Data Rocks

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 Analysis04:54 - Minimizing Technical Debt06:46 - Eliminate Manual Touches09:28 - Consider How You Use Filters11:08 - Combine Data Sources First11:48 - Set Up a Framework or Template13:02 - Use Consistent Naming Conventions14:35 - Document Your Process15:40 - The Last RecordThe Last Record:The goal of any analysis is to provide actionable insights. Be sure to keep this in mind as you are building any reportsStrive 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 visualizationWatch out for your naming conventionsBe consistent with how you name your fields throughout your workflowDocument 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 carefully05:32 - Don’t #1 - Don’t try to bite off too much07:32 - Do #2 - Look for patterns and trends10:15 - Do #3 - Consider the data source13:13 - Do #4 - Identify blind spots in your data15:22 - Don’t #2 - Don’t include incomplete or missing dataSurvey of Data Workers:https://community.useready.com/whitepapers/idc-infobrief-state-of-data-science-and-analytics/?auto-triggerThe Last Record:Look at the data carefullyDo you have all the fields that you will need?Is it a number field? Is it text?Look for the patterns and trendsConsider the sourceAre there controls in your data?Are you pulling from an outside source that may not always be available or updated?Identify the blind spots in your dataThink 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-styleTimeline:01:26 - Guideposts in Analytics03:21 - Descriptive Analytics05:26 - Diagnostic Analytics06:55 - Predictive Analytics10:04 - Prescriptive Analytics11:05 - A Deeper Dive17:56 - Which Method Should You Use?19:22 - The Last RecordThe Last Record:The most important thing you can do as an analyst is help tell the story. 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.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.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.

Description:In this episode, learn one way that you can have fun with data. We’ll discuss Sabermetrics, and how using data can enhance your enjoyment of the game of baseball.Timeline:00:55 - Welcome to the Show03:05 - Why I Love Baseball04:36 - A Few Baseball Rules07:58 - A Pivotal Moment in Baseball12:00 - Basic Statistics15:34 - More Contributions from Bill James17:05 - Lasting Effects on the Game22:40 - The Last RecordUseful Links:Retrosheet.orgBaseball-Reference.comFangraphs WARBaseball-Reference WARThe Last Record:Data can be fun, and there are so many questions that you can answer using data. In baseball that means questions like…Do better hitters get fewer good pitches to hit?Or what impact does the composition of the ball have on a hitter’s performance?This leads to the 2nd key takeaway that data can enhance your understanding of something that you enjoy, even if it is only a hobby. And sometimes, if you enjoy the data that much, it can even lead you to a career.Data often has gaps. Advanced statistics were created to try isolating the performance of the player being analyzed. In your own analysis, it is important to be cognizant of the gaps that may be present.

In this episode you’ll learn 5 tips to help you solve problems more efficiently and with greater ease.Timeline:00:54 - Intro02:08 - Today’s Interesting Data Fact03:06 - Ask Great Questions07:58 - Embrace a “Could-Do” Mindset11:04 - Work Backwards14:54 - Break the Obstacle Down18:06 - Take a Break21:53 - The Last RecordToday’s Interesting Data Fact:https://www.swnsdigital.com/2017/12/kids-ask-a-staggering-73-questions-every-day-half-of-which-mums-and-dads-struggle-to-answer/The Advantage:https://www.amazon.com/Advantage-Organizational-Health-Everything-Business/dp/1491510803The Last Record:Ask great questions - this helps you get to the root of the problem, and define what you’re solving. Examples of great questions:Can you describe the situation in more detail and why is this happening?When and how often does this problem occur?What is the impact of solving this problem?What additional context should I be aware of?Embrace “could do” mindset. Don’t just stop at the obvious solution, or settle for the way it’s always been done, especially if there is a better way. The key to coming up with the best solution is to consider all the available options and implement the one that is going to give you the greatest impact.Work backward. Starting at the end can help make the problem solving process easier. Don’t be afraid to steal from your previous work or emulate others who have solved a similar problem. One of the best ways to gain knowledge for future problems is to pick apart others’ solutions.Break the problem down into more manageable pieces. Don’t try to map out the entire solution. Instead, focus on what is most important right now, and work towards getting quick wins to build momentum.Don’t rush the incubation period. Go for a walk, get a snack, or hydrate. Keep your mind from thinking of biological needs, and free yourself up to focus on the problem. Ensure that you have some quiet time to think and come up with the best solution possible.

