What does Data Maturity Mean?
Data maturity reflects how advanced a company is regarding its use of data to within the organization. The model provides a way to measure how your company is currently using data. As data is used for more advanced processing the organization moves higher on the maturity model
How a Data Maturity Model Can Improve Your Business?
Knowing where you are on the data maturity model can help you establish projects to improve your data utilization. As companies mature they increase their ability to use data as a true asset to transform your company. To become a data driven company you need to establish your current data maturity regarding how you are using data. Create investments in projects that will improve your current level or lay the foundation to move to the next level in the data maturity model. There are 3 core benefits of progressing on the data maturity model.
- Strategic Use of Data as a Asset
- Faster reaction to Business Changes
- Better Information and Analytics
Data Maturity Models Projects Examples
- Data Strategy
- Data Governance
- Data Quality
- Data Operations
- Data Architecture
- Data Processes
Use the model below to see your current data maturity and analytics maturity.
Gartner Survey on Data and Analytics – 4 minute read
Level 1 – Basic Data Maturity
Companies in this level have basic reporting from source systems using data and information. They get their data from ad-hoc request to subject matter experts that can pull data into Excel and create pivots tables or simple graphs. Data is contained in silos and is combined in spreadsheets to try to get deeper insights.
Level 2 – Opportunistic
At this level companies may have a central reporting platform that enables custom reports. IT is supporting and implementing solutions that enable some insights into systems. Reports mash up data from different source systems to produce new insights. Data Quality issues start to emerge as data in different systems can be merged.
Level 3 – Systematic
A data and analytics strategy is defined to determine what is required to push the organization forward. Business executives become champions to change how data and analytics are used in the business, becoming data aware. Data sources are integrated into single version of the truth. External data sources are added to provide additional context to data driven decisions.
Level 4 – Differentiating
A chief data officer is hired to transform how the business uses data and analytics. Business operations are being driven by data and not reactive. Critical decisions are being made to improve on a department level. A Business Intelligence Competency Center is created to share information and content. Data and information is critical to how the business is using data.
Level 5 – Transformational Data Maturity
At this level, a business is running with a data first strategy. Strategy and operations are driven by data, analytics and machine learning. Employees from all levels are data aware and leverage data and analytics to make better decisions.
Ready to see where you are on your journey? Get more from your data with our Data Transformation Plan
Gartner provides research Here – Analytics Maturity Model