How to measure Impact in Data engineering Projects

 How to measure impact in Data engineering Project 

* Depending on the stage your data engineering project is in the, Metric to measure impact will vary

* First we need to define a goal and measure how we are doing against the goal. High level goals are 

    * Have the Data - Stage 1 

    * Know how to use it - Stage 2 

    * Trust the Data - Stage 3 

Have the Data - Stage 1 

When we just starting data team, we are in this stage. 

Accuracy

At this stage we want to measure accuracy of data. For this we generally setup alarms comparing source data with target. Example if the sum of sales of orders in source by 10 am is 20. Same should reflect in the datawarehouse 

Completness 

We want to make sure we have captured all the orders by 10am . Example 5 orders in source , same 5 should reflect in target 

Consistency 

Are we consistent in getting the data . SLA met 

Usability 

Number of reports , Dashboards published in Subject area 

User survey results 

Reliability 

Number of tickets for data quality by users 

Know how to use this data - STAGE 2 

Here we mean business users should know how to use it . We use below metrics to track it

1. User Training / BI Office hours conducted /Number of people attended weekly 
2. Number of users onboarded.
3. Scheduled reports running 
4. Adhoc queries run by users on Daily basis 

Trust the Data - STAGE 3 

Metrics we track at this stage

* Is DW used for operational reporting. We want to track add direct business impact 

* Infrastructure savings - At this stage, we want to to track how we can reduce our infrastructure costs

* Reduction in Number of Adhoc request for data via tickets 

* Turn Around Time - This is at very high level. How long does it take to fulfill a new data requests/ Project time