Log in


Connect with TECH NEWS and discover emerging trends, the latest IT news and events and enjoy concrete examples of why Technology First is the best connected IT community in the region.

LOOKING FOR MORE WAYS TO CONNECT?

Subscribe to our newsletter


  • 07/01/2021 3:58 PM | Kaitlin Quellhorst (Administrator)

    Mardi Humphreys, Change Agent Integration Edge


    I made this poster to hang in my office. (Full disclosure: Since the pandemic my office is actually the dog’s bedroom. Yes. The dog has his own room. Don’t ask.) I made the poster to remind myself that innovation is often only appreciated in hindsight. While modern-day pirates are a serious threat to certain areas of the world (e.g., West Africa and Somalia) and should be treated as such, my poster represents archetypal pirates like Blackbeard who was known not only for his leadership and non-violent nature, but also for his boldness, personal courage, and brand awareness; all important elements of innovation. Here in Dayton, OH we have a lot to live up to in terms of innovation. There’s the obvious: The Wright Brothers, Ermal Fraze, Charles F. Kettering, et al; but what about you? Your business is IT. Aren’t you innovating almost daily? There are basically three types of innovation: product, process, and business model:

    Product

    Since we work in Information Technology, this is the type of innovation we most likely think of when we hear the word because we see it all the time. Think: combining an MP3 player with a mobile phone (more on this later).

    Process

    This is where I live. We streamline SMBs’ and non-profits’ workflows and procedures to eliminate waste and human error, and to increase data security. We automate as many steps in their processes as possible, thus saving them time, energy, attention, and money. Think: a physician sending your prescription to your preferred pharmacy online instead of writing it out and handing it to you on a piece of paper.

    Business Model

    This is the most drastic innovation because it affects the entire organization. It’s most successful in startups because they’re still experimenting with how to structure their businesses. It usually occurs when the person in charge starts wondering out loud why customers aren’t buying. Think Amazon.

    You see innovation and you are innovative, but do you act on that innovation? When you have an idea you believe will change the course of the product, process, or profession, what keeps you from acting on it? Innovators have at least three things in common:

    Bravery

    Innovation is scary. You have to put yourself out there. Brene Brown says, “Vulnerability is the birthplace of innovation, creativity and change.” As the Change Agent for Integration Edge. I have to sit with vulnerability a lot. Change is hard. Even good change, like growth, is still difficult. There’s a reason growing pains is a cliché.  Humans are creatures of habit. We love doing what we’ve always done. It’s getting what we’ve always gotten that we don’t like. That’s where innovation enters the equation. One of my favorite things about innovation is that it seems so epic; but in reality, one little change can be that last push the flywheel needs to start turning as fast as you’d hoped it could.

    Boldness

    Back in the Product section of this article, I promised more to the story of combining an MP3 player with a mobile phone. Here it is, long story short: When a small team of engineers, designers, and marketers at Apple presented the idea to Steve Jobs, he vehemently opposed it. But his team saw the potential both in the technology and the market, and kept gently persuading and boldly negotiating with Jobs until he agreed. Four years after it launched, the iPhone was responsible for half of Apple’s revenue. Innovators don’t ask their customers what they want. Innovators ask their customers what they want outcomes to be. iPod customers did not suggest that Apple combine the iPod with a mobile phone. They just wanted to stop carrying multiple devices.

    Brand Awareness

    Innovation is ineffective if it’s a secret. In development you need secrecy, but once you’re in production you have to tell the world about it. It’s okay to get excited about projects that improve our clients’ businesses. You worked hard. Tell everyone who will listen. The more organizations you impact, the more innovative you’ll be and the more your reputation will grow. I learned this lesson from Edward Teach. Back in 1717 he commandeered a slave ship, La Concorde, disembarked the people at Bequia, then proceeded to acquire two more ships and 150 crew members. Teach was big and tall. In battle, he dressed in dark clothing with a sling over his shoulders that held a brace of three pistols. He wore a wide hat under which he put lit matches to appear fierce. He did this to intentionally make a reputation for himself that would scare his enemies so that he did not have to hurt them. (We have no verified account he ever harmed any of his captives.) Like Teach, we want to cultivate a certain reputation because we understand the value of appearances. How valuable was his appearance? Teach renamed the slave ship that he commandeered Queen Anne’s Revenge. Edward Teach renamed himself Blackbeard.


  • 07/01/2021 1:39 PM | Kaitlin Quellhorst (Administrator)

    Future of No-Code Bright, but Challenges Remain

    Matt Coatney, CIO, Thompson Hine LLP & Technology First Board of Directors

    Several years ago, I led professional services for a data analytics no-code/low-code company. We built cutting-edge solutions for healthcare, life sciences, finance, and more using our trailblazing platform.

