Analytics codes often fall short of software engineering standards. We've all seen it – sprawling SQL queries, duplicated logic across reports, and metrics that somehow give different results depending on who ran the query. It's a maintenance nightmare waiting to happen.
Holistics Reusability is our solution to this problem, which is made up of a set of principles:
All of this has been a bit abstract. What does Holistics Reusability look like in practice?
Make improvements or bug fixes in one place and propagate changes throughout all components.
Handle growing analytics demands without exploding your analytics codebase.
Address novel business needs easily by composing existing analytics logic.
Reduce data discrepancies with uniform metric definitions.
Provide clarity for business users by reducing the number of datasets needed to address diverse needs.
Data teams have the peace of mind that business-critical data processes are robust and reproducible.
Define reusable chart components to build similar dashboards without re-writing everything from scratch.
Reduce dashboard development time and increase ease of maintenance.
Metrics can be defined once, extended and used across the organization to ensure analytics consistency.
No more contradicting results due to inconsistent metric definitions.
Define reusable dimensions and metrics that can be recombined to create new behaviors, simplifying ad-hoc analytics exploration and construction.
Point charts to clients’ respective data sources dynamically to serve similar needs across multiple clients without duplications.
You find yourself writing a lot of repeated code for similar analytics logic, datasets, and charts across different categories, which makes it hard to maintain data consistency and usability for business users.
Creating and maintaining multiple reports for multiple clients is costing a ton of effort. Individual clients require small changes which means duplicating the entire report.
Customizing your analytics products for each customer’s need is slowing down development. Having a different version for each customer prevents you from delivering improvements and bug fixes on a wider scale.
With Holistics, our customers can easily define reusable data logic, reuse data models to reduce maintainace efforts, or reuse metrics and dashboards anytime, anywhere.
The ability to parameterize a widget using variables and reuse it was a game changer.
I have KPI widgets that appears in 50 rows in my dashboard. I was able to take one function, parameterize it. Then I just have to pass in those variables to produce a widget.
Mike K.
Director of Enterpire Analytics, Strongmind
Our main goal was to find an approach to define analytics logic centrally and have people reuse it so you can get to a faster speed to insight.
With Holistics, we can have it all under control, we can have correct, reusable measurements and metrics going out there.
Craig Wilson
Staff Data Engineer, ZOE
I can simply extend my existing data model by referencing a different schema, allowing me to maintain one lightweight table and one heavy table in different places.
AML Extend gives me a clean way of doing this, so when the schema changes, I don’t have to worry about editing in two different places.
Stephen Lee
Senior Data Scientist, Aurora
Holistics effectively allows us to be repeatable. We add a new client, copy and paste all the data models, and change the data source. Done!
David Aldred
Insight Director, Retail Insight
The semantic layer is Holistics’ biggest draw because I was able to build reusable models, dimensions, metrics, and then describe them as code
Sterling P
Sr Staff Data Engineer, Mainspring Energy
Streamlined analytics process. Reduced maintenance efforts.
It all starts with Holistics .
