Tailor your dataAre you unsure of how to make your data available to sell? Want to learn how to extract value from your data for your own business purposes? Reach out by generating a support ticket in our Discord. channelThere are two types of data you should be aware of:
Operational data This data is transactional and refers to data that is generated and used in the day-to-day operations of a business. This type of data includes information about processes, transactions, and interactions as they occur without any consideration for how they may read.
Measurable data Refers to data that is ready to be interpreted. In other words, data that can be quantified and analyzed to track performance and progress against specific goals or objectives. This type of data includes metrics such as sales, strategic (workforce, products, etc.) performance, and/or customer satisfaction.
An example that contrasts operational and measurable data could be the difference between tracking the number of emails a customer service team sends each day (operational data) versus tracking the response rate and resolution time of those emails (measurable data). The former gives a snapshot of the daily operations, while the latter helps measure the effectiveness of the customer service team in addressing customer needs.When offering operational data, you are supplying potential value to a consumer, this means that it is on them to add value to the data - usually by asking interesting questions regarding the dataset. The type of quality you can supply here depends on the comprehensibility and integrity of the transactional data.A lack of quality in operational data may appear as:
A column with free text (undefined categories) instead of categories. Sometimes a form may have free text instead of a category and users will input many different versions of the same item.
A column with nulls. Sometimes a form may not have an obligatory field.
A column with information from a different column. Sometimes a user may mix up entering information, placing the answer to field 2 in the spot for field 1 and vice versa.
When offering measurable data, you are supplying kinetic value to a consumer, this means that the data "answers" a question. The type of quality you can supply here depends on the clarity of the question in the data.A lack of quality in measurable data may appear as:
An unclear identifying index, instead of a date, you may have a datetime - creating multiple entries per day and generating confusing when graphic on a date axis.
A lack of cohorts. Take for example an age range, if you include all ages without a statistical stratification, you may confuse the user if they try to graph.
The choice of using a cohort vs leaving the continuous range. Sometimes a continuous range is needed for statistical analysis.
Lastly, consider that your datasets should read in a columnar format, meaning each row is an entry and each column is a feature.A healthy database should contain MECE (Mutually Exclusive Collectively Exhaustive) columns for its features and entries represented by one row.This below is not a database, but a visualization, you should never upload something that doesn't respect "one row per entry and one column per feature".There are many more tips and tricks for uploading data, in a standardized manner that will help you make better offers, as well as maintain a healthier database. Feel free to reach out to our business intelligence team to learn more about taking your data to the next level!
This below is not a database, but a visualization, you should never upload something that doesn't respect "one row per entry and one column per feature".
There are many more tips and tricks for uploading data, in a standardized manner that will help you make better offers, as well as maintain a healthier database. Feel free to reach out to our business intelligence team to learn more about taking your data to the next level!
