Evaluating KPI Dashboards to Increase ROI for Data Engineering Products — I

“Uncover the ROI: Evaluating Data Processing and Storage Costs for KPI Dashboards” -

I have written an article that can help to identify the ROI for each KPI Dashboard. In a larger software product having multiple KPI Dashboards, there are parallel data processing pipelines behind each KPI Dashboard. Therefor, It’s important for businesses to gauge the efficiency and effectiveness of their KPI dashboards in relation to the resources expended.

If you are unfamiliar with what a KPI Dashboard is, I have come across a document that provides explanations and examples.

Why Its Important —

you should focus on improving cost estimates and budgeting for KPI dashboards as part of your project management process. Here are a few steps you can take to address these concerns:

1. Develop Accurate Cost Estimates: Work with your project team to gather detailed information on the costs involved in developing and maintaining KPI dashboards. Consider factors such as software licensing, data processing/storage and any other resources required.
2. Allocate more funds to High-Performing KPI Dashboards: Identify the KPI dashboards that are performing well and delivering value to the organisation. Allocate additional infra resources to these high-performing dashboards to support their continued success.
3. Identify Poor-Performing KPI Dashboards: Assess if the cost of maintaining these dashboards outweighs the revenue or value they generate. Consider whether the data maintenance costs can be optimised, or if it may be more strategic to sunset these dashboards.
4. Discuss Costs and Performance Expectations: Engage with subject matter experts (SMEs) responsible for each KPI dashboard to understand their performance expectations and the costs associated with meeting those expectations.
5. Track Costs and Performance: Implement a robust cost tracking system to monitor the expenses associated with KPI dashboards. Regularly review the cost data alongside performance metrics to identify opportunities to optimize spending and improve overall financial performance.

How to achieve that ?

1. Data Ingestion ( Re-route Raw Data Based on KPIs) —

Identify data for each KPIs and route to different paths as soon as you receive the data. The KPI data router is a piece of code that can identify incoming data and reroute it to specific KPIs. It may use some reference data to identify the KPIs.

1. If the data is received in the single HDFS directory from source, then in output create multiple HDFS directories — one directory for each KPI.
2. If the data is received in Kafka cluster, then forward received data to multiple Kafka clusters — one cluster for each KPI.

2. Executing Business Logics ( Data Processing )-

1. Spark / Hive: We need to maintain separate yarn queues for each KPI to streamline data processing. Identify nodes(VMs) assigned to each queues. This information will be used to calculate the associated costs.
2. Kafka / Confluent: It is crucial to create separate Kafka clusters for each KPI to ensure reliable data streaming.
3. Snowflake: We should create different warehouses with varying compute power for each KPI to optimise data processing.

3. KPI Dashboard Data storage —

The processed data can be stored in one or more hot storage databases, depending on the application’s needs. This data is ready to be consumed after the raw data has been processed.

4. Smart API layer —

These are the actual endpoints that are responsible for sharing data to KPI dashboards. These APIs should be intelligent enough to retrieve data from the database based on specific needs (fetching data from a particular database).

KPI Income-It is calculated by subtracting the monthly bills of data ingestion, data processing, and data storage from the net monthly earning from customers.

KPI Net Income= $ net sales — ( $ bills of data ingestion + data processing + data storage )

By implementing these steps, you can gain better control over the costs associated with KPI dashboards and ensure that your budgeting aligns with your project’s financial performance expectations.

*NOTE : This article solely focuses on the costs related to data processing and data storage. However, it is important to note that there may be additional costs associated with development, licensing, testing, and other factors.