Retrieving ‘Datasets’: The stuff that unicorns are made of!
In this article we will discuss:
- What are Datasets?
- Why purchasing Datasets are the key to explosive growth
- How Datasets are currently being used by companies?
What are Datasets?
Datasets by definition are bundles of information obtained from an open-source site or multiple sites by a third-party which enriches and formalizes the data, and is then delivered in a ready-to-use format.
Here are a few examples of the different types of datasets one can choose to order:
- Entire Datasets – All the information in the dataset, from company data directories to eCommerce marketplaces, with data points such as brands, sellers, reviews, market shares, as well as bestsellers and pricing trends.
- Data Subset – A specific portion of the data derived and extracted from the full dataset. This can be as simple as all companies from the UK or more complex ones like identifying all the people who have worked in the application optimization industry for the past 3 years and have a technology title and a university degree.
- Enriched Datasets – An additional layer of information on top of what was collected from the original target website, either by cross-referencing data points from another data source or by processing existing information to identify and create extra fields and parameters. For example, identifying the main areas of interest for a certain social media influencer or by matching product details across different eCommerce platforms
- Differential datasets – This is when the same data points are collected periodically for monitoring changes and trends on a daily, weekly, or monthly basis. For example, the change in the number of professionals in the employ of startups in the medical industry as displayed on the companies’ official pages/profiles or changes in bestseller product trends for specific categories on an eCommerce site or a given brand’s market share
Why purchasing Datasets are the key to explosive growth
Datasets are key to helping businesses grow quickly because they help answer business questions that can be very difficult to solve without the necessary data. They also remove a huge weight from the ‘horse’s saddle’ (i.e., data collection teams and infrastructure), enabling them to go from a casual trot to an eager gallop.
But how does this dynamic work?!,
Your inner voice might be shouting as you read these lines.
ONE: The power of collaboration
Many corporates, teams, and companies believe that they need to be lone wolves if they are to succeed in their business or industry. More often than not, the opposite is true, especially in the field of data collection. Building high-quality datasets on your own takes a lot of time, resources, and energy. But, you get quicker, more effective results when working with a data provider who can offer an affordable, well-structured, and updated dataset. In this case, the cost of data collection, maintaining, and enriching will be split among several customers. In addition, the data provider leverages its previous data expertise for the benefit of all involved parties.
So, for example, if you are interested in a dataset that includes customer reviews or social media sentiment for cell phone brands sold in the U.K., even if others are interested in different products or regions, the same mechanism is used to build these different datasets. Thus, your ability to access hard-to-reach data and scale collection efforts grows tenfold. Additionally, your ability to perform high-quality data maintenance (i.e., ensuring that important information is updated regularly) is boosted and shared among all dataset participants.
TWO: Standing on the shoulder of giants
Many entrepreneurs believe that they need to create everything from scratch. But some of the most successful people have built their empires on already pre-existing foundations. Leveraging the expertise and value of those whose business directly pertains to collecting, enriching, manipulating, and packaging data in an easy-to-analyze structure, allows fast leaps and insights that would otherwise require a considerable amount of internal resources and time.
Isaac Newton famously once said, “If I have seen further, it is by standing on the shoulders’ of Giants.” This logic dictated that ‘inventing the wheel is not necessary when the current one works just fine, and can serve as the basis for the further development of an autonomous vehicle’, for example.
In the context of data collection, many companies waste huge amounts of valuable DevOps, IT, and engineering hours on building the necessary infrastructure, discovery, as well as collecting the target data itself. And that is before teams need to clean, synthesize, and structure datasets for implementation in systems and algorithms. This process leaves very little time to focus on customer/product development and other core business goals.
Pre-collected Datasets are essentially the ‘fast way’ to scale up. Businesses that choose ready-to-use datasets are benefiting from the extensive technological and operational know-how of a third party. Allowing these businesses to focus on creating exceptional products, and consumer experiences that serve as the staples of growth.
How Datasets are currently being used by companies?
Here are a few use cases of companies that are creating market-altering technology using datasets so that they can run, instead of walk:
Company A: LinkedIn ESG datasets enabling an environmental footprint prediction tool
This company is an early-stage startup working on building a machine learning (ML) algorithm that predicts the potential environmental footprint on a per-company basis. To accomplish this, they receive a regular influx of LinkedIn Company Profile Datasets (these are data subsets that include approximately 50 million records), including company /production plant locations, number of employees, as well as employee background diversity, among others. Once they’ve received the data, they plug in these ready-to-use datasets into their algorithm, and calculate several parameters such as emissions pertaining to electricity usage, production output, commute, etc., enabling funds with an environmental, social, and governance (ESG)-focused investment approach to hone in on relevant new additions to portfolios.
Company B: eCommerce reviews that drive sales, and a positive brand image
This company scans eCommerce datasets to identify fake reviews designed to either boost a certain seller/brand or to create negative sentiment towards a competitor. They are able to offer marketplaces, brands, and sellers a strong tool to maintain the objectivity, and authenticity of their reviews, and ratings.
They also monitor real customer reviews in order to understand what aspects of the purchase cycle, and product features consumers are happy/unhappy with, so they can recommend to their customers how to improve while highlighting these aspects in marketing campaigns and listings in order to increase their market share.
The bottom line
Datasets are an effective tool for companies to gain access to the information they need in order to compete, understand target audiences, and build industry-changing technologies. But discovering, processing, enriching, and structuring the desired data independently is not only a time-consuming and costly endeavor, but also sometimes not possible. That is why companies that want to scale fast are hooking their teams and systems up with pre-collected datasets.