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BY BEN PRESCOTT, AHEAD - This article originally appeared on AHEAD's i/o blog
Recall the year 2013. The world has just topped four zettabytes of generated data (equal to four trillion gigabytes). The term “big data” is taking hold, creeping into every corner of the tech and business worlds.
From that point on, data volumes have grown exponentially. It’s estimated that the world produced more than 44 zettabytes of data by the end of 2020 (growing by a factor of 11 in just seven years). We’re now generating more data than we can effectively manage—and it’s a big problem.
Today’s organizations are searching for ways to harness, manage, and analyze these huge influxes of information. Many are turning to machine learning (ML) and deep learning (DL) models to analyze data and predict what could happen next. In fact, organizations across every industry use ML and DL today in various capacities.
Healthcare innovates pathology detection by leveraging neural networks and tracking patient patterns to predict their length of stay. The pharmaceutical industry uses ML and DL to identify causal factors of diseases for drug development and predict patient response to drug combinations. Manufacturing organizations identify product defects, predict machine malfunctions before they even happen to provide proactive maintenance, and use generative design methods to determine the best structural design for a product (such as a car frame). From a general business perspective, many organizations leverage ML to aid in forecasting business revenue, schedule resources for projects, and drive sales and marketing efficiency.
But building and training effective machine learning models is no easy task. Add the maintenance (operations) component and achieving true machine learning operations (MLOps) is difficult for most organizations.
That’s why a platform like Azure Machine Learning can be beneficial. Azure ML is a hosted service that enables and enhances an organization’s MLOps capabilities through four key benefits:
1. Graphical interface
2. Out-of-the-box machine learning models
3. Single pane of glass view
4. Direct integration with Git and Azure DevOps
Before we dive into the ins and outs of Azure MLOps, let’s define MLOps.
What is MLOps?
MLOps blends DevOps methodologies and processes with the ML development process. There’s a lot that goes into the process of developing ML models, from collecting and cleaning data, to model training, validation, and deployment. But, while the goal is to deploy a model into production, monitoring and maintaining that model are just as, if not more, critical.
Within the DevOps umbrella are concepts known as Continuous Integration and Continuous Delivery (CI/CD), a way to shorten development and deployment cycles while maintaining quality code. While DevOps plays a critical role in core application development, MLOps puts a twist on CI/CD. Within MLOps, CI validates data quality and structure, and CD broadens focus to the full ML solution.
MLOps also introduces a new concept—pushing the deployed ML model back through the training and validation process, often referred to as Continuous Training (CI/CD/CT). This is a way of ensuring the model doesn’t become stale, having only “seen” aging data. It also continually evaluates and improves a model’s ability to predict. After all, the last thing you want is to make business decisions from a model that provides poor predictions or recommendations.
MLOps with Azure Machine Learning
One of the biggest struggles of adopting MLOps is piecing together the services needed to support the full data lifecycle. Data scientists and machine learning engineers are traditionally really good at building robust models, but the process of deployment and monitoring is usually done manually, or not at all.
Once a model is in production and consuming new data, how is it monitored and retrained? The retraining process quickly becomes manual—going back through the same steps performed during development before pushing the retrained model back into production. As soon as it’s back in production, it’s out-of-date again. As with any iterative process, this becomes time-consuming, expensive, and frustrating.
Azure ML supports MLOps by giving teams a platform to manage models and integrate model usage, output, and insights across the organization. It does this through the areas we mentioned earlier—a graphical interface, out-of-the-box algorithms, single pane of glass approach, and integration capabilities.
Azure ML provides both a graphical web interface (Azure ML Studio) as well as SDKs that (at the time of writing) support Python, R, and Azure CLI. This is important because some organizations prefer to work in their existing environments while leveraging the features and capabilities of Azure ML.
Azure ML comes with many pre-built algorithms to help you get started quickly, including those for regression, text analysis, recommendation services, and computer vision. In addition, Azure ML Studio boasts “Automated ML,” a no-code solution to automatically find, train, and tune the best model for your data.
Single Pane of Glass
Azure ML takes a single pane of glass approach that provides capabilities in all areas of the MLOps lifecycle, while integrating with common DevOps services. Each capability within Azure ML can operate as an independent feature to help gradually grow MLOps maturity. Whether it’s data scientists making use of the notebooks and experimentation, infrastructure engineers managing the CPU or GPU-backed compute infrastructure, or security teams leveraging Azure ML’s model change tracking logs, everyone has a role to play in embracing an MLOps methodology. Azure ML helps make this transition easier by providing ready-to-be-consumed services.
