Importance of Data Collection in Artificial Intelligence

Why do we need datasets?

Artificial Intelligence(AI) depends heavily on data. Without data, an AI system
can’t learn. It is the most crucial aspect that makes algorithm training possible.
No matter how great your AI team is or the size of your data set, if your data
set is not good enough, your entire AI project will fail.
Machine learning algorithms require large amounts of data to work. When
managing millions or even billions of data samples, it’s truly difficult to
pinpoint what precisely causes a framework to perform seriously. Along these
lines, when aggregating your information, it’s insufficient to accumulate an
immense stream of data, feed it to your model, and anticipate great outcomes.
The cycle should be substantially more finely tuned.

Data assortment is a significant bottleneck in AI and a hot subject in different
networks. There are generally two reasons for the criticality of data collection.
To start with, as AI is turning out to be all the more generally utilized, we are
seeing new applications that do not really have enough tagged data. Second,
unlike conventional AI, deep learning techniques automatically generate
features, which saves feature engineering costs, but in return may require
larger amounts of labeled data. Interestingly, recent research in data collection

comes not only from the machine learning, natural language, and computer
vision communities but also from the data management community due to the
importance of handling large amounts of data. Data collection largely
consists of data acquisition, data labeling, and improvement of existing data or
models.

Data collection is the single most important step in solving any machine
learning problem. Teams that dive headfirst into projects without considering
the right data collection process often don’t get the results they want.
Fortunately, there are many data collection tools to help prepare training
datasets quickly and at scale.
The best data collection tools are easy to use, support a range of
functionalities and file types, and preserve the overall integrity of data. They
are:
• Synthetic Data Generators
Synthetic data can also be programmatically generated to obtain large sample
sizes of data. This data can then be used to train neural networks. There are a
variety of tools for generating synthetic datasets. Various Python libraries can
be used to generate a large synthetic database as specified by the user. Data
generator tools let users create custom CSV, SQL, JSON, and Excel datasets to
test and demo software.

• Data Augmentation Tools
Data Augmentation can be used to expand the size of an existing dataset
without gathering more data. For instance, an image dataset can be
augmented by rotating, cropping, or altering the lighting conditions in the
original files. OpenCV and Scikit python libraries include image augmentation
functions and features for bounding boxes, scaling, cropping, rotation, filters,
blur, translation, etc.

• Open-Source Datasets

Another way to obtain raw data for machine learning is to obtain pre-built,
publicly available datasets on the internet. There are thousands of publicly
available datasets spanning a wide range of industries and use cases.

• Data Collection Tools & Services
The majority of algorithms require data to be formatted in a very specific way.
As such, datasets usually require some amount of preparation before they can
yield useful insights. After you’ve collected enough raw data, you’ll still need to
preprocess it before it’s useful for training a model. There are a hundred of
Data Collection service providers around the world.

Quality, Scope, and Quantity
Machine Learning is not only about large data sets. Indeed, you don’t feed the
system with every known data point in any related field. We want to feed the
system with carefully curated data, hoping it can learn, and perhaps extend, at
the margins, the knowledge that people already have.

I have a data set, what now?
Not so fast! You should know that all data sets are inaccurate. At this moment
of the project, we need to do some data preparation, a very important step in
the machine learning process. Basically, data preparation is about making your
data set more suitable for machine learning. It is a set of procedures that
consume most of the time spent on machine learning projects. The collected
data needs to be processed as per the algorithm and application type before it
is used for the training.

Data Preprocessing
At this step, you have gathered your data that you judge essential, diverse, and
representative for your AI project. Preprocessing includes the selection of the
right data from the complete data set and building a training set. The process
of putting together the data in this optimal format is known as feature
transformation.

1. Format: The data might be spread in different files. For example, sales
   results from different countries with a different currency, languages, etc. which
   needs to be gathered together to form a data set.
2. Data Cleaning: In this step, our goal is to deal with missing values and
   remove unwanted characters from the data.
3. Feature Extraction: In this step, we focus on the analysis and
   optimization of the number of features. Usually, a member of the team has to
   find out which features are important for prediction and select them for faster
   computations and low memory consumption.