WHY DATA ANALYTICS?

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Data Analytics is turning observation and information into insight. Data is all around us-financial data, workforce data, demographic data, geographic data; there’s even data about data. And it’s often messy, complicated, and incomplete. Data Analytics is the process of taking all of these data points and presenting your findings effectively to tell a story; a story that examines why something happened in the past, a story that justifies a decision to be made in the present, or a story that forecasts the future. From predicting terrorism patterns to driving diversity recruitment strategies, Data Analytics is here to stay as a vital tool in an increasingly connected world.

The best part about working with data is seeing the impact it can have in an organization.

Data Strategies

There are a variety of strategies for quantitative and qualitative analyses, many of which go well beyond the scope of an introductory Handbook. Different strategies provide data analysts with an organized approach to working with data; they enable the analyst to create a “logical sequence” for the use of different procedures

we offer four examples of strategies for quantitative analysis that you may consider as

you work with and develop your skills in data analysis as well as reasons why you may

consider using the strategy. Some of these strategies are used when looking

at particular content area data.

Strategy: Visualizing the Data

Involves: Creating a visual “picture” or graphic display of the data.

Reason(s): a way to begin the analysis process; or as an aid to the reporting/

presentation of findings.

Strategy: Exploratory Analyses

Involves: Looking at data to identify or describe “what’s going on”? – creating an

initial starting point (baseline) for future analysis.

Reason(s): Like you have a choice?

Strategy: Trend Analysis

Involves: Looking at data collected at different periods of time.

Reason(s): to identify and interpret (and, potentially, estimate) change.

Strategy: Estimation

Involves: Using actual data values to predict a future value.

Reason(s): to combat boredom after you have mastered all the previous strategies.


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