7. Data Analytics - Ask Questions to Make Data-Driven Decisions - Week 2

Definitions:

Data // a collection of facts

Data // can help us make more informed decisions

Data-Driven Decision Making // 

Data-Inspired Decision Making // explores different data sources to find out what they have in common

Algorithm // a process or set of rules to be followed for a specific task

Pivot table // a data summarization tool that is used in data processing. Pivot tables are used to summarize, sort, reorganize, group, count, total or average data stored in a database.

Metric Goal // a measurable goal set by a company and evaluated using metrics

Dashboard // a single point access for managing a business's information.

Mathematical thinking // breaking down a problem step-by-step in order to see patterns


Small Data vs Big Data:

    Small Data:

        - Specific metrics

        - Short time-period

        - Day-to-day decisions

        - Spreadsheets

    Big Data:

        - Large and less specific. Stored in databases

        - Long time-period

        - Big decisions

        - SQL

        - VOLUME (AMOUNT), VARIETY (KIND OF DATA), VELOCITY (PROCESS SPEED),                      VELACITY (QUALITY AND RELIABITY)


Types of Dashboards:

    - Strategic: focuses on long term goals and strategies at the highest level of metrics

    - Operational: short-term performance tracking and intermediate goals

    - Analytical: consists of the datasets and the mathematics used in these sets


Data vs Metrics:

    - Data // collection of facts

    - Metric // single, quantifiable type of data that can be used for measurement. Specific Data or formula.

            EX: Sales revenue from individual salespersons


Incomplete Data vs Small Data:

    - Making decisions based on incomplete data is dangerous.

    - Making decisions on accurate small data is good.


Quantitative vs Qualitative Data:

    - Quantitative data // specific and objective measures of numerical facts.

            - The what, how many, how often?

            - Visualization by charts and graphs.

    - Qualitative data // subjective or explanatory measures of qualities and characteristics.

            - Things that can't be measured, like customer reviews.

            - The why? Helps understand the quantitative data.


Data Presentation Tools:

    - Reports // a static collection of data given to stakeholders periodically

            Pro:

                - High level historical data

                - Easy to design and sent out periodically

                - Reflect data that's already cleaned and sorted

            Con:

                - Continual maintenance

                - Less visually appealing

                - Static information

    - Dashboards // monitors live, incoming data

            Pro:

                - Dynamic, automatic, and interactive

                - More stakeholder access

                - Low maintenance

            Con:

                - Labor-intensive design

                - Can be confusing

                - Potentially uncleaned data


Additional Resources:

https://www.tableau.com/learn/articles/business-intelligence-dashboards-examples

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