Integration with Git and Azure DevOps
Integrating Azure ML with Git and Azure DevOps offers many automation opportunities that are otherwise not accessible:
At the end of the day, it’s better to back your MLOps with tools that will get you there better and faster, and that’s just what the Azure ML platform does.
To learn more about how Azure ML can help your organization leverage machine learning, reach out to our team.
Mardi Humphreys, Change Agent, Integration Edge/RDSI
I’m a storyteller and I love data analytics. These two things may seem mutually exclusive, but bear with me. Data analysis is a process. So is storytelling. Data analysis inspects, cleanses, transforms, and/or models data. So does storytelling. You use data analytics to discover useful information, form conclusions, and support decision-making. So is… never mind; you know what goes here. Let me give you an example. Here’s the story of Goldilocks and the Three Bears as if they were all data analysts.
Goldilocks finds an empty-looking cabin in the woods. She peeps in the window. She doesn’t see anyone inside. She knocks on the door. No one answers. She turns the knob and finds the door unlocked. Given this data, she decides to enter the cabin. The data she’s missing? The cabin belongs to a family of three bears: Papa, Mama, and Baby. The Bear family goes out to pick fresh blueberries every morning.
Goldilocks gathers more data. There are three various-sized bowls of porridge on the dining room table. She tastes the contents of the biggest bowl. It burns her mouth. She tastes a spoonful from the medium-sized bowl. It’s cold. She tastes the porridge from the smallest bowl and finds it palatable. With this data, she discovered enough useful information to form a conclusion. She decides to eat the whole small bowl of porridge and leave the other two sitting.
Goldilocks continues being nosy, er, I mean, gathering data. She wanders into the living room and spies three chairs. Full of porridge, she decides sitting a spell is a wise choice. She tries out Papa Bear’s large chair. She determines it is too hard to sit on. Next, she tries Mama Bear’s medium-sized chair. She determines it is too soft to sit on. Sticking to her data-gathering process, she moves on to Baby Bear’s tiny chair and gives it a sit. It’s perfect! She’s so excited her experiment worked, she does a happy dance and promptly breaks the chair. (Side note - This is why we never test anything in production.)
All this porridge eating and chair breaking made Goldilocks sleepy. She again wanders around the cabin; this time looking for a bed in which to nap. Refining and iterating her process based on feedback, she is not surprised to find three various-sized beds on the second floor. Continuing her data gathering process, she plops onto the ginormous Bed #1. She about breaks her back because it’s as hard as steel. Sticking to her method, she moves on to the medium-sized Bed #2. After wallowing in the piles of blankets and pillows, she decides it’s too soft. She moves on to test the smallest, Bed #3. It must have met her search criteria, because she falls asleep in it.
Not long after, the owners of the cabin return from blueberry picking to find things are not as they left them. The data they first encounter is the dining room table with two bowls of half-eaten, Goldilocks-germ-infested porridge on it and one tiny bowl licked clean. This data leads them to believe someone’s been eating their porridge. This is a correct analysis of their collected data.
The Bear family goes to the living room to see if there is more data to collect. They find their three chairs not as they left them. The big hard chair and the medium-sized soft chair are salvageable, but inspection of the smallest chair deems it transformed beyond repair. This additional data leads them to conclude a hungry vandal entered their house and may still be there. Another correct analysis of the collected data.
Not finding anyone on the first floor, the Bear family angrily stomps upstairs to see if there is more data (or a full-bellied, chair-destroying vandal) to be collected. They find their three beds not as they left them. The biggest and medium-sized beds are now unmade and rumpled, but the covers on the smallest bed are rhythmically moving and snoring. This aggregated data leads the Bear family to the conclusion that the culprit who entered their cabin, ate their food, and broke their chair is now sleeping in their bed. Acting on their conclusion, the three angry bears roar and Goldilocks awakens.
What have we learned about data analytics from this story?
