Search here

Can't find what you are looking for?

Feel free to get in touch with us for more information about our products and services.

Enabling Market Expansion: Data Refinement at Grepsr

Grepsr’s creative approach to data refinement helps brands expand into new markets

market expansion

Any data is only as good as the insights derived from it. However, before we begin the analysis, the data must be put through adequate pre-processing techniques that standardize, aggregate, and categorize the dataset.

As we’ve mentioned in our previous article that explores the importance of data refinement, most data scientists spend 50 to 80 percent of their time refining data.

At Grepsr, we have several QA checks to guarantee the speedy refinement of your data. The two use cases described in this article should give you enough insights into our data refinement procedures.

We’ve helped numerous brands expand into new territory by providing them with up-to-date data in real time.

Since enlarging your operations requires serious consideration on several fronts, i.e. geographic feasibility, data pertaining to specific criteria, competitor’s standing, etc., the data you work with demands rigorous scrutiny.

Our strong QA checks enable our clients to make the best decisions. The processes listed here ensure high-quality data for downstream analysis.

Case I

Client requirements: Extract any and all CF Moto dealership data, exclusive to France.

Field requirements:

Product name, Dealer name, Address, City, Geographic details (latitude and longitude)   

Overview: The brief was pretty straightforward. We needed to extract a particular dataset from a specific geographic location, i.e. from a single country. When scraping data on such a massive scale, it is natural to come across some undesirable elements. This is an account of how we rectified that problem.

Phase I : Data extraction

Our developers get to work and scrape the required dataset. The following sample shows the result of that effort.

sample data
Sample dataset: After the first round of extraction

As you can see, several data points in this dataset have values that sit at loggerheads with the client requirements. Then it brings us to the next phase.

Data to make or break your business
Get high-priority web data for your business, when you want it.

Phase II: Data issue detection

Once all runs are complete and the data is ready to be reviewed, our QA team gets to work and begins analyzing the dataset to discover contaminations. Depending upon the nature of client requirements, we use various data analytics and visualization tools to identify and correct data issues.

In this case, the QA team was able to find the most glaring data inconsistencies by visualizing the dataset. Clearly, we had several pieces of information from outside the vicinity of France, which we didn’t need.

Revelation of faulty data after visualization
Revelation of faulty data after visualization

Insights gained: The data is not fit enough to be delivered, yet. The QA team sends it back for reinspection.

Phase III: Data refinement

Now, fully aware of the problem, the developers set out to refine the data. Armed with insights obtained from the previous exercise, the Delivery team generates another dataset.

sample data
Sample dataset: After the second round of extraction

Phase IV: Data issue detection

The QA team verifies whether the data extracted in the second round has the same issues as before. We could have used a variety of methods to determine the verdict, but for this, we used data visualization once again.

Data visualization of a fully refined dataset
Data visualization of a fully refined dataset

It is easy to see that the dataset no longer consists of CF Moto dealerships outside of France.

Phase V: Data Delivery

The map indicates the high quality of the dataset. Afterward, the Customer Success team sends it over to the client for immediate deployment.

Case II

Client requirements: Extract any and all Yamaha motors dealership data from the UK.

Field requirements:

Product name, Dealer name, Address, City, Country, Email address, Website details 

Overview: Similar to Case I, the client needed dealership data of a particular brand from the UK. As before, we witnessed problems arise after the first round of extraction. This time around, our approach to data refinement was slightly different.

Phase I: Data Extraction

Our developers set out to scrape data.

Sample dataset: After the first round of extraction
Sample dataset: After the first round of extraction

Phase II: Data issue detection

The QA team discovered a lot of issues with this dataset. For this use case, we used the dataprep library in Python for analytics. The generated report provides clear insights into the issues of the dataset.

Issues afflicting the dataset
There are a number of issues afflicting the dataset after the first round of data extraction.

As you can see, the report shows the discrepancy between the number of rows and columns, missing cells and values, as well as the percentage of distortion.

