Series 3: Uses of data exploration
Data exploration and its use
The very first step involved in the data analysis process, Data Exploration, involves going through or exploring a large set of structured data while trying to uncover the point of interests, characteristics and patterns.
Rather than revealing every bit of information featured in a data set, this process is aimed at creating a broad picture of key points and trends to study in detail. Data exploration uses a combination of automated tools and manual methods, including initial reports, visualizations and charts.
The technical architecture and design of a data lake include with Data Exploration is aided by ingesting all the enterprise data in Data Lake – and then using tools like Azure Synapse Analytics which allows ad hoc queries over federated data from various sources with Serverless SQL pool to over the data directly in data lake.
This exploration for instance can be done with Azure Databricks as well. Just as an overview, the Data Exploration Layer in Data Lake is one of the components as listed below:
Data Ingestion Layer
This layer is the first step for the data coming from variable sources into the Data Lake Store.
Ingestion is the process of bringing data into the Data Lake Store. Ingestion involves connecting to various data sources, extracting the data, detecting the changed data and tracking the data flow.
Data Storage Layer
This layer focuses on storing large amounts of data efficiently.
Data will be stored in its raw format until it is needed. It allows for data to be usedmany times for different analytic needs and use cases based on businessrequirements.
Data Processing Layer
In this primary layer, the focus is to specialize the data pipeline processing system to transform the data collected in the previous layer to be processed in this layer.
Data Exploration Layer
This is the layer where active analytic processing takes Place.
Here, the primary focus is to gather the data value so that they are made to be more helpful for the next layer.
Data Consumption Layer
This is the layer that allows various roles/personas like data scientists, data developers, and business analysts to access data with their choice of analytic tools and frameworks.
The data exploration layer or process can make deeper analysis much easier as it aids in targeting future research and starts the procedure of excluding irrelevant search paths and data points that may end up with no results.
It especially aids in building a familiarity with the existing information, which makes finding the needed insights a lot simpler.
Importance of Data Exploration
People process visual data way better than their numerical counterparts. Hence, it becomes extremely challenging for data analysts and scientists to assign meaning to thousands of columns and rows of data points so as to communicate its key meaning without any kind of visual components.
Data visualization, as a part of the data exploration process, tends to leverage familiar visual cues like colors, angles, points, lines and shapes.
So that the data analysts would be able to competently visualize and define metadata, and then go on to performing data cleansing, Choosing to perform the initial step of data exploration goes a long way in enabling data analysts to gain a better understanding of the data set, visualize anomalies, and grasp relationships that may go undetected otherwise.
Data Exploration in ML
An ML or Machine Learning project would be as good as the foundation of data on which it is built. To perform perfectly, ML data exploration models should ingest large amounts of data and perform the steps mentioned below:
Variable identification: Defining each variable and the role it has in the dataset
Bi-variable analysis – Determining the interaction between variables by developing visualization tools
Detecting and treating missing values
Detecting and treating outliers
The key goal of data exploration ML is to provide data insights meant to inspire the model-building process and feature engineering.
Feature engineering is meant to facilitate the whole ML process and boost the predictive power of ML algorithms by creating features from raw data.
Data Exploration in GIS
GIS or Geographic Information Systems is a framework meant for collecting and analyzing data that is connected to geographical locations, as well as their relation to natural or human activity on the planet.
Geospatial analysts have to deal with an increasing volume of geospatial data as a great chunk of the world & data is not location- enriched.
Incorporation of Spatio-temporal analysis into existing big data analytics workflows can be facilitated through advanced GIS software tools and solutions, thereby enabling data analysts to both create and share intuitive data visualizations that can provide assistance in data exploration. Categorizing and narrowing down raw data is vital for data analysts who have to deal with billions of mapped points.