Goldilocks’ Data Analysis:
What insight did she gain? The cabin offered three sizes of food, chairs, and beds
What understanding did it build? The third option always suited her best
What decisions did it influence? Eat, sit, sleep
The Bears’ Data Analysis:
What insight did they gain? They never had to lock the cabin door before
What understanding did it build? If they leave the cabin door unlocked, they may get robbed
What decisions did it influence? Whether or not to lock the cabin door
How did she use her collected data? To ransack The Bears’ cabin
What innovation did she achieve? She learned not to make herself at home in a home that wasn’t hers
The Bears Results:
How did they use their collected data? To find the culprit who vandalized their home
What innovation did they achieve? They discovered Goldilocks made a better meal than porridge and blueberries
Okay, that last conclusion is pure speculation on my part, but you see what I’m getting at. Data analysis is rich inspiration for storytelling. You help your clients visualize their choices when you include storytelling in your design, development, and deliverable.
Kathy Vogler, Communications Manager, Expedient Technology Solutions
Early on in my career, I was told by a boss that my intuition was a gift and that I should always trust my guts in every decision I made. That advice has really worked for me most of my life. However, the new reality in a data-driven culture embraces the use of data in decision-making.
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” ~ Jim Barksdale; President and CEO of Netscape 1995 - 1999
Maybe my mind captures, cleans and curates meaningful data. But the volume of our organizational data is much more than my job or my thoughts. Our culture is supported by data-driven decision making and the data holds our teams accountable.
The literacy level of any team member to leverage the data at hand and turn that into appropriate decisions is key. Hence a systematic approach to analysis of the data and reporting that is understandable and actionable is of utmost importance. The staggering amount of data that we store and analyse means that we need to meet often to review data findings, choosing what needs to be measured and what metrics will be implemented. Our data wizards have created detailed metrics on our customers’ experience which helps our team deliver wow.
No Departmental Silos Allowed
Departments tend to focus on the data that affects them directly, and rightfully so. But this sometimes creates a logjam and we become ignorant to the language of data that is being interpreted by each team. If we tie every team in an explicit and quantitative level, it helps us to understand if we have enough data for a reliable model to make intelligent decisions. We need to evaluate uncertainty by testing and reevaluating the data collectively. It’s been said that promising ideas greatly outnumber practical ideas. Proof of concepts will determine if the idea is viable in production. The immediate goals directly affect each team member by saving time and avoiding rework with readily accessible knowledge at their fingertips. Metrics should be universal and each team should take ownership of interpreting their data to help the literacy of the enterprise.
Measure what you Should, not what you Can
More data doesn’t guarantee better decisions, but it is always better to start with data. Better decisions almost always begin with better informed teams. And it’s our duty as team members to ask questions. And so I bring up skewed data …
1 Some distributions of data, such as the bell curve or normal distribution, are symmetric. This means that the right and the left of the distribution are perfect mirror images of one another. Not every distribution of data is symmetric. Sets of data that are not symmetric are said to be asymmetric. The measure of how asymmetric a distribution can be is called skewness.
The mean, median and mode are all measures of the center of a set of data. The skewness of the data can be determined by how these quantities are related to one another.
One measure of skewness, called Pearson’s first coefficient of skewness, is to subtract the mean from the mode, and then divide this difference by the standard deviation of the data. The reason for dividing the difference is so that we have a dimensionless quantity. This explains why data skewed to the right has positive skewness. If the data set is skewed to the right, the mean is greater than the mode, and so subtracting the mode from the mean gives a positive number. A similar argument explains why data skewed to the left has negative skewness.
Pearson’s second coefficient of skewness is also used to measure the asymmetry of a data set. For this quantity, we subtract the mode from the median, multiply this number by three and then divide by the standard deviation.
X = mean value
Mo = mode value
S = standard deviation of the sample data
Skewed data arises quite naturally in various situations. Incomes are skewed to the right because even just a few individuals who earn millions of dollars can greatly affect the mean, and there are no negative incomes. Similarly, data involving the lifetime of a product, such as a brand of light bulb, are skewed to the right. Here the smallest that a lifetime can be is zero, and long-lasting light bulbs will impart a positive skewness to the data.
1 Taylor, Courtney. "What Is Skewness in Statistics?" ThoughtCo, Aug. 25, 2020, thoughtco.com/what-is-skewness-in-statistics-3126242.
It’s important to take note of skewness while assessing your data since extreme data points are being considered. Take into consideration the extremes for current logic instead of focusing only on the average which provides a better picture of the future logic. Flawed data analysis leads to flawed conclusions which often result in poor business decisions.