    There was one problem though. Consulting was a great revenue stream, but business wants the recurring revenue of licensing and “citizen developer” self-service. But no matter how much we tried to convince companies they could build applications with our tool, inevitably they would lean back on us instead.

    I call this the Development Gap: the gulf between organizations’ application needs, the availability and technical sophistication of their business teams, and the state of no-code platforms. The gap is still too wide for the average company to get real, transformative benefit from these tools., But I remain optimistic in the long run.

    There is a lot of hype around products like Microsoft PowerApps that promise “anyone can build an app.” I recently surveyed the landscape, tried out several tools, and admittedly walked away disappointed. Most tools approach the space in a similar way and focus on basic use cases: form data entry, editable lists of items, and dashboards. While you can create some useful apps with those building blocks, it is not much more than a glorified online spreadsheet with a few more bells and whistles.

    But if you want to make an immersive application experience? Forget about it. Even something as simple as uploading a file and performing an action in real-time proved difficult in these platforms. In many cases, the user must write code to accomplish more than trivial use cases, and we are right back where we started. Non-developers are either overwhelmed by the interface or stymied by limited functionality, and developers will not use the tools because, well, they love to write code.

    So why am I still optimistic? For one, the market demand has increased significantly, and investment dollars have followed. Plus, with several large players pushing into this space, I expect significant improvements over the next five years in user interface design, reusable component libraries, and AI-enabled automation. Additionally, as younger tech-natives enter the workforce, they are more attuned to how software works, and while they are not developers, they often have a better grasp of data and business logic.

    In the meantime, what can you do to help bring innovative applications to your organization more quickly (the promise of no-code)? One way is to leverage more mature self-service platforms for targeted use cases, especially in the area of data reporting and dashboards. Your development teams can also spend more of their scarce time building platforms to support rapid development and self-service, rather than bespoke business unit solutions. And development teams can also further embrace agile iterative development with shorter cycle times, which ultimately bring product to market quicker.

    These steps will help you advance innovation initiatives while we all wait for the Development Gap to get just a bit narrower.


  • 07/01/2021 10:14 AM | Kaitlin Quellhorst (Administrator)

    Technology First has teamed up with Girl Scouts of Western Ohio for a CSA Cyber Challenge with support of the Ohio Cyber Range Institute!

    Volunteers from Technology First’s membership will lead a series of “plugged” and “unplugged” cyber challenges to over 50 Girl Scouts grades 6-12. This event will take place on July 23rd at the University of Cincinnati’s 1819 Innovation Hub. Participating companies include 84.51, CareSource, Great American Insurance Group, Kettering Health, Kroger, P&G, and the University of Cincinnati.

    If you know a Girl Scout interested in participating, they can register by Friday, July 9th!

  • 06/30/2021 1:07 PM | Kaitlin Quellhorst (Administrator)

    Business Leaders and Community Stakeholders,

    A successful and sustainable recovery for Ohio must include all Ohioans. Nearly 20% of Ohioans live in distressed areas—defined as areas that have not recovered from the last recession. The COVID-19 pandemic further disproportionally impacted these communities and their residents. It is critical that we support these areas of the state to help them and Ohio overall with post-pandemic economic recovery.

    These communities are home to businesses with intellect that can benefit all of Ohio. JobsOhio is prioritizing efforts to empower the businesses with the resources they need to reach their potential. Since the start of 2020, we have worked on an inclusion strategy that focuses on investing in and driving job creation to Ohio’s distressed areas as well as providing the capital needed to grow the businesses in these communities and those owned by underserved populations.

    One component of this strategy is the JobsOhio Inclusion Grant, which was created in July 2020 as part of JobsOhio’s COVID relief programming. The program puts funding of up to $50,000 for small and medium-sized businesses located in distressed areas of the state and owned by underrepresented population groups. As of today:

    • 133 companies have closed deals or are currently in the pipeline.
    • Over 4,287 jobs have been created or retained.
    • Of these projects, 82% are in distressed communities, 23% are woman owned, 21% are minority owned, and 11% are veteran owned.

    Some of the participating homegrown businesses include The Chef’s Garden, an agriculture company that pivoted its business model due to the pandemic, and MAKO Finished Products, a rural Ohio-based business that helps power a leading fitness brand. After a successful first year in providing $5 million in financial support to small and medium-sized businesses, the JobsOhio Board of Directors recently approved an increase of up to $8 million in 2021 due to the success and growing interest from local businesses.

    Our efforts to ensure Ohio’s recovery is inclusive of all Ohio continue and we will share more in future updates.

    Learn more about the JobsOhio Inclusion Grant


  • 06/29/2021 10:08 AM | Kaitlin Quellhorst (Administrator)


    Treg Gilstorf has been selected chief operating officer for Smart Data, a local IT services provider and Technology First Member.

