Have you ever put together some analysis only to have your stakeholders say, “This is great, can I get this every month?”  Or have you ever thought, “this report takes too much time to build every week, I wish there was a way to shorten the amount of time I spend putting this report together?”  If you have, this episode is for you.  Tune in to hear some tips that will help you create repeatable analysis.

Timeline:
01:41 - The Goal of Analysis
04:54 - Minimizing Technical Debt
06:46 - Eliminate Manual Touches
09:28 - Consider How You Use Filters
11:08 - Combine Data Sources First
11:48 - Set Up a Framework or Template
13:02 - Use Consistent Naming Conventions
14:35 - Document Your Process
15:40 - The Last Record

The Last Record:

1. The goal of any analysis is to provide actionable insights.  Be sure to keep this in mind as you are building any reports
2. Strive to minimize the technical debt and technical interest.  That’s the time needed to re-do portions of any analysis in order to do it properly, or the time needed to refresh the data on a regular basis.
   • Avoid manual touches - this could be data pulls, or even hand keying in information into a spreadsheet.
   • Put filters up front, and remove as much data as possible in the first filter, and each subsequent filter should remove less and less data.
   • Set up a framework so you only have to update the raw data, and not rebuild a summary or data visualization
   • Watch out for your naming conventions
     • Be consistent with how you name your fields throughout your workflow
3. Document your steps and process.  Your future self will thank you for saving pain and heartache trying to figure out why you created the process the way you did.  

In this episode I’ll discuss some keys to remember when examining a dataset for the first time.

Timeline:
02:25 - Do #1 - Look at your data carefully
05:32 - Don’t #1 - Don’t try to bite off too much
07:32 - Do #2 - Look for patterns and trends
10:15 - Do #3 - Consider the data source
13:13 - Do #4 - Identify blind spots in your data
15:22 - Don’t #2 - Don’t include incomplete or missing data

Survey of Data Workers:
https://community.useready.com/whitepapers/idc-infobrief-state-of-data-science-and-analytics/?auto-trigger

The Last Record:

1. Look at the data carefully
   • Do you have all the fields that you will need?
   • Is it a number field?  Is it text?
2. Look for the patterns and trends
3. Consider the source
   • Are there controls in your data?
   • Are you pulling from an outside source that may not always be available or updated?
4. Identify the blind spots in your data
   • Think about the questions you can answer using this data.  
   • Are there any limitations?  If there are, you may need to supplement with additional data.

In this episode you’ll learn about the 4 main types of analytics and when to use each method in your own analysis.

Previous Episode:
https://datarockspodcast.buzzsprout.com/1276397/5866354-having-fun-with-data-sabermetrics-style

Timeline:
01:26 - Guideposts in Analytics
03:21 - Descriptive Analytics
05:26 - Diagnostic Analytics
06:55 - Predictive Analytics
10:04 - Prescriptive Analytics
11:05 - A Deeper Dive
17:56 - Which Method Should You Use?
19:22 - The Last Record

The Last Record:

1. The most important thing you can do as an analyst is help tell the story.  
2. The progression of analytics contains four main methods.  Descriptive (what happened); Diagnostic (why did it happen); Predictive (what will happen); and Prescriptive (what actions can I take based on what will happen).  With each step in the progression, the analysis becomes more difficult, but the payoff is with greater value.
3. We talked about an example in transportation where I walked you thru some of the questions and types of analysis you may encounter in each of the four methods.
4. In order to know what method to use, you have to know what data you have available and what you are capable of providing.  It’s important to listen to your stakeholders and understand the problem that you’re trying to solve, so you can provide the best solution possible.