Data modeling with bipp
Data modeling brings real-world context and associations to flat data that makes it come alive and allows it to be used for complex decision-making.
Data modeling lets you draw inferences from data by organizing it. Data modeling takes structured data, such as relational database tables, and creates a view of this data, using associations for multiple purposes. Then, using data modeling, we create representations of how this data works in the real world. This can be used for forecasting, business decision-making, identifying where efficiencies can be made, and where bottlenecks occur.
Data modeling has multiple use-cases:
Capture human knowledge about information currently stored in databases. For example, ignore the customer phone number in the customer table; instead, get it from the auxiliary customer info table.
Capture rules about different tables on how they are joined together. For example, finding a salesperson’s most lucrative customers from orders and inventory price.
Capture and persist the logic of frequent computations. For instance, don’t use a price column as is; always multiply it with the conversion rate for the relevant currency.
Enumerate all tables (and views) and the columns in them that are relevant.
Create un-materialized views that do not already exist in the database.
Data modeling is simply an extra layer of logic over the top of your databases to provide more contextual insights than is presently available. There are three primary types of data models, which vary according to their degree of abstraction.
Conceptual data models offer a big-picture view of what the system will contain, how it will be organized, and which business rules are involved. They are usually created when gathering initial project requirements.
Logical data models provide greater detail about the concepts and relationships in the domain under consideration. They can be helpful in highly procedural implementation environments. bipp’s single-source-of-truth data model approach encompasses both the conceptual and logical abstractions.
Physical data models provide a schema for how the data will be physically stored in the database. They offer a design that can be implemented as a relational database. BI platforms, like bipp, sit on top of the physical data model.
Organizations of all sizes rely on data models. In-house experts, external consultants, or third-party business intelligence software can all build models. Data modeling can even be used for personal applications, such as finding out when you are most productive, considering eating habits, sleep, type of work, and so on. In short, data modeling is for any application with a large enough data set to draw inferences.
Without data modeling, analysis is limited Data can be complex, with different data sources contributing to an overall picture. Linking these data sources with a data model can represent a real-life system and its interactions more accurately. Conversely, using a single source for data leads to limited data insights.
If you compare two businesses, for a basic example, selling raincoats. One company does data modeling, considering yearly weather patterns, competition, pricepoint, consumer sentiments, and previous sales to predict demand. The other business simply guesses based on their best estimates. The first business is more likely to accurately predict how much stock to buy to meet demand - and outperform the competition.
Underlying data must be clean, complete, and up to date. To get accurate insights from data modeling, the underlying data must be correct too. You also need a big enough pool of data to be able to make inferences with confidence.
Data modeling should consist of all relevant data sources to gain accurate insights. For example, a restaurant wanting to plan its weekly fresh vegetables and meat orders may include total dish orders from the previous week, orders from the equivalent week in the last year, and upcoming reservations. Without this information, they may produce extra produce or run out of dishes, impacting profit.
A BI platform with a feature such as bipp’s bippLang data modeling language is balanced to meet the needs of both technical teams and self-service end-users. bippLang is a minimalistic data modeling language with a compact vocabulary.
The bipp platform uses the data model to establish data governance based on a central business logic layer. This data model is separate from the underlying data and makes it easy to expand core dashboard infrastructure around the country or the world while maintaining centralized management.
This small footprint approach guarantees an easy learning curve for people with SQL skills. In addition, they can easily update the data model, with changes automatically backed up to a Git repository. Updates can be reflected instantly across all of a company’s dashboards or updated progressively, depending on the requirement.
The business logic layer ensures business users can explore their customized dashboards with confidence. They can trust their visualizations and easily filter them in real-time, which means they’re making decisions based on the latest information.
To see bipp’s data modeling in action…
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