I do trust my instincts, but my reporting to others is much better with statistical data.
Uptime Solutions & Vertiv
It’s not an exaggeration to say the global pandemic created a new healthcare delivery model virtually overnight. Telehealth, previously a simmering patient engagement option, has now erupted.
The global telehealth market is expected to grow dramatically, reaching $266.8 billion by 2026 and showing a compound annual growth rate (CAGR) of 23.4% between 2018 and 2026. The biggest barrier to pre-pandemic adoption was behavioral inertia. Now due to COVID-19, momentum is building toward a marked transformation.
Understandably, most healthcare providers are struggling to keep pace. Existing IT systems, infrastructure, and security and privacy protocols already were stretched or outdated due to the proliferation of diversified healthcare systems. Telehealth adds another layer of complexity requiring new IT strategies and investments.
Read our white paper to learn more about the critical decisions that are looming for healthcare IT managers and chief information officers (CIOs).
Download the White Paper at the Vertiv Blog
Governor's Office of Workforce Transformation
The next round of TechCred opens on June 1st and closes on June 30th at 3:00 p.m.
Ohio businesses can visit TechCred.Ohio.gov to apply and help their employees earn a short-term, technology-focused credential at no cost. Not only can businesses upskill their current employees, but they can upskill those they plan to hire as long as they are on the payroll at the time of reimbursement.
More than 1,100 Ohio businesses have used TechCred, some having used it multiple times, creating the opportunity for 19,841 technology-focused credentials to be earned by Ohio employees.
The results from the April application period will be announced in the near future.
Visit TechCred.Ohio.gov to learn more!
Info-Tech Research Partnership
To provide additional resources for our members, we have formed a new Partnership with Info-Tech Research Group.
Through this relationship, Info-Tech is offering our community complimentary access to specific research and services across a wide range of topics as an additional benefit to members across the Technology First community.
Ensure your IT team delivers measurable results for your organization while networking with a community of peers and access these benefits through Technology First today!
If you are a member, be sure to log in and visit the Tech First Member forum under our Peer Groups tab to receive your complimentary resources!!
WANTED: Area organizations to host a student for 1 week!
Back to Basics Youth Education Center is a free non-profit after school program that focuses on coding and programming in the Dayton area.
This summer, they are holding a 4-week summer program for 5 students in the area. Two of the weeks they are wanting to set up 5-day mini internships with local IT organizations! Would you be willing to sponsor a student for a week?
Please contact: Lawrence Lindsey, Director, Back to Basics Youth Education Center at email@example.com
Every employee affects your organization. The executive leadership team may have the most direct impact on your company culture, but any employee who “leads” can too. While culture revolves around the engagement, environment, atmosphere and success of the company; leadership affects staff confidence and self-worth. Leaders at every level have a responsibility to “be the change you want to see” and to demonstrate the company values in their actions. Technology connects us in increasingly new ways that place a bigger social responsibility on our companies.
If you have a great culture, guard it as the precious foundation of your success. You must hire people who fit your culture. If your employee represents your company through account management, sales, marketing, support staff or even avenues outside of work; you’ll want to make sure that first impression is an accurate and favorable reflection of your company.
Why do you want to work here?
This is a common HR question and a great way to know that the prospective employee has done research on your company and the position. Are they onboard with your values? Do they live your values in their personal life? No one is perfect, but you’ll want to watch for dysfunction, passive-aggressive behavior and the dreaded sociopath. HR and hiring managers will need to look past the surface and see the real person. Some people are fantastic at interviews and can hide their true selves. A deceptive bad hire may slip through and will be very hard on team members and your culture even if they don’t make it to their 90-day review.
We’ve all witnessed that high-performing employee who didn’t fit the culture, often called “cultural vampires.” Even with solid performance, their attitude will be detrimental to the company culture. Maybe you’ve lived the “people don’t leave a company they leave a bad boss” meme. I have. And I felt forced to leave a job I loved and people I respected. A bad boss is hard on the culture. HR may want to look for signs to see if that polished, charming, capable person you are interviewing may be a toxic nightmare for your staff and culture.
