Data exploration is the very first step in the data analysis process. It all begins with exploring a large set of unstructured data while looking for patterns, characteristics, or points of interest. Summarizing the size, accuracy and initial patterns in the data is key to enabling deeper analysis. The purpose of this process is meant to help create a broader picture of potential trends or points to look for upon further analysis to refine the data.
What is Data Exploration and Why is it Important?
Data exploration uses both manual data analysis (often considered one of the most tedious and time consuming tasks in data science) and automated tools that extract data into initial reports that include data visualizations and charts. This process enables deeper data analysis as patterns and trends are identified. Data exploration helps create a more straightforward view of datasets rather than pouring over thousands of figures in unstructured data.
When it comes to data exploration, the most essential steps are Variable Identification, Univariate Analysis and Bi-Variate Analysis (read more about those steps here) or a tool can be used to begin acquiring knowledge of your dataset.
It sounds like a lot of number crunching right? While it is a tedious preliminary step to processing data where you begin to actually interesting insights, it is a necessary evil. Why?
By skipping this first exploratory step, data scientists are not be able to immediately understand key issues in the data or be able to guide deeper analysis in the right direction. Understanding and interpreting data from large data sets can be very challenging. It is difficult to understand the data set and make conclusions without looking through the entire data set. Yes, that might actually mean spending more time exploring the sample to get a better representation of the data set.
Using different data exploratory data analysis methods and visualization techniques will ensure you have a richer understanding of your data. Once data exploration has uncovered connections within the data, and then are formed into different variables, it is much easier to prepare the data into charts or visualizations.
What is the Use of Exploratory Data Analysis?
Data exploration can help cut down your massive data set to a manageable size where you can focus your efforts on analyzing the most relevant data. It is both an art and a science. There is the science of digging into and processing the data. The art is knowing where to look and collaborating to find the best answers to the biggest questions lying in the data.
To get the most of data exploration, it is important to take the time to acquire a greater volume of data that gives you a greater variety of options. You’re looking for the best answer lying in the data.
Traditionally data scientists conducted data exploration via manual methods which included filtering and drilling down unstructured data into spreadsheets to analyze the raw data in hopes to answer potential questions about a business issue.
Now there are automated tools that prepare extracted data by exploring, assessing, and refining the data quickly. Import.io’s Web Data Integration, for example, treats the entire web data lifecycle as a single, integrated process. It’s a simpler and easier way to view the most relevant features of a dataset.
Explore how Import.io’s Web Data Integration has provided value to 3 different industries. With this solution, you can take data from the world’s biggest repository and reliably leverage it to drive improved business outcomes. With Import.io, you can convert human readable web data to machine-ready intelligence, so you can gain maximum insights from the web’s alternative data sets.
Retail and Ecommerce
To meet the growth and revenue challenges within the Retail and Ecommerce industries, retailers must deliver increasingly optimized, customer-centric services. Ultimately, only the most data driven retailers will be able to stay successful in their industry. More than ever, due to the explosion of smartphones and social media, web data is needed to give retailers the extra intelligence to outperform competitors and stay on top of dynamic markets. Import.io’s web data integration gives retailers insights into:
- Tracking and Automating Competitive Price Monitoring
- Minimum Advertised Price (MAP) Compliance Monitoring
- Intelligent Product Matching
- Capturing Images and Descriptions for Online Marketplaces
- Monitoring Customer Sentiment
Finance and Equity
Market data within the Finance and Equity industries are very spread out. However, market data is very important for the success of business within the industry. With Import.io’s web data integration solution, it is easier than ever to make data driven decisions that elevate your business. Some of the benefits of Import.io’s web data integration within the financial, insurance and equity research industry include:
- Accessing Alternative Data
- Aggregating News Articles
- Harnessing Dispersed Market Data
- Extracting Data From Financial Statements
- Optimizing Insurance Models
Conclusion
Successfully exploring the dataset will ensure organizations won’t be missing out on opportunities to leverage web data and won’t get left behind because of incomplete data access, poor data quality, unreliable or out of date data, high costs, or uncertain business risks. Web Data Integration can help deliver that value and bring that data to life.
A lot of hard work goes into extracting and transforming data into a usable format, but once it’s done, data analytics can provide users with greater insights into their customers, business, and industry.
If you’re ready to try Web Data Integration, contact a data expert to see how we can do the heavy lifting for you.
