Target Baseline Value
Name Description Performance Value
KPIs Scorecard for Big Data This big data scorecard is an example that is used to illustrate ideas discussed in the “Using KPIs to Focus Big Data Efforts” article. Learn more: https://bscdesigner.com/kpis-for-big-data.htm 73.13%
Level 1. 3-V metrics 72.99% 16.67
Volume Volume of data is a measure by itself (GB, TB, etc.) 60% 1.2
Variety Variety can be quantified as the number of different types of data sources 92.31% 48
Velocity Velocity is defined by the volume of data generated/analyzed per time period 66.67% 0.8
Level 2. Big data process metrics 70.68% 31.5
Frequency of data collection 72.73% 1.2
Time needed for data to be available for analysis 70.59% 0.8
Time needed for data to be reported in a form of KPIs 88.24% 2
Query to report conversion rate Define the concept of a qualified query first, and then track the percentage of qualified queries that our data scientists were able to answer 37.5% 30
Data capturing capabilities The accuracy level of the data that we capture (the ideas discussed above in Veracity). 70% 70
Customer privacy audit score 85% 85
Level 3. Lagging KPIs (examples) 0
Customer lifetime value changing
First-call resolution rate change
Time to performance (HR) metric
Level 4. Leading KPIs. Ensuring big data success. 75.5% 68
Funds invested in big data initiatives
Time spend on big data initiatives
Big data training effectiveness 76% 76
% of strategic goals with big data initiatives 75% 60
Challenges Main challenges of big data 78.67% 73.67
Challenge 1. Focusing big data The main challenge is to focus big data on what matters, and then deliver it into the right hands. 76% 76
Big data training effectiveness 76% 76
Challenge 2. Alignment with business strategy Big data will be game changing when it provides tangible business value. 75% 60
% of strategic goals with big data initiatives 75% 60
Challenge 3. Data security and privacy Data collection and analysis must be ethical and legal. 85% 85
Customer privacy audit score 85% 85
Capabilities Improve team’s capabilities in terms of big data 75.5% 68
Train key members on big data 75.5% 68
Big data training effectiveness 76% 76
% of strategic goals with big data initiatives 75% 60
Internal Big data process 65.44% 10.93
Collection 70.59% 0.8
Time needed for data to be available for analysis 70.59% 0.8
Analysis 37.5% 30
Query to report conversion rate Define the concept of a qualified query first, and then track the percentage of qualified queries that our data scientists were able to answer 37.5% 30
Reporting 88.24% 2
Time needed for data to be reported in a form of KPIs 88.24% 2