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50% of professionals are feeling overwhelmed at the pace of change in the workplace. So, why do so many gurus place the burden of adapting on the employee?         New LinkedIn Data (and our own experiences) show the workplace is changing faster than ever before. https://lnkd.in/gugD5MxZ   Beyond the overwhelm, 38% of professionals say they don't know where to start making changes to keep up.   The advice from some gurus? "Adopt a growth mindset" "Keep up with what people are talking about"   Is this just code-speak for "figure it out on your own"?   It seems that's how we got to this place.   The workplace is changing as fast as it is because leadership keeps falling for the old shiny-object syndrome.   Lots of new "sexy" tools (looking at you AI)....and not a lot of direction for what to do with them.   Staff are being asked to do things they've never done before, with tools they've never used before.   If you want to prevent your team from becoming overwhelmed, here are a few suggestions: Do not throw new technology into their day-to-day lives without planning what you want to do with it. Do not leave it up to your employees to learn how to use new technology. Understand that stress is made worse when it is coupled with uncertainty. If your staff are worried that AI will take their jobs (and they probably are), help reduce the uncertainty by being transparent about plans and coming changes.   Why do so many feel overwhelmed right now?   Because leaders created this context by introducing lots of uncertainty into the workplace.   It doesn't have to be this way.   Like any analytic project or technical solution, it takes careful planning, development, testing, and refinement to integrate these solutions.   Why aren't we doing the same with AI and other new tools?   #f1analytics Training Leaders to Use Analytics without Learning Statistics or Programming

Don't let your data quality be the barrier stopping you from implementing analytics in your organization! We've all heard the phrase "garbage in = garbage out." While this is a true statement, the following is also true: "imperfect data is not garbage." Most data collected for analytics is "okay." It's not perfect. There are nuances that present challenges. There are idiosyncrasies that must be dealt with to produce accurate results. This is the whole purpose of data cleaning, which every analyst will tell you is a key step in the analytic process. And from all of this "okay" data, we learn loads of information about everything from the laws of the universe to which subject line in an email is most likely to result in a consumer reading your message. Data isn't usually "garbage" or "perfect." Data represent a continuum of quality. Sure...some data might be "garbage" for some purposes. But you figure that out along the way and fix it. Collecting high quality data is a process, not an event. Your data systems will require tweaking and editing over time. That's a standard circumstance of analytics. So if the quality of your data has been holding you back from implementing analytics....don't. Just start. #f1analytics Training Leaders to Use Analytics without Learning Statistics or Programming

📢Calling All Leaders! Want to be able to speak the same language as data scientists? Let me know the analytic topics that are most important for all leaders to understand!         #f1analytics Training Leaders to Use Analytics without Learning Statistics or Programming

Analytics isn't strictly the domain of companies like Apple, Amazon, Google, and Meta. Sometimes the challenges that larger organizations face with analytics are actually EASIER to handle in small- and medium-sized organizations.         #f1analytics Training Leaders to Use Analytics without Learning Statistics or Programming

All metrics have a level of measurement that plays an important role in how you analyze the data. Here is a short refresher on this critical concept.        #f1analytics Training Leaders to Use Analytics without Learning Statistics or Programming

Analytics isn’t just a cost center; it should be increasing revenue, reducing costs, and improving efficiency. But here are some hidden considerations for ROI on analytics.         #f1analytics Training Leaders to Use Analytics without Learning Statistics and Programming

Data literacy is a ridiculous concept...here's the one sentence in a report that convinced me of this like a lightning bolt!         "....many organisations are starting to use 'data fluency' in place of 'data literacy' - to emphasize he depth and breadth of data skills need in an organization, rather than risk creating or reinforcing a false binary perception of 'data literate' / 'not data literate'." - Deloitte, 2022 Chief Data Office Survey   People develop different proficiency levels in working with data. Different levels of fluency.   For some, it’s reading results.   For others, it’s planning projects and knowing which analyses will work best.   For the most fluent, it’s understanding the complex math and programming to execute a project.   As you think about the role of data and analytics in your organization, consider the degree of data fluency needed for each staff member.   Don't think about what they do now that appears successful.   Think about how they might be able to use data and analytics to do even better.   Then ask these questions. Do they need to know: How to read analytic results accurately? How decide which analyses are needed? How to analyze data and execute projects?   These are three different scopes of knowledge. Three different levels of fluency.   If you have analysts on your team, most professionals may only need the fluency to achieve #1 and #2.   If your team has to work with the data themselves, then they will need some fluency for #3.   Taking your team to next-level performance with data will require a careful assessment of your data fluency needs.   And if you want to know where your people stand now...a great tool to use is the F1 Analytics Data Literacy Assessment (I know, I need to change the name!).   You can download a free copy here: Https://https://lnkd.in/gWYqvXwW   #f1analytics Training Leaders to Use Analytics without Learning Statistics or Programming

When presented with the percentage change in a metric, always ask what the base rate is. The base rate puts percentage change in context. Let me explain...         Think about two scenarios: 1. The hospital mortality rate increases 3% 2. The hospital mortality rate increases 300%   Which one of these is better? It's easy to think that #1 is the better scenario.   But, what if #1 was a national figure for the US? Over 700,000 people die in hospitals each year in the US. A 3% increase would mean 21,000 more deaths.   What if #2 was a local figure for a small regional hospital? If that hospital typically experienced 2 deaths per month... A 300% increase would mean 6 deaths in a month.   There 3 reasons why #1 is the more problematic scenario: 1. The increase is on a national level, suggesting there may be a systemic issue. 2. The sheer number of additional deaths is far greater 3. The additional deaths in #2 are analytically within a range that might happen with some regularity over time.   To be fair, our small regional hospital should still investigate the root cause of the increased number of deaths to ensure the pattern does not continue. Death is still death and should be of concern.   However, anytime your base rate is below 100 events/objects/people, an increase or decrease of 1 will produce a percentage change that is greater than 1 percent.   Similarly, when your base rate is very large, even small percentage changes can have dramatic impacts on large numbers of events/objects/people.   The next time someone presents you with a HUGE percentage change score, or a small change of "ONLY X%", make sure you ask what the underlying base rate is.   How MANY does that percentage change impact?   #f1analytics Training Leaders to Use Analytics without Learning Statistics or Programming

Hurricane Helene dropped 40 trillion gallons of water on the US. Here's what that means in terms of New York Cities....         Some analytic findings are so big and incomprehensible that they need to be expressed in practical, everyday terms.   Researchers recently released findings that Hurricane Helene dropped 40 trillion gallons of water.   That's about 5,347,222,222,222 cubic feet of water (i.e., 5.3 trillion cubic feet if you don't like commas).   The Empire State Building is roughly 37,000,000 cubic feet.   That means Hurricane Helene dropped approximately 144,520 Empire State Buildings worth of water.   Still not entirely clear?   There are roughly 120,000 city blocks in New York City.   The Empire State Building's footprint covers half of a city block.   That means if you put an Empire State Building on every block in NYC, you would have 24,520 blocks with two ESBs....   For a comparison to reality, NYC currently has only 421 skyscrapers (150m or taller, at least 40 floors), and roughly 7,000 high-rise buildings (35m or taller, at least 9 floors).   FYI - the ESB is 102 floors.   A simpler way to express Helene's rainfall is that she dropped about 200 New York Cities worth of water (we're using back-of-the-napkin math here).   40 Trillion gallons is a huge amount of water....but the impact is staggering when put in practical terms.   #f1analytics Training Leaders to Use Analytics without Learning Statistics or Programming