A strong culture is distinguished by firmly held core values that are organized, openly shared and permeate throughout the entire staff. Employees will thrive when they know their leaders personally care about their well-being. The five aspects of well-being include physical, social, community, financial and purpose. Focusing on only one or two aspects will miss important opportunities to grow the best workplace culture. Quality leaders demonstrate a genuine interest in promoting the growth of their employees and will collaborate to build career paths and provide the resources needed. By encouraging employees to take risks in order to grow, effective leaders are able to foster a culture of learning and growth. Good leaders incentivize hard work and good behavior, and they promote a vision of the future that is positive and values-based. Strong leadership knows that good ideas and good decisions can come from anywhere.
People are all different, but you need that.
Most companies use some form of personality tests prior to hiring and there are plenty to choose from. Hippocrates suggested that humans had a “persona” in 460 BC and the rise in psychodynamics in the 1800s led to a drastic change in the way we viewed and understood personality in social situations. Sigmund Freud suggested that our behavior and personality are driven by our innate desires and needs. Carl Jung proposed there are four human personality influences: sensing and intuition (irrational or perceiving functions) and thinking and feeling (rational or judging functions). This spurred the popular Myers-Briggs testing. New hire assessments come in many forms and people are pretty used to doing skills assessments, personality quizzes and IQ testing as part of the hiring process.
Those of us who are Technology First Women 4 Technology (W4T) members have had the great opportunity to delve into the importance of creating teams and groups inside of our organizations that encompass all personality types. In 2017 we walked through our DISC (Dominance, Inducement, Submission and Compliance) profiles and in 2021 we virtually worked through our Clifton Strengths self-assessments. I walked away from these meetings with a better understanding of my own strengths, the strengths of my peers and that each personality type requires unique communications. In our differences, we are a stronger team.
The DISC session was focused on building effective teams using complementary styles. You probably get along great with some people and others take all of your energy and patience. In this analysis, all four personalities have a style that determines the way we approach our work and life and defines how we will react to situations and other people. This program is a great way to identify your style and what to look for in others to build highly effective teams. Those teams need to have an even number of people of each type and not be dominated by one certain personality characteristic.
We use this program at work through Innermetrix values with Dave Ramsey’s goal tactics as part of our hiring process and openly share our profiles with the team. I was surprised to see how different my “adaptive” style is from my “natural” style, which I attribute to a large family and strict parents. You can see by my chart that “C” (compliance or caution) goes from 39 natural to 60 adaptive. Whew!
You can use the complete executive summary to understand how employees will approach solving problems, interacting with team members, environmental preferences, expectations and their approach to the pace of their work. Additionally, the results will offer opportunities for developmental growth, motivation, learning styles and communication insights. There is a free assessment, or you can complete the full assessment for $29 per employee.
Focus on strengths and not on your weaknesses.
Clifton Strengths from Gallup breaks talents (strengths) down to 34 themes that fall into 4 different domains (Executing, Influencing, Relationship Building and Strategic Thinking). We are born with innate talents or gifts. The Clifton Strengths Test method, introduced in 2001 by Gallup and created by Don Clifton, concludes that a person experiences more success and satisfaction when they can use their talents in their daily activities. According to Gallup, a focus on weaknesses does nothing to help development, but a focus on strengths will allow you to succeed and continue in situations that others have decided are impossible or extremely difficult. When we create teams with a variety of strengths, the number of tools and talents they have access to increases exponentially.
Of course, completely ignoring weaknesses is not an option. It is healthy to identify the cause of the weakness (education, experience or opportunity) and there is almost always something that can be done about it. StrengthsFinder 2.0 ISBN 978-1-59562-015-6 can be purchased anywhere, I bought mine on Amazon for $17. Each book contains a unique code to take the CliftonStrengths Assessment online. The personality test consists of 177 questions and each should be answered within 20 seconds. The entire test usually takes around 30 minutes. A report is generated based on the answers and clarifies the five strongest themes and summarizes a strengths insight guide.
You cannot change your basic personality type. However, you can, and should, change the aspects of your personality that you are unhappy with to become a more well-rounded person.
Are technical people introverts and sales people extroverts?
2014 was declared the year of the “Introvert Craze” mainly due to the influx of technology in our daily lives and the rapid rise of social media channels. The terms introvert and extrovert are consistently misunderstood and usually show two polarized personalities – the extremely shy and the extremely confident. The truth is that introverts can be as social as extroverts, but when their batteries are drained, they need solitary downtime. Extroverts, on the other hand, need people to recharge their batteries. 2020 memes often showed introverts claiming to have prepped their whole life for the solitude of the pandemic. Has social media given a voice to introverts? Face-to-face socialization gears us for talking while the qualities of listening and thinking are largely ignored. Unfortunately, we may have taken that same thought into social channels too.
