type of analytics
- Descriptive
- historical
- kpi
- roi
- report on sales and financial
- Diagnostic
- anomaly detection, investigation, data collection and statistical explanation
- Predictive
- time series prediction
- machine learning modelling
- Prescriptive
- By analyzing past decisions and events, organizations can estimate the likelihood of different outcomes.
- Cognitive
- Cognitive analytics attempt to draw inferences from existing data and patterns, derive conclusions based on existing knowledge bases, and then add these findings back into the knowledge base for future inferences, a self-learning feedback loop. Cognitive analytics help you learn what might happen if circumstances change and determine how you might handle these situations.
- Inferences aren’t structured queries based on a rules database; rather, they’re unstructured hypotheses that are gathered from several sources and expressed with varying degrees of confidence. Effective cognitive analytics depend on machine learning algorithms, and will use several natural language processing concepts to make sense of previously untapped data sources, such as call center conversation logs and product reviews.
roles
- Business analyst
- focus on business domain interpretation
- Data analyst
- profiling, cleaning, transforming
- build semantic model
- and turn data into insights
- manages power bi assets ( report, dashboards, workspaces, and semantic model)
- work with DE to locate data source
- Data engineer
- manage on prem and cloud
- collate multiple sources (structured and unstructured)
- ensure data service securely and seamlessly integrate
- goes beyond database admin
- but might not include overall operational data management
- Data scientist
- descriptive + predictive + diagnostic
- devise hypothesis
- DA assist with visual and report
- Database administrator
- performance and optimization of database
- granting access priviledges
task of da
prepare profiling, cleaning, and transforming Privacy and security assurances - ensuring the integrity of the data, - correcting wrong or inaccurate data, - identifying missing data, - converting data from one structure to another or from one type to another, - or even a task as simple as making data more readable. model - done by defining and creating relationships between the tables - impact on the general accuracy and performance of your report - An effective semantic model - makes reports more accurate, - allows the data to be explored faster and efficiently, - decreases time for the report writing process, - and simplifies future report maintenance. visualize - too much data point can lead to confusion and lost of focus analyze find insights, identify patterns and trends, predict outcomes, and then communicate those insights in a way that everyone can understand. manage Power BI consists of many components, including reports, dashboards, workspaces, semantic models, and more. As a data analyst, you are responsible for the management of these Power BI assets, overseeing the sharing and distribution of items, such as reports and dashboards, and ensuring the security of Power BI assets. Apps can be a valuable distribution method for your content and allow easier management for large audiences. This feature also allows you to have custom navigation experiences and link to other assets within your organization to complement your reports.