Big Data Maturity Level People Process Maturity Level 1 - Data Access • Basic IT / Computer skills • By memory
Level 2 - Consolidation • ETL , DBA • By experience
Level 3 - Reporting
• Data Quality
• Statistics
• Development
Level 4 - Alerting • Advanced Statistics • Automated
• Documented and reproducible
Level 5 - Engaging • NLP , Predictive , Modeling , Math , Machine Learning
• Learning and evolving
Figure 2 : Big Data Maturity
Figure 2 outlines the increasing maturity of big data adoption within an organization . As organizations mature through the different levels , there are technology , people and process components . The data lake is commonly deployed to support the movement from Level 3 , through Level 4 and onto Level 5 . The data lake provides a platform for execution of advanced technologies , and a place for staff to mature their skill sets in data analysis and data science .
The Business Value of a Data Lake
The primary value of a data lake is enabling flexibility , through a scalable platform for analysis of complex data sets . Many different technologies will go into this analysis , including predictive analytics tools , data modeling , data quality and machine learning . The first part of any analytical workflow is the data process , Figure 3 shows the steps commonly followed to Ingest , Cluster , Index and ultimately Analyze data within a data lake . These steps are key to ensuring that high quality data is brought together , associated properly and organized to enable data scientists to analyze the prepared data .
Ingest Bring in data from multiple sources and associates
Cluster Discovery of patterns and relationships
Index Organize data for high-speed access and location
Analysis
Identify & analyze relationships
Figure 3 : Analytical Approach to Data Analytics
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