Data Collection Without Complexity

What is a dataset? 

Datasets are essentially files containing collected records of information (data fields) that cover specific topics and are designed to answer relevant business questions or use cases. These files can be analyzed directly or serve as input to programs or algorithms to obtain specialized results or analysis. 

For example, an online fashion marketplace may want to optimize its product offerings based on industry trends and buyer preferences, and accordingly need to gather the following information:

• Best-selling items from leading online stores in each relevant product category
• Sales volume or inventory levels for key competing products
• Identifying successful sellers and stores on leading marketplaces for potential collaboration.
• Review analysis to track changing preferences.

Datasets can be cataloged so that they can be found and used without necessarily displaying the source website. Each dataset typically consists of millions of numerous 'data records', each with its own data fields related to a specific segment. For example, the presence of key social media influencers across various platforms. 'Data fields' refer to a specific category of data contained in a record, such as account name, number of followers, or average engagement rate per post. 

The ways to organize and access these datasets vary. Here are some of the most common methods:

• Full Datasets: These cover entire domains and include all data records, e.g., all companies in a specific industry segment.
• Smart Subsets: Here, various filters are applied to full datasets to answer a specific business question. For example, a venture capital firm may look for early-stage companies, focusing on people who founded companies in the last 3 years, have a strong tech base, company size of 5-25, and have not raised more than $2M in various funding rounds.
• Differential Datasets: These are datasets that are continuously collected and recalculated from data sources to detect changes and focus exclusively on the 'difference', i.e., parameters that have changed since the previous review. Examples include price changes, job postings, or newly added records.
• Data Merging/Enrichment: This involves combining two or more data sources into one dataset, e.g., cross-referencing datasets from different digital marketplaces.

Here are the three most popular datasets 

We have identified three types of datasets that are most popular, including: 

• E-commerce Websites: Companies in digital retail are currently most interested in acquiring full datasets from popular marketplaces, which help them map all competing products and sellers in their niche. They are also very interested in pre-collected datasets containing consumer reviews of these products and sellers. 
• Social Networks: Companies increasingly seek access to industry influencers and micro-influencers, as well as engagement data (e.g., views, likes, shares of specific content). Note that 'smart filtering' of influencers can be based on type, location, topic, follower count, and other parameters.
• Business and People Websites: Companies in finance, investment, and HR are interested in obtaining extensive information about companies from various directories and sites, as well as employee data. Each type of company may want to slice the data differently to get personalized insights and answers. 

What are the benefits of pre-collected datasets?

Let's examine the benefits of using pre-collected datasets from operational and budgetary efficiency perspectives:

• From an operational perspective, there is no proprietary infrastructure to build or maintain. No need to keep technical staff dedicated solely to data collection and cleaning. Finding and ingesting new data can be done extremely quickly (within minutes). And most importantly, datasets are already structured and ready to use in your preferred storage method (parsing JSON, CSV, or Excel).
• From a budgetary perspective, since datasets are collected in advance, they are a much more cost-effective option than active data collection or outsourcing. Additionally, they provide a high level of budget control and flexibility. For example, if you have a new project, client, or idea for which your team wants to prepare a Proof of Concept (PoC) proposal, your ability to scale (up/down) and diversify input data is limitless. 
• From a data perspective, datasets provide greater value and larger data volume, including through the process of data validation and enrichment. This is complemented by the use of 'smart filtering', allowing companies to answer specific queries that still rely on having the full domain of data as a baseline. Additionally, datasets are created based on an extensive 'discovery phase' of all relevant pages of the target domain, which is often critical. 

Choosing the Option That Meets Your Needs 

If you have decided that using a dataset is right for your company, you can choose one of three options:

Option One: Get an Enriched Snapshot of an Entire Website
In this case, you can focus on a specific website and access millions of pages that can be ingested into your systems. Since the snapshot was created through a full discovery process, it will include all relevant pages. For example, if your company aims to identify successful sellers or e-commerce stores, you can access a dataset of all sellers per marketplace and input that information into your systems. A nice aspect is the ability to update datasets later, keeping your tools current. 

Option Two: Get a Targeted Subset of Data 
This option allows targeted data collection, which can save time and money, especially if you know exactly what you need. This is done by defining the most suitable filters and parameters. For example, if you are a hedge fund looking for a specific industry segment, you might need a subset of data related to jobs, positions, companies, and people. 

Option Three: Get a Fully Customized Dataset 
If you have a very specific dataset or combination of data points you would like to access, and the two previous options cannot provide the necessary information, you can contact us directly and we will create a dataset tailored to your needs. For example, if you want to find specific types of doctors in Australia, recent court rulings in Texas, or all possible configurations of custom-made trucks, we can create such a dataset for you. 

Conclusion

Whatever your company's specific data needs, having access to datasets without having to collect them yourself has its advantages. This includes eliminating the need to build your own infrastructure, freeing up technical staff, allowing you to focus on product development, and providing instant custom solutions to new clients. Datasets can increase operational efficiency, giving you a competitive edge in your industry.