Are you suffering from any of these data-related business pain points?
- Lack of trust in the data and data driven metrics
- Expected regulatory compliance and indecisiveness as to where to start
- Complexities pertaining to controlling and forecasting data-related costs
- Struggling with data valuation
- Lack or absence of context of data
- Challenges around visibility for data lifecycle and lineage
- Inability to successfully implement Data Governance policies and procedures
- Poor quality data in effect fuelling poor business decisions
REALITY CHECK (As-is state of an organization)
How mature is the data that your organization houses?
The data readiness level (DRL) is a concept that refers to the quality and maturity of data. There are different stages (i.e., bands) that represent this quality and maturity, and each stage has its own specific requirements.
Inspired by Neil D. Lawrence - Cornell University
Data Readiness Levels
- Band A - Context: Band A represents data in context, it is about the data utility in terms of data appropriateness being able to solve the desired business problem.
- Band B - Representation: Band B is about data validity assessment in terms of data correctness (data quality) and data completeness.
- Band C - Accessibility: Band C is a category that deals with assessing data in terms of its accessibility, privacy, legalities, and other aspects.
Want to know where your organization lies on that spectrum?
The good news is that we at NobleProg offer an extensive data readiness levels (DRL) assessment and curate a data readiness report for you.
The following are the parameters against which the evaluation will be carried out:-
- Data Management Maturity
- Capacity to Change
- Collaborative Readiness
- Business Alignment
- Data accessibility (programmatic access, data format, data licenses, ethics & safety, secured data access and access permissions)
NOBLEPROG DATA MANAGEMENT SOLUTION - How can we help?
DAMA-DMBOK Knowledge Area Wheel
NobleProg - Data Management Consulting Services Include:
- Data Quality
- Metadata Management
- Data Governance
- Reference & Master Data
- Data Security
Transition from "As-Is" State to "To-Be" State of Data Management