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As someone deeply involved in shaping data strategy, governance and analytics for organizations, Im constantly working on everything from defining data vision to building high-performing data teams. My work centers around enabling businesses to leverage data for better decision-making and driving impactful change.
Producing insights from raw data is a time-consuming process. Predictivemodeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. imputation of missing values). ref: [link].
You may be interested to know that TechJury reports seven out of ten businesses rate data discovery as very important, and that the top three business intelligence trends are data visualization, data quality management and self-service business intelligence. or What is happening? And that is exactly what is happening!
Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data.
There are other dimensions of analytics that tend to focus on hindsight for business reporting and causal analysis – these are descriptive and diagnostic analytics, respectively, which are primarily reactive applications, mostly explanatory and investigatory, not necessarily actionable. This is predictive power discovery.
By leveraging data analysis to solve high-value business problems, they will become more efficient. This is in contrast to traditional BI, which extracts insight from data outside of the app. that gathers data from many sources. These tools prep that data for analysis and then provide reporting on it from a central viewpoint.
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