Data maturity model
We defined 5-level maturity model that business go through to master usage of information in business.
1. Awareness (Data Novice)
At this stage, businesses recognize the importance of data but may not have any structured approach to its collection or analysis.
Characteristics:
- Reactive decision-making.
- Limited data collection, often manual.
- No clear data strategy.
- Recognize the value of data but unsure where to start.
2. Consistency (Data Apprentice)
Businesses have started collecting data regularly and see its value but may not fully utilize it for strategic decisions.
At this level you may be spending about an hour every day reviewing the information to get insights of the business operations, finding improvements, and hints of future opportunities. Reports need to be prepared with manual adjustments and analysis.
Characteristics:
- Basic tools for data collection in place.
- Beginnings of data-driven culture but limited to specific departments or functions.
- Some data inconsistency and quality issues.
3. Integration (Data Practitioner)
Data is integrated into decision-making processes, and there's an emphasis on improving data quality and insights.
At this level more people in the organization start using information in their work. You start asking new, more complex and insightful questions of the data available to you, and all your decisions and business conversations have information, measurement aspects to them. Most of the information is available on-demand and continuously updated.
Characteristics:
- Consistent data collection across departments.
- Beginning of data analytics initiatives.
- More employees trained and comfortable with data.
- Start of predictive analytics.
4. Optimization (Data Specialist)
Data-driven decision-making is a norm. The business optimizes operations and strategies based on data insights.
Characteristics:
- Advanced analytics and possibly machine learning initiatives in place.
- Strong data-driven culture across all business functions.
- Regular reviews of data strategies and tools.
- Emphasis on continuous improvement based on data.
5. Innovation (Data Visionary)
Data is not just a tool but a core strategic asset. The business is at the forefront of leveraging data for innovative solutions and offerings.
Characteristics:
- Proactive use of data for business innovations.
- Integration of AI and machine learning for complex predictions and strategies.
- Strong emphasis on data privacy, ethics, and quality.
- Leading the industry in terms of data utilization.
We defined 5-level maturity model that business go through to master usage of information in business.