Identifying Data and Methods Assignment | Custom Assignment Help

Introduction
To proceed with your analysis, you’ll need data. Your assignment guides you through identifying whether you can already acquire that data and flagging where you have gaps. If you already know how to acquire all your data, you are done with this step and will not require this guide. However, if you have gaps, the tactics below can help you identify how to fill them.
Remember, this program encourages a lean results-oriented approach to analytics. You may find that, ideally, you would be able to access data that turn out in practice to be expensive or time-consuming to acquire. As you consider such data gaps, take a few minutes to consider how much insight you can learn from simpler data that you can mobilize quickly. This approach can help you accelerate progress, build your confidence incrementally, and can inform the value of acquiring further data later.
Below we summarize the most typical approaches that business leaders use to address data gaps. Feel free to employ other approaches as relevant to your Personal Case Project.
Step 1. Determine How You Will Address Each Gap
Consider the approaches below as you brainstorm about how you can fill data gaps you have identified.
Approach 1: Use an Available Subset to Stand in for the Full Data Set You Seek
Is there a subset of the data you seek that behaves similarly to how the whole set behaves regarding your outcomes but is practical for you to acquire?
For example, Erica’s analysis called for data on how often customers who visit your stores one month return the next month. That was not available. In its place, she chose to use instead data on a subset of customers. Specifically, she chose to analyze how often customers who paid with a credit card one month returned the next month and paid again with the same credit card. She could mobilize this data and felt it would behave regarding repeat visits as would the full data set. After all, she was interested in what changes repeat visits.
Approach 2: Use an Analogous Source
You may want data that is specific to your company or your region. Is similar data you want available for your overall industry or for another organization? Would it behave regarding your desired outcomes similarly to the data you initially wanted?
An industry association or data service provider may offer relevant data. Can you purchase it from them?
1.
Use an Available
Subset
2.
Use an
Analogous
Source
3.
Use a Proxy
4.
Run an
Experiment
5.
Explicitly Gather
Data
Guide to Identifying Data
ExecOnline – Leading Data & Analytics
Approach 3: Use a Proxy
Is there another indicator that behaves regarding your desired outcomes similarly to how the whole set behaves but is more practical for you to acquire?
For example, in the sales compensation example, Professor Misra noted the company wanted to get data on effort but couldn’t since that’s unobservable. Instead, their theory told them monthly sales were an outcome of effort. So, they used monthly sales as a proxy.
Approach 4: Run an Experiment
You may want data across a several different conditions you would like to test. It may be that your firm does not have experience with those conditions. Can you run a rapid experiment to gather the data you seek in a constrained way?
For example, ZipRecruiter wanted to assess demand at ten different price points. To do so, they ran a one-month experiment. This quick experiment led them to make a dramatic shift in their pricing.
Approach 5: Explicitly Gather Data
You always have the choice of actually gathering the data you seek. Depending on what that data is, such an effort can be expensive and time-consuming.
If you are considering this option, you may wish to test your analysis first by gathering a subset of the data you require. For example, if your theory requires data on, e.g., customer demographics which you cannot otherwise mobilize, you could manually gather, e.g., 5-20 data points at random and run an initial analysis with those. If the results appear promising, you could then consider launching a more extensive effort to uniformly acquire the data you want. And, if the results indicate that your sample data end up being far out of the range that they would need to be to shift paths regarding your business challenge, you will have saved your company significant effort.
Step 2. Capture Your Thoughts
After you use this guide and have identified how you will address any gaps you have, take a minute to reflect on what you have determined.
• If you do not face gaps and can readily mobilize the full set of data you require, note in your workbook under Step 1 the steps you will follow to do so.
• If you can readily mobilize adequate data after addressing gaps, note under Step 2 your plan for how you will address each gap, along with the steps you will take to gather this data.
• If you cannot mobilize the data that you’ll need to complete your analysis, even after considering the approaches below, you’ll need to limit yourself during the remainder to testing your analysis. That test will help you structure how to pursue a more complete analysis after the program.
o Note in your workbook under Step 4 that you will test your analysis.
o To run your test, you’ll want some data. Use the ideas below to determine what data you can mobilize to test, even if it’s only by manually collecting a few illustrative data points to take an initial rough stab at your analysis. Capture your thoughts in your workbook.

