It’s common for businesses to have large amounts of unused data but struggle to organize, explore, and leverage it to make strategic decisions. Data can be powerful—some of the most successful and well-known companies today only made it to where they are because they followed their data. In today's highly competitive market having every advantage possible is a must, meaning the better the data, the better decisions businesses can make, and the more competitive they become.
In this blog, we will dive deep into understanding data exploration and how businesses can explore and leverage their data to make data-driven decisions.
What Is Data Exploration?
Data exploration is the first step in the data analysis process. Data analysts leverage data visualizations to identify patterns and relationships within a dataset such as numerical values, accuracy, quantity, size, and much more. These characteristics enable us to get a deeper understanding of the data. In order to explore data and begin driving insights, businesses first need to implement the right BI (business intelligence) tool. A BI tool enables companies to analyze data by transforming data into actionable insights to improve decision-making.
Why Is Data Exploration Important?
Because humans are visual learners, we can quickly learn much from visualizations. We can spot areas of interest within a chart and begin digging for more details and insights. This process often gives us a broad view of the data, making it easier to ask the right questions. With a user-friendly interface, anyone within an organization can quickly familiarize themselves with the data and self-serve by answering their own questions without relying on or waiting for more technical users to give them insights.
Data Exploration Use Cases
One of the most common use cases for data exploration is to help businesses explore and analyze massive amounts of enterprise data for further details. Data exploration can help organizations develop winning strategies and create efficient workflows to reach target goals that drive the business forward. Data visualization enables users to examine data at a high level, clarifying which data is necessary and which can be removed. Data exploration can help users in conducting data analysis by finding better paths or starting points and reducing the time it takes to get to insights.
Data Exploration vs Data Discovery
Data discovery starts once data exploration has prepared and organized the data into visualizations or the preferred viewing experience. Data discovery enables business users to drill through datasets and visualizations to answer specific business questions from the data. The goal is to identify patterns, trends, and unique data points that can be used to make data-driven decisions that will ultimately provide an edge over the competition and move the business forward.
One thing to note is that not all business intelligence platforms are built equally. Many lack the horsepower required for cloud modern businesses. BI tools not built for the modern data stack struggle to provide value due to their limited scalability, speed, poor user interface, and complex data modeling requirements. Making it difficult for business users to answer their own questions. Check out how Sigma enables data exploration and discovery with near-unlimited scale and speed to help your business users drive insights like never before.
Data Examination vs Data Exploration
Data examination and data exploration are very similar. Data examination helps users understand the data's state, consistency, and quality for future analysis.
Benefits of Data Exploration
Big-Picture View of Many Data Streams
Data streams are large volumes of continuous data that come in different forms. For example, a data stream can be transactional, sensor, image, or web traffic data, and many more. Having a big-picture of all the different data streams can help your organization identify inefficiencies in your business, reduce risk, and streamline operations.
Get an overall understanding of your business through different datasets and visualizations created from raw data. Humans can easily identify visual patterns which makes data visualization perfect for finding unique patterns which can be used to answer business questions or for further investigation.
Data exploration can help empower teams across your organization by providing them with the data they need. Data democratization within an organization ultimately helps to drive data-informed decisions by enabling everyone regardless of their technical level to access and work with data easily. If your internal teams are collaborating on the same datasets, they will be able to answer difficult questions that will impact the business. Data democratization is an ongoing process which is why many organizations are making it a part of their culture.
Improved Risk Management
With a cloud-based business intelligence platform, your data is much more secure and located in one location, your CDW (cloud data warehouse). With no need to extract or download data, you are less at risk of losing or having the data fall into the wrong hands. Some BI tools allow you to customize the access per user so only the data they need can be accessed, reducing the chance or human error or unauthorized data. Having governance over your data is key for risk management.
Real-time decisions can be made with the right data stack and BI platform. Real-time data can give your business a massive advantage over your competitors and enable your teams to deliver business decisions instantly using the most recent data.
What are The 5 Steps of the Data Exploration Process?
Step 1: Ask The Right Questions
What are you trying to learn from your data?
It's critical to understand what you're trying to learn from your data. It can be the starting point for a great data strategy. Find out your strengths and weaknesses, empower your users and drive the business forward by asking the right questions and solving them with the help of the data.
What goals are you trying to achieve?
