i.e., ASCII, UTF-8)
❖Uncompressed (space permitting)

JSON
XML
YAML

Structured and Unstructured data

Structured and Unstructured data- 2

Structured and Unstructured data- 2
Examples of structured Examples of unstructured
data include data include
 names  text file
dates  video files
 addresses audio files
credit card numbers
stock information
 geolocation
and more.
mobile activity
social media posts
satellite imagery
surveillance imagery
the list goes on and on.

MODULE 1 LESSON 1: DATA CAPTURING
THE END

MODULE 1 LESSON 2: DATA CAPTURING PROCESS
Form design for data capturing
Different kinds of forms for data capturing
Basic data capturing tools
Naming conventions of captured data
Workflows associated with Data capturing
Data capturing checklist

Form design for data capturing
4 Major areas of Form design Categories
Structure
Text
Technical design
Data validation

Form design for data capturing - 2
 Text Design
 Structure Design
 Clear and concise labels: Brevity
is important on forms because
lengthy text looks more
 Structure everything vertically:
Every element of your form should
be structured vertically in a column.
complicated to users.
 Rely on one column: A single
column form reduces the chances
a user will miss an input field.
 Use action words for buttons:
“Sign-up,” “Make Payment,”
“Create Account,” etc
 Don’t split numbers: All entry fields  Use First-Person: “Create My
Account.”
should be one box.
 Separate Placeholder and Label
Text: For example, a placeholder
text will say, “Phone Number,”
instead of, “(xxx)xxx-xxxx.”
 Break up long forms: Break your
form into multiple parts so users can
easily fill out each section.

Form design for data capturing - 3
Technical Design Data Validation

 Validate Data Inline: An easy way
to solve this problem is to validate
each entry field as a user enters
their information.
 Autofill: you should use autofill
where possible.
 Keyboards: For example, if you are
asking for a credit card number,
users should get a numeric  Autocorrect Format Errors: When
possible, the form should
keyboard instead of the standard
QWERTY keyboard. autocorrect formatting issues
 Predictive Search: Predictive
search can be a huge time saver
when there are a lot of options
available.

Form design for data capturing – 4 (Example)

Different kinds of forms for Data capturing
 Opt-in forms are generally simple but  Contact forms generally serve as the
should clearly explain how a visitor’s main way for customers — or
email address, physical address, or prospective ones — to communicate
other contact information will be used. with you

Different kinds of forms for Data capturing - 2
 Any payment form
that you create should
be detailed so that
customers know what
they’re paying for,
how much they’ll be
charged, and what
options are available,
especially when it
comes to shipping
products or paying for
services.

 The job application
forms you create
should be detailed and
provide enough
information to
determine whether
someone has the right
qualifications and skills
for an open position.

 The questions in your
candidate screening
form screen should
allow you to determine
whether a candidate is
a good fit for a specific
team and the
company as a whole.

Basic data capturing tools
Survey Sparrow FastField
Fulcrum Zonka Feedback
Forms on Fire
GoSpotCheck
Zoho
Team scope
Kobo Toolbox
Magpi
PaperForm
JotForm.

Using Kobo Toolbox for data capturing
 Step 1: Signup for an account and Login into your account
 Step 2: Define Project
 Step 3 : Design form
 Step 4: Deploy your form
 Step 5: Share form and select required method
 Step 6: Analyze your data or Export your data

 Step 1: Signup for an account https://kf.kobotoolbox.org/accounts/login

Step 2: Define your project

Step 2: Define your project - 2

Step 2: Define your project - 3

Step 3: Design form

Step 3: Design form - 2

Step 4: Deploy your form

Step 5: Share form and select required method

Step 5: Share form and select required method - 2

 Step 6: Analyze your data or Export your data

 Step 6: Analyze your data or Export your data - 2

Naming conventions of captured data
 Descriptive file names are an important part of organizing, sharing,
and keeping track of data files. Develop a naming convention
based on elements that are important to the project.
 File naming best practices:
 Files should be named consistently
 File names should be short but descriptive (<25 characters) (Briney, 2015)
 Avoid special characters or spaces in a file name
 Use capitals and underscores instead of periods or spaces or slashes
 Use date format ISO 8601: YYYYMMDD
 Include a version number (Creamer et al. 2014)
 Write down naming convention in data management plan

