Vendors should focus on creating the most attractive products possible in order to maximize their share of market, while still operating within organizational & market constraints. Attractive products cater to a sizeable audience, convert the maximum number of buyers and minimize buyer turnover.
To maximize the attractiveness of their products, data Vendor should focus on including the data fields and entries that matter most to buyers of data.
In the prior blog posts, we conducted analysis to create a well-defined set of use cases and customer types (See "Structured to Sell - Part 2 of 4: Your Data's Market Potential"). Based upon this evaluation, you should have a good understanding of the market needs and how to address them.
Vendors should focus their structuring efforts on how to present the data fields and entries in a way that induces usability and compliance.
Data’s Usability
It seems obvious to state that the value of a data product dramatically increases in lock-step with its usability. Buyers of data want to quickly and easily put data to use from the moment they purchase it. They thirst for the insights the data will reveal. To be successful, data Vendors need to deliver the optimal recipe for maximum usability.
To assess if data buyers can readily use the data, you should evaluate it on four dimensions: Relevance, Detail, Consistency and Format.
Relevance
It is imperative that a data product provides the information necessary to drive analysis and produce actionable insights. Data products need context and relationships in order to be relevant.
Generally, the more enriched a data set, the more relevant it becomes. Thus, DataStreamX encourages data Vendors to provide as much detail as possible in data products and to price them accordingly. Pricing considerations are covered in our whitepaper, “A Practical Guide to Pricing Data Products” found here.
Detail
Data Buyers should not have to perform any guesswork to interpret and understand the data contained in a Vendor’s product. The simple solution is to properly document the data schema, parameters and values contained in the data set.
Detailed and clear schema accelerate the data buying process, while complicated schema actually create obstacles in the buying process. Therefore, Vendors should always include thorough, accurate and easy-to-understand data schema with each data product. A quality schema defines each term within the data set.
Consistency
Data products need consistency of records and formatting. Consistency of the records is crucial, as data Buyers will build applications around the format and structure of the data. If records, labels and other critical fields are altered, the applications may be disrupted. The data product may then be deemed unreliable and Buyers may discontinue the use of the product.
A few common consistency issues (there are a great many more):
- Monetary unit
- Decimal place location
- Naming conventions and spelling
- Location information
- Date format
- Time stamp
In addition to assuring that data is consistent, Vendors should not “clean” their data beyond their normal circumstances. False positives and abnormalities can actually be some of the most valuable data points—and precisely what Buyers are looking for. Thus, “clean” data does not always translate into higher fidelity of data.
Format
Finally, excessive formatting of data is not necessary. In most cases, Buyers want to ingest and process “raw” data without any formatting obstacles. For example, merged cells and conditional formatting should not be present in a Microsoft Excel or CSV file as it would be quite difficult to use with other data in applications.
Data products should follow standard file formats as well. Buyers will be familiar with ingesting any of the file types below:
- JSON
- XML
- CSV
- XLS
- XLSX
- PDF
If you are interested in learning more about how to select and structure data products, please download the whitepaper “The Practical Guide to Selecting & Structuring Data Products.”
Missed the first part of the series? See "Structured to Sell - Part 1 of 4: Data Monetization Motivations" to help your organization understand how to sell data.