With the right data, you can begin to address problems and inefficiencies within your business. It's important to define your goals and create a roadmap of how you will achieve them. By aligning your organization with a roadmap, everyone will be on the same page and have a better understanding of how they can add value with their skills.
What is the problem?
Of course, understanding the problem(s) your business faces is the most important question you need to know. Many businesses want to use data without fully understanding the problems they are trying to solve. Having a BI tool is great, but without the right questions, there will be no starting point.
Data exploration starts with asking the right questions
Data exploration is a great way to gain a deeper understanding of your overall business. Most successful modern companies leverage data in their day-to-day operations. These companies understand that good data is rarely wrong and leveraging it can quickly get you ahead of the competition.
Step 2: Data Collection
Identifying sources of data
Data comes in many different forms and from many sources. Data collection is critical and used for business decision-making. Customer, sales, marketing, and transactional data are just a few of the many data points that can be collected and analyzed with a BI tool to make data-driven decisions that help drive the business forward.
Structured, semi-structured, and unstructured data
- Typically, structured data is highly organized and comes within a spreadsheet or tabular format.
- Semi-structured data is partially organized and can come in the form of TXT, JSON, or HTML files.
- Unstructured data has no organizational form or format. Can come in the form of images, videos, sound files, PDF files, etc…
Use data that is relevant to the asked questions
It's easy to get lost in the large amounts of data flowing into your organization, which is why it's crucial to pinpoint the data that is most relevant to helping you answer your business questions and ultimately meet your goals.
Step 3: Data Cleaning
Data cleaning is the process of correcting or removing inaccurate or incomplete data from a record set. The data cleaning process can look different for each company, but the main purpose is to remove data that does not belong in your dataset.
Can be done manually or with automated scripts
Data cleaning can be done in several different ways. For example, removing duplicate data, fixing errors such as mislabeling, identifying and filtering outliers, etc. These can be done manually by a data analyst or can be automated with the right tool or data modeling language.
An important step in order to validate data
The most important step in data cleaning is to validate and test the data. Making sure the data is meeting your standards and can be used to answer business questions. The last thing you want for your business is inaccurate data that can lead to false conclusions, slowing down your teams and causing confusion.
Step 4: Exploratory Analysis
The most valuable insights come from asking questions of the data and asking follow-up questions until you're able to find a solution and ultimately make a decision backed by hard data. Exploratory analysis enables us to dive deep into large datasets and investigate findings using data visualization techniques.
Look for trends, patterns, and gaps
Exploratory analysis enables organizations to get a full picture of their data to quickly identify patterns, outliers, anomalies, and relationships to investigate and answer questions based on the data.
Analyze the data
The discovered data can then be further analyzed by business users or more technical data teams to make business decisions.
Step 5: Visualize Your Data
Use visuals to present your data so it’s easy to understand
The right visualizations can bring everyone to the same page. Humans are visual learners. By creating visualizations with data, we can easily spot trends and unique patterns. Even more importantly, presenting data in a visualization is a great way to reach non-technical viewers without confusing them.
Use visualization to tell a story
Data tells us a story and can bring several advantages to your business. Visualizations make it easy to share information, especially in the form of dashboards. Most people can look at a dashboard and have some understanding of the data in front of them. Being able to visualize data in a dashboard is great however, interacting with and exploring this data is truly groundbreaking.
With the right business intelligence tool, you can dig into the underlying data on a dashboard and answer more difficult questions. Organizations want to empower their employees to be able to use data in their day-to-day to make the best decisions. With data visualization this becomes possible.
Provide content for your findings
Once you start looking at the data and drawing insights, you can begin to understand where your business was lacking and where it was excelling. These findings can be very helpful in creating content moving forward and assist you in delivering high-quality and impactful content to your partners, customers, and audiences.
Sigma For Data Exploration
Connect to your cloud data warehouse in minutes and start your data exploration journey without any complicated or technical requirements. Sigma lets you work with data the way you and your team want. No longer do business users have to wait days or weeks for data analysts or engineers to create dashboards or reports. Anyone can use Sigma to explore, calculate, model, and generate insights without the need to write any code. If you are familiar with spreadsheets, then you will be familiar with Sigma’s spreadsheet interface which enables teams to analyze multi-billion row datasets quickly and easily. Run your business at the speed of your data with Sigma.
Let's explore together! Schedule a demo today.