which of the following describe the provess of a deciding where to locate data
Answers
Answer:
You need to know it is the right data for answering your question;
You need to draw accurate conclusions from that data; and
You need data that informs your decision making process
In short, you need better data analysis. With the right data analysis process and tools, what was once an overwhelming volume of disparate information becomes a simple, clear decision point.
To improve your data analysis skills and simplify your decisions, execute these five steps in your data analysis process:
Step 1: Define Your Questions
In your organizational or business data analysis, you must begin with the right question(s). Questions should be measurable, clear and concise. Design your questions to either qualify or disqualify potential solutions to your specific problem or opportunity.
For example, start with a clearly defined problem: A government contractor is experiencing rising costs and is no longer able to submit competitive contract proposals. One of many questions to solve this business problem might include: Can the company reduce its staff without compromising quality?
Step 2: Set Clear Measurement Priorities
This step breaks down into two sub-steps: A) Decide what to measure, and B) Decide how to measure it.
A) Decide What To Measure
Using the government contractor example, consider what kind of data you’d need to answer your key question. In this case, you’d need to know the number and cost of current staff and the percentage of time they spend on necessary business functions. In answering this question, you likely need to answer many sub-questions (e.g., Are staff currently under-utilized? If so, what process improvements would help?). Finally, in your decision on what to measure, be sure to include any reasonable objections any stakeholders might have (e.g., If staff are reduced, how would the company respond to surges in demand?).
B) Decide How To Measure It
Thinking about how you measure your data is just as important, especially before the data collection phase, because your measuring process either backs up or discredits your analysis later on. Key questions to ask for this step include:
What is your time frame? (e.g., annual versus quarterly costs)
What is your unit of measure? (e.g., USD versus Euro)
What factors should be included? (e.g., just annual salary versus annual salary plus cost of staff benefits)
Step 3: Collect Data
With your question clearly defined and your measurement priorities set, now it’s time to collect your data. As you collect and organize your data, remember to keep these important points in mind:
Before you collect new data, determine what information 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 process saves time and prevents team members from collecting the same information twice.
If you need to gather data via observation or interviews, then develop an interview template ahead of time to ensure consistency and save time.
Keep your collected data organized in a log with collection dates and add any source notes as you go (including any data normalization performed). This practice validates your conclusions down the road.
Step 4: Analyze Data
After you’ve 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. A pivot table lets you sort and filter data by different variables and lets you calculate the mean, maximum, minimum and standard deviation of your data – just be sure to avoid these five pitfalls of statistical data analysis.
Explanation:
Answer:
You need to know it is the right data for answering your question;
You need to draw accurate conclusions from that data; and
You need data that informs your decision making process
In short, you need better data analysis. With the right data analysis process and tools, what was once an overwhelming volume of disparate information becomes a simple, clear decision point.
To improve your data analysis skills and simplify your decisions, execute these five steps in your data analysis process:
Step 1: Define Your Questions
In your organizational or business data analysis, you must begin with the right question(s). Questions should be measurable, clear and concise. Design your questions to either qualify or disqualify potential solutions to your specific problem or opportunity.
For example, start with a clearly defined problem: A government contractor is experiencing rising costs and is no longer able to submit competitive contract proposals. One of many questions to solve this business problem might include: Can the company reduce its staff without compromising quality?
Step 2: Set Clear Measurement Priorities
This step breaks down into two sub-steps: A) Decide what to measure, and B) Decide how to measure it.
A) Decide What To Measure
Using the government contractor example, consider what kind of data you’d need to answer your key question. In this case, you’d need to know the number and cost of current staff and the percentage of time they spend on necessary business functions. In answering this question, you likely need to answer many sub-questions (e.g., Are staff currently under-utilized? If so, what process improvements would help?). Finally, in your decision on what to measure, be sure to include any reasonable objections any stakeholders might have (e.g., If staff are reduced, how would the company respond to surges in demand?).
B) Decide How To Measure It
Thinking about how you measure your data is just as important, especially before the data collection phase, because your measuring process either backs up or discredits your analysis later on. Key questions to ask for this step include:
What is your time frame? (e.g., annual versus quarterly costs)
What is your unit of measure? (e.g., USD versus Euro)
What factors should be included? (e.g., just annual salary versus annual salary plus cost of staff benefits)
Step 3: Collect Data
With your question clearly defined and your measurement priorities set, now it’s time to collect your data. As you collect and organize your data, remember to keep these important points in mind:
Before you collect new data, determine what information 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 process saves time and prevents team members from collecting the same information twice.
If you need to gather data via observation or interviews, then develop an interview template ahead of time to ensure consistency and save time.
Keep your collected data organized in a log with collection dates and add any source notes as you go (including any data normalization performed). This practice validates your conclusions down the road.
Step 4: Analyze Data
After you’ve 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. A pivot table lets you sort and filter data by different variables and lets you calculate the mean, maximum, minimum and standard deviation of your data – just be sure to avoid these five pitfalls of statistical data analysis.