Data Products
Data Categories
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Facts
Fact data describe the factual information pertaining a dataset.
Examples include facts about businesses (annual revenue, no. of employees, country origin, location of offices etc.)
Because these data are not widely used by multiple businesses, it is usually customized (prepared only upon request).
Fact data tend to come together with a master dataset.
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Master
Master data describe the people, places, and things that are involved in an organization’s business.
Examples include people (customers, employees, vendors, suppliers etc.), places (locations, sales territories, offices etc.), and things (accounts, products, assets, document sets etc.).
Because these data tend to be used by multiple business processes and IT systems, standardizing master data formats and synchronizing values are critical for successful system integration.
Master data tend to be grouped into master records, which may include associated reference data. An example of associated reference data is a state field within an address in a customer master record.
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Reference
Reference data are sets of values or classification schemas that are referred to by systems, applications, data stores, processes, and reports, as well as by transactional and master records.
Examples include lists of valid values, code lists, status codes, state abbreviations, demographic fields, flags, product types, gender, chart of accounts, and product hierarchy.
Standardized reference data are key to data integration and interoperability and facilitate the sharing and reporting of information. Reference data may be used to differentiate one type of record from another for categorization and analysis, or they may be a significant fact such as country, which appears within a larger information set such as address.
Organizations often create internal reference data to characterize or standardize their own information. Reference data sets are also defined by external groups, such as government or regulatory bodies, to be used by multiple organizations. For example, currency codes are defined and maintained by ISO
Data Access Models
Point-in-time download
Data will be updated as of time of download (constant data refresh/ updates will not be provided)
API access
API stands for Application Programming Interface, it is a software-to-software interface. APIs provide a secure & standardized way for applications to with one another to deliver information/ functionality - allowing data to be constantly updated & refreshed
SaaS no-code
Using a no-code SaaS platform allows both developers and non-developers to rapidly build features without the need for coding - they can just drag and drop modules into a logical chain
Applicability across industries & verticals
Use Cases
 Information Technology |  Banking |  Healthcare |  Pharmaceutical | |
|---|---|---|---|---|
| Data | Corporate Legal Hierarchy | Swift Codes | ICD Codes | Drug Molecule by Brand & Country |
| Problem Statement | Millions spent to procure corporate legal hierarchy data Millions more spent to improve quality | Incorrect swift codes leading to lost/ delayed payments in billions every year | Incorrect ICD codes leading to loss in millions for insurance companies & hospitals | Inaccurate information on drug molecules leading to patients being prescribed wrong medications |
| Root
Cause | External data sources rife with broken hierarchies | Swift codes revised and increased every year Outdated information | ICD codes revised and increased every year Outdated information | Data of drug molecules are gotten from various sources and are hence in inconsistent formats |
| Impact | Sales allocations, revenue forecasting Impact customer satisfaction | Cost for tracking and rectification Incomplete and inaccurate financial data | Denial of claim leading to lawsuits Incorrect payment of claims | Each year in the U.S., 7,000 - 9,000 people die due to medication error Total cost of looking after patients with medication-associated errors exceeds $40 billion each year |
Use Cases
| Information Technology | Banking | Healthcare | Pharmaceutical |
|---|---|---|---|
Data
Corporate Legal Hierarchy |
Data
Swift Codes |
Data
ICD Codes |
Data
Drug Molecule by Brand & Country |
Problem Statement
Millions spent to procure corporate legal hierarchy data Millions more spent to improve quality |
Problem Statement
Incorrect swift codes leading to lost/ delayed payments in billions every year |
Problem Statement
Incorrect ICD codes leading to loss in millions for insurance companies & hospitals |
Problem Statement
Inaccurate information on drug molecules leading to patients being prescribed wrong medications |
Root Cause
External data sources rife with broken hierarchies |
Root Cause
Swift codes revised and increased every year Outdated information |
Root Cause
ICD codes revised and increased every year Outdated information |
Root Cause
Data of drug molecules are gotten from various sources and are hence in inconsistent formats |
Impact
Sales allocations, revenue forecasting Impact customer satisfaction |
Impact
Cost for tracking and rectification Incomplete and inaccurate financial data |
Impact
Denial of claim leading to lawsuits Incorrect payment of claims |
Impact
Each year in the U.S., 7,000 - 9,000 people die due to medication error Total cost of looking after patients with medication-associated errors exceeds $40 billion each year |
Reliable and easily consumed foundational data is simply not available at scale across verticals
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