For more in depth analysis, you can also see a visual summary of each variable.

Visual summary of each variable
Visual summary of each variable

A detailed section for each variable category allows you to view the overall statistics of that particular category.

Overall stats of variable
Overall stats of the variable

The QA team located several discrepancies in the dataset. Then, it’s sent to the Delivery team for reinspection. The following bar graph gives them a visual representation of missing values for each variable.

Missing variables in dataset
Missing variables in the dataset

Insights gained: Compared to the issues of Case I, we discovered more problems in the dataset after the first round of extraction. There were a lot of missing values like dealer name, email, and website details.

Phase III: Data refinement

With a clear roadmap laid before the Delivery team, there was nothing left to do but make the necessary changes. Then, the Delivery team sent another dataset to the QA team after the second round of data extraction. Thankfully, there were no errors this time. The following report fully substantiated the accuracy of our data.

Final report before delivery
Final report before delivery

Phase IV: Data Delivery

The Customer Success team now sends the data to the client. Finally, it can be deployed immediately.

To conclude

Most of our clients have one thing in common. Their data requirements are all unique. Naturally, we employ different QA checks to guarantee the integrity of their data.

We perform manual and automated QA processes to ensure the quality of your data. Furthermore, our robust data platform supports multiple data formats and delivery destinations. Seamless integration with popular platforms like Amazon S3, Google Cloud, Azure, etc., is a non-issue.

While data refinement demands a lot of manual work, as long as it delivers high-quality data, nonetheless, we do it for you.

Related reads:

Web data made accessible. At scale.
Tell us what you need. Let us ease your data sourcing pains!

A collection of articles, announcements and updates from Grepsr


The Application of Web Scraping in Data Visualization

Imagine you’re a business analyst tasked with understanding current trends in the sneaker market. You could spend hours combing through blogs and news articles trying to figure it out. However, that data would be scattered and difficult to analyze.  A potential solution is web scraping. It acts like a digital shovel, extracting valuable data from […]

Mastering Data Visualization in Python with Grepsr’s Data

In a world where data reigns supreme, the ability to make sense of the overwhelming volume of information is nothing short of a superpower. Harnessing the power of data visualization in Python is a superpower in itself. From interactive charts and graphs to immersive dashboards, visualization helps businesses and individuals gain insights from data.  But […]

data refinement

Why Data Refinement is Important for Your Business

Did you know most analysts spend 50 to 80 percent of their time refining their data than any other function in the data lifecycle? Even when we include other steps like data extraction, data analysis, and data visualization? We’ve talked at length about the importance of data for your business. The only thing we’ve emphasized […]

data analysis

Business Data Analytics — Why Enterprises Need It

Objectivity vs subjectivity The stories we hear as children have a way of mirroring the realities of everyday existence, unlike many things we experience as adults. An old folk tale from India is one of those stories. It goes something like this: A group of blind men goes to an elephant to find out its […]

data visualization

Data Visualization Is Critical to Your Business — Here Are 5 Reasons Why

Data visualization is a powerful tool. When done correctly, it is a much more elegant method of explaining even complex concepts compared to lengthy texts and paragraphs. Maps and graphs have existed since the 17th century as a means of visualizing data. It was in the mid-1800s that the world saw one the first examples […]

Why Data Visualization Matters to Your Business

There are several reasons why we believe that visual representation of data is becoming an integral part of Big Data analytics or any other kind of data-driven analytics, for that matter

covid test centers

Insights into Covid-19 Test Locations in the USA

An analysis of coronavirus testing facilities — geographic distribution, types, franchises, and testing and turnaround times

Big Data is Redefining News & Journalism

If digital data were something physical, it would have massively altered the shape of our world, probably, with new data mountains rising every hour. Whether you browse the web or flip pages of print media, you are sure to stumble upon some news about big data, all the while feeding the web with your digital […]