Naming conventions of captured data - 2
 Elements to consider using in a naming convention are:
 Date of creation (putting the date in the front will facilitate
computer aided date sorting)
 Short Description
 Work
 Location
 Project name or number
 Sample
 Analysis
 Version number

Naming conventions of captured data - 3
 File structure
 Hierarchical file structures can add additional organization to your
files. As with file naming use whatever makes most sense for your
data. Some possibilities include:
 Project
 Date
 Analysis
 Location

Workflows associated with Data capturing

Data capturing checklist
 Step 1: Make the purpose clear.
 Step 2: Define the scope of your data collection.
 Step 3: Design your sample.
 Step 4: Develop your data collection instrument.
 Step 5: Flowchart the procedure of collecting the data.
 Step 6: Pilot test the whole thing.

MODULE 1 LESSON 2: DATA CAPTURING PROCESS
THE END

MODULE 2 LESSON 1: METHODS OF CAPTURING DATA
Manual methods of capturing data
Tools used for manual data capturing
Automated ways of capturing data
Different tools for automating data capturing
Advantages of each method
Disadvantages of each method

Manual methods of capturing data
 Manual Data Capture:
 This method uses manual keying of required data from written
forms into a computer for digitized access. It is suitable for
businesses where the volume of data is low and variable. Manual
data capture depends on human labor making it susceptible to
errors or data omissions, the very reason why automated data
capture technology is becoming an ideal solution.

Tools used for manual data capturing
 Paper form
 Biro
 Pencil
 Mouse
 Graphics tablet
 Keyboard
 Touch-screen – e.g. PDA
 Tracker ball

Automated ways of capturing data
 OCR (Optical Character Recognition): it provides the ability to recognize
machine produced characters as part a data capture and extraction
process.
 ICR (Intelligent Character Recognition): A scanned image of a
handwritten document is analyzed and recognized by sophisticated ICR
software.
 Barcode/ QR recognition: Dependent upon the type of barcode that is
used, the amount of metadata that can be included or marked up can
be high, as is the level of recognition.
 IDR (Intelligent Document Recognition): Intelligent document recognition
also interprets and indexes different documents based on the document
type, its meta data and elements of the document identified.
 Screen Scraping: Screen scraping is used by Robot Process Automation
and other tools to navigate, interact and capture raw data that appears
on a digital display, application or website.

Automated ways of capturing data - 2
 MICR (Magnetic Ink Character Recognition): This is a data capture
technology capable of recognizing characters machine printed in a
magnetic ink. It is mainly used in the bank industry for cheque processing.
 Swipe or Proximity cards: Magnetic swipe or proximity cards are used to
store data. Card readers capture this data to confirm identity and
control to access to a building or shared device.
 Intelligent Voice Capture: The boom in smart devices has also seen the
rise of voice controlled virtual assistants from the likes of Apple (Siri),
Google (Google Assistant), Amazon (Alexa) and Microsoft (Cortana).
 Intelligent image & video capture: Intelligent image and video data
capture involves real-time analysis of images and moving image data for
objects or “triggers” before executing a certain process.

Different tools for automating data capturing
 Artificial Intelligence Tools
 Web Forms
 QR code and Barcode scanners
 OCR Software
 ICR Software
 IDR Software
 OMR scanners

Advantages of automated data capture
 Automated Data Capture Methods Supersede Manual Data Entry: A study
on the quality of manual data entry found that participants who did
visual checking made 2,958% more errors than those who performed
double entry.
 Automated Data Capture Software Can Optimize Workflows: Automated
data capture is one of the most effective ways to streamline workflows.
 Automation Simplifies Data Capture Management: Workflow software
that supports automated data capture can also simplify data processing
and management.
 Field Data Capture Software Supports Staff: Field staff may no longer
need to take readings directly from sensors or equipment that are not
readily accessible.
 Real Time Data Capture and Management: Automated data capture
with online collection also offers enterprises the advantage of real-time
reporting.

