5 Steps for Better Data Science Decision Making

In an organizational or business data analysis, you must Begin with the right question. Questions should be measurable, clear and concise. Design your questions to either qualify or disqualify potential resolution to your specific job or opportunity.

Step 2: Set Clear Measurement Priorities

Using the government declarer example, consider what sort of data you d need to answer your key inquiry. In this fount, you d need to know the number and cost of current faculty and the percentage of sentence they spend on necessary occupation functions.

Thinking about how you place your data is just as important, especially before the data solicitation phase, because your process either backs up or discredits your analysis later on.

Key questions to ask for this step includes

What is your time frame ?

What is your unit of measure ?

What factors should be included ?

Step 3: Collect Data

With your question clearly defined and your measure priorities set, now it s time to collect your information . As you collect and organize your data, remember to keep these important points in idea

Before you collect new data , determine what data could be collected from existing databases or sources on hand. Collect this data first.

Determine a file storing and naming system ahead of time to help all tasked team members collaborate. This cognitive process save time and prevents team members from collecting the same information twice.

If you need to gathering data via observance or audience s, then develop an interview template ahead of time to ensure consistency and save time.

Keep your collected data organized in a log with collection and dates and add any reservoir notes as you go including any data normalization performed . This practice validates your conclusions down the route.

Step 4: Analyze Data

After you have collected the right data to answer your question from Step 1, it s time for deeper data analysis. Begin by manipulating your data in a number of different ways, such as plotting it out and finding correlations or by creating a pivot table in Excel.

As you manipulate information , you may discovery you have the exact data you need, but more likely, you might need to revisal your original doubt or collect more data. Either way, this initial analysis of trends, coefficient of correlation , variations and outlier helps you focus your data analysis on better answering your head and any objections others might have.

During this stride , data psychoanalysis tools and software are extremely helpful. Visio, Minitab and Stata are all good software packages for advanced statistical data analysis. However, in most subjects , nothing quite compares to Microsoft Excel in terms of decision making tools.

Step 5: Interpret Results

After analyzing your data and possibly conducting further research, it s finally time to interpret your resultant. As you interpret your analysis, keep in mind that you cannot ever prove an assumumption true, rather, you can only fail to reject the hypothesis. Meaning that no matter how much data you collect, modify could always interfere with your results.

As you interpret the results of your data, ask yourself this key question

Does the data answer your original question?

Does the data help you defend against any objections?

Are there any limitations on your conclusions, any angles you have not considered?

If your interpretation of the data holds up under all of these questions and considerations, then you likely have come to a productive conclusion. The only remaining step is to use the results of your data analysis process to decide your best course of action.

Source: HOB

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