Learn how to use a proven and repeatable framework for approaching analysis. I’ll walk you through the 5 stages of design thinking and give some specific examples that you can implement in your analysis.Last Episode: https://datarockspodcast.buzzsprout.com/1276397/5224414-what-is-data-and-what-is-the-big-dealTimeline:00:53 - Intro06:03 - Stage 1: Empathy09:30 - Stage 2: Define14:32 - Stage 3: Ideate / Brainstorm15:37 - Stage 4: Prototype17:50 - Stage 5: Test18:58 - The Last RecordToday’s Interesting Data Fact:https://uxdesign.cc/3-things-you-probably-didnt-know-about-design-thinking-and-why-they-matter-for-business-5fa89b885c9cSNL Skit:https://www.youtube.com/watch?v=cVsQLlk-T0s&t=154sFor Further Reading:https://www.interaction-design.org/literature/article/5-stages-in-the-design-thinking-processhttps://uxplanet.org/its-an-approach-not-a-process-66e348489fe1Key Information Checklist:ScopeBroad: Display information about the entire organization.Specific: Focus on a specific function, process, product, region, etc.Business RoleStrategic: A high-level, broad, and long-term view of performanceOperational: A focused, near-term, tactical view of performanceTime HorizonHistorical: Looking backwards to track trendsSnapshot: Showing performance at a single point in timeReal-Time: Monitoring activity as it happensPredictive: Using past performance to predict future performanceCustomizationOne-size-fits-all: Presented as a single view for all usersCustomizable: Let users create a view that reflects their needsLevel of DetailSummary: Presenting only the most critical top-level informationDrillable: Providing the ability to drill down to detailed numbers to gain more contextPoint of ViewPrescriptive: The analysis specifically tells the user what the data means and what to do about itExploratory: Users have the latitude to interpret the results as they see fitThe Last Record:Design thinking is an iterative process and doesn’t have to be so rigid. If you’ve worked with a group long enough that you know their tendencies, feel free to shorten the empathize stage. Also, if you’ve gained consensus with your first sketch, don’t worry about sketching more advanced prototypes.Give them what they need, not what they want. It’s important that you solve the problem and set up your audience for success. By giving them what they want rather than what they need, you open the door to scope creep and questions down the road.Make sure you understand the key information needed during the define stage. See notes above. Also, be sure to repeat the question back using, “What I am hearing is...”Wire-framing is a helpful tool when trying to gain consensus on a project. The key is to get this into the hands of your stakeholders as fast and early as possible so that you get alignment, and can start gathering the data needed to provide your solution.During the testing phase, you want the user to try all possible scenarios and show you where there are problems, not simply tell you that it’s broken.

In this episode: Learn about the different types of data, how it is stored, examples of data that you encounter every day, and a brief teaser for Big Data.Timeline for the show:00:54 - Welcome to the Show!01:22 - A Little Bit About Me04:18 - What to Expect From This Podcast08:05 - Today's Interesting Data Fact09:07 - What Is Data?14:54 - All About Data Storage17:40 - Daily Data Examples22:40 - What is Big Data?26:06 - The Last RecordToday's Interesting Data Fact:http://rcnt.eu/un8bgThe Last Record:Data is nothing more than a story waiting to be toldIt takes analysts to give it meaning and purpose and unveil the patterns and trendsWe talked about both structured and unstructured dataThe different data elements within both of these like strings and integers on structured data and images and audio clips on unstructured dataWe also discussed metadata which is the data behind the data, and how metadata can show you the shape of your dataWe discussed storage options for your dataLow, medium, and high tech examples of how data might be gatheredHow it might be archived, whether it’s a snapshot or archived on a scheduleThere are various ways you can encounter data in your daily lifeSuch as, in your calendar, email, checking the weather, using a navigation app, online shopping, or looking for something to watch on TVAnd lastly, we talked about big data and the internet of thingsBig data is extremely large data setsSome of the examples of the internet of things are having smart light bulbs or appliances

Here’s a sneak peek at what you can expect from Data Rocks. Find me on Instagram: https://www.instagram.com/datarockspodcast/Episodes coming soon!