Disadvantages of automated data capture
 Expensive to implement
 Challenging if poorly implemented
 Regular upgrades and updates will be required
 If you choose the wrong distribution channels, you might end up with
little data or really biased data
 Participants might be less engaged in filling a survey out online than if it
were done in person
 Repeated requests to take a survey or questionnaire can become
irritating to individuals and could actually damage your brand
 It’s harder to verify identification. Therefore someone could have a friend
fill out the survey for them or perhaps one person could submit multiple
surveys
 You might have difficulty reaching certain groups if they have limited or
no access to the internet, though this is rarer in today’s digital world

MODULE 2 LESSON 1: METHODS OF CAPTURING DATA
THE END

MODULE 2 LESSON 2: MODERN TECHNOLOGIES FOR DATA
CAPTURING
Mobile devices and data capturing
AI and data capturing
Web scrapping
GPS coordinates and data capturing

Mobile devices and data capturing
What is mobile data capture?
Mobile data capture is the method of gathering different types of
information using mobile devices such as smartphones, tablets and
other handheld tools. Though data capture is nothing new, the
introduction of mobile devices means this is now more flexible and
efficient.
Data input or captured into phones may be transmitted or shared
in many ways (including SMS, MMS, USSD, Bluetooth, wireless
Internet, or the exchange of physical memory cards). Where
mobile connectivity is not available, data can be stored on the
phone and transmitted later once a phone is within sufficient range
of a cell tower.

Mobile devices and data capturing- 2
Advantages
Speed
Accuracy
Ubiquity, familiarity and convenience
Training
Low power
Combining with other data
Low cost

Mobile devices and data capturing- 3
Issues and Challenges
Technology: What technology should we use? What are the
minimally viable specifications required for the devices used in
mobile data collection efforts?
Training: in some circumstances additional technology-related
training and support may still be required.
Cost: The costs of designing survey instruments delivered digitally
may be considerably higher when constructing traditional paper-
based questionnaires.
Data security: Digital collection and transmission of data as part of
large scale survey efforts carries with it numerous potential risks and
challenges related to data security and privacy

AI and data capturing
Artificial Intelligence is ultimately an umbrella terms for different
artificial intelligence techniques. Best viewed in context of the
use case and application.
Computer vision Image or pattern recognition to improve the
recognition of any type image.
Neural Networks & Machine learning to assist with accurate
recognition training based on large data sets and assisted
learning.
Natural Language Processing for interpreting sentences and their
meaning.
Cognitive computing
Knowledge Mining
Anomaly detection

AI and data capturing - 2
Computer vision
A field of artificial intelligence in which programs attempt to
identify objects represented in digitized images provided by
cameras, thus enabling computers to “see.

Natural Language Processing (NLP)
Natural Language Processing (NLP) refers to artificial
intelligence method of communicating with an intelligent
systems using a natural language such as English.

Cognitive computing (CC)
Cognitive computing is a self-learning system that uses Machine
Learning and Data Mining algorithms, Neural Networks, and
Visual Recognition to perform human-like tasks intelligently.

Web scrapping
Web scraping, web harvesting, or web data extraction is data
scraping used for extracting data from websites. Web scraping
software may directly access the World Wide Web using the
Hypertext Transfer Protocol or a web browser.

Web scrapping – 2 (using Python Script)

Web scrapping -3 (Tools)
ParseHub
Scrapy
OctoParse
Scraper API
Mozenda
Webhose.io
Content Grabber
Common Crawl

GPS coordinates and data capturing
GPS Units
You likely use some form of GPS in your daily life, but do you
actually know what it is or how it works?
A GPS unit is any device capable of receiving information
from GPS satellites and calculating your geographical
position.
The Global Positioning System (GPS) is a network of about 30
satellites orbiting the Earth at an altitude of 20,000 km. The
system was originally developed by the US government for
military navigation but now anyone with a GPS device can
receive the radio signals that the satellites broadcast.

