Announcing the First Ever Search Engine for Data Buyers
Be Among the First to Try it Out.
“Find the best data from quality suppliers at the right price.”?
Enter a Search and Find the Best Options in the Market.
If you've been following us for a while, you know how much we care about data accessibility. After talking with countless industry professionals, we discovered that there were many inefficiencies in the market related to purchasing and selling data.
For one, many data professionals discussed the lack of transparency in terms of price, quality and choice of data sets. A notably "trillion-dollar market" (Infonomics, Douglas Laney) has been riddled with problems from the start, positioning data buyers to be unprepared, confused, and ill prepped for their data buying endeavors.
As a result, our team at Blue Street Data decided to create the PQC Engine to assist in streamlining the data acquisition process. By cataloguing similar data sets in a standard scoring model, the PQC Engine enables better data buying decisions for everyone, regardless of their data literacy level. In an effort to remove inefficiencies in the buying process, Blue Street Data prequalifies data sets and suppliers. From novice to professional, our Guide to Buying 3rd Party Data and PQC Engine tool makes every stage of the data buyer's journey easier.
By utilizing our data query services, companies save an average of 4-5 hours and $20,000 per data query. With over 7,000 listings and 2,000 data providers, they are able to find a vast range of options within their industry. The PQC engine also provides a standardized scoring model with a holistic evaluation of providers based on over 40 proven factors.
"Most vendors won’t allow you to actually get a subsample of the data before you work with it, which makes [evaluating quality] very challenging."
Price, Quality and Choice of Datasets.
Pain points such as a lack of price transparency, quality transparency and a lack of choice between alternative datasets can hurt an organization's bottom line and their ability to serve their customers. Solving these problems and allowing for a more transparent market was Blue Street Data 's central focus when designing the PQC Engine for data buying professionals.
As a supplement to the PQC Engine, our team designed a buyer's guide that outlines tangible action steps to use when preparing to purchase 3rd party data. The Guide to Buying 3rd Party Data gives organizations a roadmap to effectively implement 3rd party data into their organization. The PQC provides some of the many tools mentioned in the buyer's guide to effectively judge the value of a dataset during the buying process.
Building the PQC Engine
Audrey Hasson , Blue Street Data's Development Manager provided some insight into building the PQC engine.
"The PQC was built in a twelve-week span, beginning with a two-week planning and research phase into the technology stack and software packages that would best complement our goals. After that, we began the development phase, and then entered a phase of testing and redrafting."
The largest roadblocks in development were centered around the data ingestion process, which pulls listings for the catalog from multiple sources across the internet automatically and combines it with internally sourced listings. We're most excited to provide users with the PQC matrix, a visual guide to help users compare the relative price and quality of each data option.
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Integrating a New Way to Evaluate Data Quality: The BQI Score
The BQI, or Blue Street Data Quality Index Score, is an all-encompassing data quality score that looks at 5 elements across 40 different factors of data quality. Each listed dataset in a search result has an associated BQI score, allowing you to immediately judge the quality of a dataset.
Plus, search results will include the PQC Matrix, a graph allowing data buyers to visually compare available datasets based on price relative to quality.
Traditional data quality metrics aren't enough to measure the value of 3rd party data. Therefore, we integrated 5 different elements to take into consideration when designing the PQC Engine.
"Having insight into the data dictionary when it’s delivered and how that model needs to be maintained is important. Some things that aren’t always observed in the process are licensing around the data — what are the permitted use cases? Can you duplicate the data? Can you put it into multiple systems?”?
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Population Health + Value Transformation Executive
4 个月I love the idea of quality rating the dataset to make the entire process more consumer friendly. The definitely goes a long way to correct information asymmetry, from an economic theory perspective. No more, “garbage in, garbage out” analytics.