    Treg is Vice Chair of the Technology First Board and has 25+ years of experience in IT, strategic planning, process improvement, consulting, operations and project management. Prior to joining Smart Data, he spent eight years as chief information officer at Yaskawa Motoman, the Miamisburg-based robotics division of Yaskawa Electric Corp. Before that, he held leadership roles for companies throughout Southwest Ohio and Indiana — including Cincinnati-based Fifth Third Bank, Afidence and Luxottica; as well as Duke Energy’s operations in Indianapolis.

    Congratulations on the new adventure, Treg!


  • 06/28/2021 1:03 PM | Kaitlin Quellhorst (Administrator)

    Celebrate Interns in your organization (July 12-16, 2021)

    DAYTON, Ohio (June 28, 2021) – The Southwestern Ohio Council for Higher Education (SOCHE) leads several initiatives to increase internships and close workforce gaps in Ohio. The Dayton Region recognizes the immense value of interns in workplaces and communities. Dayton Region Internship Appreciation Week encourages Southwestern Ohio companies to show their appreciation for interns at all levels. Companies can visit  https://bit.ly/3hL2ZvT for ideas on how to thank their interns.   

    Several Mayors in the Miami Valley have issued proclamations to declare Intern Appreciation Week for their city from July 12 – 16, 2021.  Thanks much to the cities of Springfield, Beavercreek, Trotwood, and Centerville! Other cities are welcome to join!   

    SOCHE’s member colleges and universities, representing nearly 200,000 students, participate in thousands of internships throughout the year. These 200,000 students, in addition to all the region’s high school students, are the future workforce. Hiring these students boosts the likelihood that they will remain in the area as full-time employees.

    “We’re proud of the ever-increasing intern numbers across the region and we hope that employers and colleges and universities are equally proud of the progress,” said Cassie Barlow, President of SOCHE. “Every year we work to strengthen and evolve partnerships between the private sector and higher education to provide more internship opportunities in Ohio,” Barlow added.  

    SOCHE’s internship program employs hundreds of students, high school through the post-doctoral level, in many positions to support Wright Patterson Air Force Base, the City of Dayton, and Montgomery County, as well as numerous small and mid-sized companies. Internship roles call for students majoring in various subjects, including science, technology, engineering, math, arts, business, social sciences, manufacturing, and humanities majors. 

    Patty Buddelmeyer, Vice President of Development at SOCHE, added, “We’re excited to see more and more employers with interns in the Dayton region. Businesses are experiencing the value interns bring to workplaces. The rising internship numbers speak to the commitment of the local business community and colleges and universities to retain talented and engaged students in the region.”  


    Formed in 1967, SOCHE is the trusted and recognized regional leader for higher collaboration, working with colleges and universities to transform their communities and economies through the education, employment, and engagement of nearly 200,000 students in southwest Ohio. SOCHE is home to SOCHEintern, the Aerospace Professional Development Center (APDC), and Defense Associated Graduate Student Innovators (DAGSI). For more information about SOCHE and all SOCHE initiatives, visit http://www.soche.org/.


  • 05/26/2021 12:56 PM | Kaitlin Quellhorst (Administrator)

    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.

    Graphical Interface

    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.

    Out-of-The-Box Algorithms

    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:

    • Enables the use of Azure DevOps Pipelines to automate the data ingestion process and perform data checks
    • Provides the ability to automate the Continuous Training and Continuous Deployment aspects of the MLOps lifecycle, removing the need to manually retrain models as performance degrades
    • Automates scaling of compute resources for model training, both up and out, and provides a one-stop-shop for managing backend compute needs
    • Automates deployment of trained/retrained models to internal- and external-facing services on Azure Kubernetes Services, Azure App Services, Azure Container Instances, or Azure Virtual Machines, and removes the need to manually deploy new or retrained models

    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.


  • 05/25/2021 1:30 PM | Kaitlin Quellhorst (Administrator)

    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

    Goldilocks’ Results:
    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.


  • 05/25/2021 1:27 PM | Kaitlin Quellhorst (Administrator)

    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.


ANNUAL PARTNERS

Our Annual Partners share a common goal; to connect, strengthen and champion the IT community in our region. A Technology First Annual Partner is an elite member leading the support, development and expansion of Technology First services. In return, Annual Partners improve community visibility and increase their revenue. Make a difference in our region and your business. Become a Technology First Annual Partner.  

Learn more about the benefits of being one of our Annual Partners.




Technology First

1435 Cincinnati St, Ste 300, Dayton Ohio 45402

Info@TechnologyFirst.org

© Technology First, All Rights Reserved