For complex problem solving, understanding context is often the first and important step in understanding and decision-making.
Initially, raw data undergoes processing to minimize inconsistencies and errors. Information is often stored in databases like relational databases, which organize data in tables with rows (records) and columns (attributes). Queries retrieve specific information subsets based on defined criteria.
Information Consolidation: Data from various sources is consolidated into data warehouses through ETL (extract, transform, load) processes. Extraction involves copying data from its source, transformation cleans the data to fit analytical needs, and loading integrates the cleaned data into the warehouse.
Information Analysis: Analysis begins with descriptive and exploratory methods, using statistics to summarize data and identify insights through distributions and relationships between data fields. Visualizations like histograms aid in understanding.
Predictive analysis: Predictive analysis then uses machine learning and regression to forecast future trends, employing data mining techniques such as cluster analysis and anomaly detection to discover patterns and unusual instances within the data.
The digital ecosystem is complex and volatile. For complex problem solving, understanding context is often the first and important step in understanding and decision-making. Create the relevant context to make a more lasting solution. The power of information is to empower businesses with real-time insights across the organization in ways never possible before. In the digital era with “VUCA” characteristics, timing is always important to leverage the right information for making effective decisions by the right people.
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