Analytics Maturity Self-Assessment
Data Maturity Level
Answer a Few Basic Questions about your Organization.
1. How do you assess the state of Analytics/ Data Science use in your organization?
Decisions are all ad-hoc/ instinct-driven
Basic understanding of the potential value is emerging from pilot projects
Its value has been proven in limited use cases
It's widely used across the organization for a variety of use cases
It's an integral part of the organization's culture and its decision making process
2. How is Analytics/ Data Science perceived by your organization's top management?
They are still skeptical, but curious
Targeted analytics activities at work but only limited support from the top
Open and wilful support of pilot analytics activities to prove value on promising use cases
Full organization support in place for proven use cases and piloting new use cases
Fully bought-in and constantly investing in people, technology and processes, to support it across the enterprise
3. What is the state of data collection & storage for Analytics use?
Data is not yet collected, stored and managed for any analytics purpose
Data collection is ad-hoc and on a 'need-only' basis for targeted activities
Processes in place for limited but proven analytics activities
Mature, but siloed
A unified enterprise data strategy is already implemented with full governance
4. How do you describe your data availability and access processes for Analytics?
Data is not readily available for analytics activities and very often, it has to be extracted from original source systems
Only specific/ relevant data is readily available and accesible for Analytics
All relevant data is readily accessible for Analytics, but analysts/ data scientists have to go to different places to fetch it
All relevant data is accessible via a unified data store (EDW/ Data Lake) for those who need it, with full governance and QA
5. How do you gauge the data quality processes in your organization that's used for Analytics?
Low (investment lacking; DQ processes are ad-hoc)
Medium (More investment needed for DQ processes to improve)
High (plenty of investment already; DQ processes are highly matured/ optimized)
6. What is the state of analyzing Big Data in your organization?
We do not see the need!
We have a need to move towards a scalable data/ analytics platform soon
Efforts are already underway to improve existing data/ analytcis platform(s) for big data
We have a mature scable, measurable & cost effective data/ analytics platform(s) in place
7. What are the levels of Analytics/ Data Science activities you conduct at your organization?
Not much done beyond Excel
A. BI reports, dashboards, OLAP cubes, visualizations, … are designed, created, and updated in batches
B. (A) plus reatimel or near-realtime BI capability for high velocity high volume data
C. (A) plus Machine Learning/ Predictive/ Prescriptive Analytics typically using the granular detailed data
D. (B) + (C)
8. How do you assess your organization's advanced/ predictive analytics competencies?
9. How do you describe the presence of your Analytics/ Data Science process/ team?
None/ Scarce Data Science Analytics function in the enterprise
Disparate Analytics teams/ champions operating in separate business functions
A centralized Data Science team has been established in support of different BUs/ functions and their Analytics needs
A CoE has been established for Data Science/ Analytics practices
10. To what extent are the analytics "deployed and leveraged" in business decisions/ operational processes?
(e.g., consumed by business to support/ validate decisions, implemented in production, used for automated decision making, etc.)
11. Typically, what is the general perception/ attitude of the business users lower levels, in utilizing the Analytics-generated results and the resulting change management?
Is slowly gaining support
Cannot perform their jobs without it
Additional Custom Note