Introduction
To proceed with your analysis, you’ll need data. Your assignment guides you through identifying whether you can already acquire that data and flagging where you have gaps. If you already know how to acquire all your data, you are done with this step and will not require this guide. However, if you have gaps, the tactics below can help you identify how to fill them.
Remember, this program encourages a lean results-oriented approach to analytics. You may find that, ideally, you would be able to access data that turn out in practice to be expensive or time-consuming to acquire. As you consider such data gaps, take a few minutes to consider how much insight you can learn from simpler data that you can mobilize quickly. This approach can help you accelerate progress, build your confidence incrementally, and can inform the value of acquiring further data later.
Below we summarize the most typical approaches that business leaders use to address data gaps. Feel free to employ other approaches as relevant to your Personal Case Project.
Step 1. Determine How You Will Address Each Gap
Consider the approaches below as you brainstorm about how you can fill data gaps you have identified.
Approach 1: Use an Available Subset to Stand in for the Full Data Set You Seek
Is there a subset of the data you seek that behaves similarly to how the whole set behaves regarding your outcomes but is practical for you to acquire?
For example, Erica’s analysis called for data on how often customers who visit your stores one month return the next month. That was not available. In its place, she chose to use instead data on a subset of customers. Specifically, she chose to analyze how often customers who paid with a credit card one month returned the next month and paid again with the same credit card. She could mobilize this data and felt it would behave regarding repeat visits as would the full data set. After all, she was interested in what changes repeat visits.
Approach 2: Use an Analogous Source
You may want data that is specific to your company or your region. Is similar data you want available for your overall industry or for another organization? Would it behave regarding your desired outcomes similarly to the data you initially wanted?
An industry association or data service provider may offer relevant data. Can you purchase it from them?
1.
Use an Available
Subset
2.
Use an
Analogous
Source
3.
Use a Proxy
4.
Run an
Experiment
5.
Explicitly Gather
Data
Guide to Identifying Data
ExecOnline – Leading Data & Analytics
Approach 3: Use a Proxy
Is there another indicator that behaves regarding your desired outcomes similarly to how the whole set behaves but is more practical for you to acquire?
For example, in the sales compensation example, Professor Misra noted the company wanted to get data on effort but couldn’t since that’s unobservable. Instead, their theory told them monthly sales were an outcome of effort. So, they used monthly sales as a proxy.
Approach 4: Run an Experiment
You may want data across a several different conditions you would like to test. It may be that your firm does not have experience with those conditions. Can you run a rapid experiment to gather the data you seek in a constrained way?
For example, ZipRecruiter wanted to assess demand at ten different price points. To do so, they ran a one-month experiment. This quick experiment led them to make a dramatic shift in their pricing.
Approach 5: Explicitly Gather Data
You always have the choice of actually gathering the data you seek. Depending on what that data is, such an effort can be expensive and time-consuming.
If you are considering this option, you may wish to test your analysis first by gathering a subset of the data you require. For example, if your theory requires data on, e.g., customer demographics which you cannot otherwise mobilize, you could manually gather, e.g., 5-20 data points at random and run an initial analysis with those. If the results appear promising, you could then consider launching a more extensive effort to uniformly acquire the data you want. And, if the results indicate that your sample data end up being far out of the range that they would need to be to shift paths regarding your business challenge, you will have saved your company significant effort.
Step 2. Capture Your Thoughts
After you use this guide and have identified how you will address any gaps you have, take a minute to reflect on what you have determined.
• If you do not face gaps and can readily mobilize the full set of data you require, note in your workbook under Step 1 the steps you will follow to do so.
• If you can readily mobilize adequate data after addressing gaps, note under Step 2 your plan for how you will address each gap, along with the steps you will take to gather this data.
• If you cannot mobilize the data that you’ll need to complete your analysis, even after considering the approaches below, you’ll need to limit yourself during the remainder to testing your analysis. That test will help you structure how to pursue a more complete analysis after the program.
o Note in your workbook under Step 4 that you will test your analysis.
o To run your test, you’ll want some data. Use the ideas below to determine what data you can mobilize to test, even if it’s only by manually collecting a few illustrative data points to take an initial rough stab at your analysis. Capture your thoughts in your workbook.