GPS coordinates and data capturing - 2

MODULE 2 LESSON 2: MODERN TECHNOLOGIES FOR DATA
CAPTURING
THE END

MODULE 3 LESSON 1: ANALYZING THE CAPTURED DATA
Basic aggregate functions for analyzing data
Different kinds of analysis
Decision making based on data
Presenting your data
Formats for presenting your data

Basic aggregate functions for analyzing data
An aggregate function returns one value after
calculating multiple values of a column.
Various types of aggregate functions are:
Count()
Sum()
Avg()
Min()
Max()
Product()

Basic aggregate functions for analyzing data -2

Charts for Descriptive analysis

Charts for Diagnostic analysis

Charts for Predictive analysis

Understanding Prescriptive analysis

Decision making based on data
What is data-driven decision-making?
Data-driven decision-making (DDDM) is defined as using facts,
metrics, and data to guide strategic business decisions that align
with your goals, objectives, and initiatives.
Advantages
You’ll Make More Confident Decisions
You’ll Become More Proactive
You Can Realize Cost Savings

Presenting your data
Make sure your data can be seen no matter the device
Focus most on the points your data illustrates
 Share one — and only one — major point from each chart
Label chart components clearly
Visually highlight “Aha!” zones
Write a slide title that reinforces the data’s point
Present to your audience, not to your data
Save 3D for the movies
Choose the appropriate chart
Don’t mix chart types for no reason
Use color with intention

Presenting your data - 2
What is a Report?
Reports can be a presentation of corresponding charts and
other visualizations, or they can be a large set of charts and
visualizations that may or may not directly relate. A report is
meant to be used to gather detailed intelligence on the
operations within an organization.
What is a Dashboard?
All dashboards should revolve around answering a central
question. For example a Chief Executive might simply want to
know, at any given time, in up to the minute detail, “How is the
business doing?”

Presenting your data – 3 (Dashboard Vs Report)
 A dashboard is a visualization tool
that contains the most important  Reports can cover issues of any
scope and can be used for data
information on a topic.
that does not necessarily have to be
 A dashboard is used as a tool to
related to business performance.
monitor the performance of an area
of the company.  Corporate reports usually have
several screens or pages with graphs
 Dashboards always contain metrics,
and tables to represent the
performance indicators and KPIs.
information.
 In a dashboard, the big picture is
 Reports provide an overview of the
more important than the detail.
reality being explored through
detailed and well-arranged
information.


Presenting your data – 4 (Dashboard)

Presenting your data – 5 (Report)

Formats for presenting your data
#2 Text
#1 Tabular Data
Write your findings in paragraphs
and bullets.
Tabular data is data presented in
rows and columns.
❖ 65% of email users worldwide
access their email via a mobile
device.
❖ Emails that are optimized for
mobile generate 15% higher click-
through rates.
❖ 56% of brands using emojis in their
email subject lines had a higher
open rate.

Formats for presenting your data - 2
#3 Pie or Donut #4 Bar Chart
Their heights or lengths depict the
values they represent.
If you’re using it to show percentages,
make sure all the slices add up to 100%..

#6 Line graph
#5 Histogram
Line graphs are represented by a group of
data points joined together by a straight line
Histogram only measures things that
can be put into numbers.

#8 Scatter Plot
#7 Heat map
A scatter plot is a grid with several inputs
showing the relationship between two variables.
A heat map represents data density in
colours. The bigger the number, the more
colour intense that data will be represented.

MODULE 3 LESSON 1: ANALYZING THE CAPTURED DATA
THE END

MODULE 3 LESSON 2: PRESERVING THE CAPTURED DATA
How to preserve data and information
Preserving data based on its format
Places that data can be stored
Preserving Integrity of data
Summary and Conclusion

How to preserve data and information
Make a detailed plan for the stewardship and preservation of
your data, from its inception to the end of its lifetime.
Be aware of data costs including hardware, software, support
and time, and include them in your overall IT budget.
Associate metadata with your data.
Make multiple copies of valuable data. Store some copies off-
site and in different systems.
Plan ahead of time for the transition of digital data to new
storage media.
Plan for transitions in data stewardship. If the data eventually will
be turned over to a formal repository, institution or other
custodial environment.

Places that data can be stored
Hard Drive Disks
Floppy Disks
Tapes
Compact Discs (CDs)
DVD and Blu-ray Discs
USB Flash Drives
Secure Digital Cards (SD Card)s
Solid-State Drives (SSDs)
Cloud Storage
Punch Cards

MODULE 3 LESSON 2: PRESERVING THE CAPTURED DATA
THE END

THANK YOU FOR YOUR TIME
THE END OF THE TRAINING

DATA CAPTURING TRAINING_FINAL.pptx

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