Data modeling defines how an organization analyses and extracts value from its data. Although everyone in the business will not have the technical knowledge to assist in building a data model for data analytics, they should understand what it is, and its incumbent importance. 

What is a data model?

A data model determines and visualizes how data will expose itself to the end-user. The objective of a data model is to optimize the creation and structure of database tables, setting the business up for the best possible data analysis. A data model for data analytics delivers clean and useable data fro driving business decisions.

Data modeling is a process that feeds data analytics, and they should not be confused as the same thing. The creation of data models and analyzing data are different skillsets that may typically operate between various departments. 

Data models simplify data analytics

A data model for data analytics makes it a far more straightforward function. As the data models only give users the data they need, it negates the risk of redundant, dormant, and low-quality information. The data modeling roles tend to be relatively technical but will rely on input from all personnel to define the objectives and serve up the correct data.

A data modeler will be responsible for:

  • Creating relationship diagrams that show how critical business concepts connect
  • Building a data dictionary that communicates the data requirements to all stakeholders
  • Generating data maps that can resolve issues as part of data migration or data integration projects

The data modeler will not look to manipulate data or be involved in database design.

Data dictionary

An integral feature of a data model for data analytics is the data dictionary. An efficient data dictionary will describe all the objects that exist within a data model. As a minimum, this includes appropriately labeling tables and defining terms. All the rows and columns will have a precise specification, enables data analysts to understand what they are working with. 

If a marketing team wants to work with data to create a campaign, they can easily understand the fields available within the data model. A data model for data analytics needs to be self-explanatory and not left open to any interpretation.

The data dictionary is maintained by all stakeholders but is the ultimate responsibility of a data modeler. The expert can adjust the data model as required to enable better downstream use in analytics.

The importance of a data model

A data model for data analytics has several long-term benefits.

Quality results

Imagine if an architect goes ahead with construction without understanding the blueprints. The likelihood is that it would not be an efficient process. The same applies to data analytics, whereby having a model in place helps define a problem and source the best solutions.

Cost

Data models will catch any oversights, reducing risk and the cost of errors in analysis. It is far easier to fix data models than the end-product.

Scope

A data model gives analysts a better overview of what they need to consider as part of their processes and projects. A model allows end-users to collaborate and agree on a scope.

Performance

A database with a robust structure and models performs faster than one without. Modeling provides an understanding of the data for stakeholders to optimize their requirements.

Documentation

We have all been in a situation where a member of staff leaves a business and takes their knowledge with them. Data models are a form of documentation that mitigates the risk of knowledge workers leaving a company.

Summary

A data model for data analytics has several benefits. It reduces the risk of low data quality, improves performance, and ensures that data is not kept in siloes. Any business that deals with data should always start with a modeling plan before it gets out-of-hand, at a time where volumes of information continue to accelerate.

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