The stereotype of tech people being introverts is mostly false. Highly technical people tend to be more capable of being quiet and focused on what they are doing and are proficient at working alone. You won’t get very far in technology without this natural behavior. However, it’s unfair to perceive the extreme when most people are adept at crossing directions and socially adapted and able to handle human interactions. The perfect example of each extreme are the rivals Thomas Edison (extrovert) and Nikola Tesla (introvert). Where would we be without either of those? I believe I am an Ambivert. Anyone who has met me knows I love people and talk to everyone, but what you don’t see is when I’m drained of energy that I prefer to be alone to recharge. I think this is much more common than either of the extremes.
“Company culture is what employees live and breathe every day in the workplace.” ~ Mike Zani, The Predictive Index
Diversity and inclusivity at every level of your organization brings many benefits to your company and to your individual team members. It is important to create a culture where all people feel included and represented -- every department, every team, every project. You must first understand the true personalities of your team members then work to utilize their skills collectively.
Jim Bradley, VP IT, Tecomet & Technology First Board, Past Chair
So, what has it been like being a technology leader during these unprecedented times over the past year? And how has the technology culture changed in the new normal virtual world?
Being a technology leader during the past year has been truly challenging. For many of us, traveling has gone from regular to non-existent, whether that be local, regional, national, or global. This has freed up a significant amount of time which was subsequently and rapidly filled with on-going remote video meetings, typically supported by more presentations. Also, most of us are working longer hours without our usual commutes, both earlier in the morning and longer into the evenings and weekends.
Many of us depended on personal face-to-face interactions with our customers, our team and our partners and suppliers. We never thought it could or would work without that, but it has. It is less personal for sure, without the ability to see full body language, having some of the interpersonal chats before or after the meetings, or the always valuable informal hallway chats or those during travel, meals, and drinks. We’ve had to compensate and communicate in other ways.
Our first thoughts were around the bandwidth for work from home, having enough computers, peripherals and accessories, cybersecurity, and personal home wi-fi and internet. Many of these concerns were easier to overcome than initially anticipated. One of the items we had to address quickly were electronic signatures, which also became easier than originally thought.
What about Vision and Innovation? Technology has received more respect over the past year; everyone began to see their own dependencies for sufficient and better technology. On top of that, there was now more interest in how technology could help organizations more, adding to what was the technology vision and innovative solutions for many Executive Teams and Boards. What else could we do for mobility, agility, productivity, and efficiency now that it was in the spotlight? It was good for us to have more of a say at the table and to bring forth what else could be done.
As for the technology culture, changes in the new normal virtual world have been dramatic. Technology influences our everyday lives and has a strong influence on culture. It impacts the ways in which we do most anything; personal computers, mobile smartphones, tablets, e-mail, the internet, social media - how did we ever survive without these things before we had them? They have changed the way we communicate, learn, think, and how we interact on daily basis. We are more interactive and collaborative than ever before. But do these items also lead to addiction, psychological, or physical health issues?
Promoting technological understanding at a user level occurred as many, both in the professional and personal world, have become much more computer savvy, leading us to the point that we are all likely much more efficient…or are we? While we think we are, what overall psychological or physical health detriments have we ignored? How are we working harder and longer than ever before while remaining isolated from each other, unable to socially interact as we used to? And how are we physically? Are we still getting as much exercise and/or are we eating better in a more sedentary world? I would argue both worse and better depending on the individual. Have you rebalanced your eating, drinking, and exercise habits to remain healthy while working longer, and more stationary, hours? I’ve seen many more people standing at their desks all day to compensate.
Information Exchange occurs predominantly via remote video meetings, as well as over the phone. Is that as productive as a face-to-face meeting? I say not, but I also see it working for some individuals. But are we making tasks easier and solving problems this way? I believe we are, there is more automation, less downtime, and we are still getting our jobs done.
What do you think? My goal was to get you to consider all the pros and cons we’ve experienced in these past few months and truly think about it. What would you like to change now, and what would you like to keep? While we’ve still got a long way to go, keep your chin up and do what you can! Good luck!
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