Technology
Diverse Testing for the Best User Experiences
Long before I began consulting, I was developing new applications for a Marketing company. Nearly everything was built from the ground up then, and there was very little reuse. That changed over time as I developed reusable functions and eventually created a “standard system” that significantly reduced development time due to reuse. Throughout this multi-year period, I had an unplanned but valuable assistant – “Wendy”
My user interfaces were generally well received for their layout, workflow, help screens, etc. But a new hire on the Customer Service team consistently ran into problems. I was young, and one of my first interactions with her probably went something like this: “Why would you do it that way? That doesn’t even make sense? Have you ever worked with computers before?”
She began crying. I felt like a jerk as my frustration began to wane. Days later, I realized Wendy was a gift and not a problem. She had an incredible knack for finding obscure flaws and breaking things. I embraced it, bought her lunch, and asked her to help improve my software. She was excited to help, and eventually we laughed about our initial encounters.
Wendy and I had become allies in a quest to create custom software that provided a better, problem-free user experience. Nothing was taken for granted. Everything became more robust. Surprisingly, everyone appreciated these changes, not just Wendy. She helped me become a better programmer and analyst, and I gave her an experience that helped her become one of the first Quality Assurance Analysts in the company. It was a win-win.
There is often a considerable difference in the expectations and ways that Gen Z, Millennial, Gen X, and Baby Boomer users interface with applications. Creating a one-size-fits-all application is far more challenging today because of this simple fact. But it is essential to success.
People today tend to move on when their experiences fail to match their expectations. Investing in your system’s “Wendy-proofing” can become a competitive advantage. I have long believed that “People buy easy.”
If one person encounters a problem, others will likely follow unless you implement a remedy. It is more work, but the result can be increased satisfaction, usage, and loyalty. That seems like a good tradeoff to me.
What are your thoughts?
Using Themes for Enhanced Problem Solving
Thematic Analysis is a powerful qualitative approach used by many consultants. It involves identifying patterns and themes to better understand how and why something happened, providing context for other quantitative analyses. It can also be used when developing strategies and tactics because of its “cause and effect” nature.
Typical analysis tends to be event-based. Something happened that was unexpected. Some type of triggering or compelling event is sought to either stop something from happening or to make something happen. With enough of the right data, you may be able to identify patterns that help predict what will happen next based on past events. This data-based understanding may be simplistic or incomplete, but often it is sufficient.
But people are creatures of habit. If you can identify and understand those habits and place them within the context of a specific environment that includes interactions with others, you may be able to identify patterns within the patterns. Those themes can be much better indicators of what may or may not happen than the data itself. They become better predictors of things to come and can help identify more effective strategies and tactics to achieve your goals.
This approach requires that a person view an event (desired or historical) from various perspectives to help understand:
- Things that are accidental but predictable because of human nature.
- Things that are predictable based on other events and interactions.
- Things that are the logical consequence of a series of events and outcomes.
Aside from the practical implications of this approach, I find it fascinating relative to AI and Predictive Analysis.
For example, you can proactively monitor data, activities, and patterns by understanding recurring themes and triggers. That provides actionable intelligence that can be automated and incorporated into a larger system. Machine Learning and Deep Learning can analyze tremendous volumes of data from various sources in real-time.
Combine that with Semantic Analysis (and products like AllegroGraph) to harness the power of language through taxonomies and ontologies. The monitoring system would better understand what is happening (not just be aware of it), possibly determine how and why it happened, and result in even more focused and accurate predictions. Finally, include spatial and temporal data such as IoT, metadata from photographs, etc., and you should be able to view something as though you were very high up – providing the ability to “see” what is on the path ahead. It is obviously not that simple, but it is exciting.
This approach provides a multi-dimensional view of events and their causality, giving you the means to identify and prevent complex problems before they negatively affect a business.
Keeping these thoughts in mind will help you see details others have missed. Better tools can make for better analysis, better strategies, and better outcomes. Who wouldn’t want that?
Biometric Identity Theft
Recently, I have been researching the potential for fraud and identity theft using fingerprints from photos posted on social media. Last week, Amazon released its “Amazon One” Palm Scanner to pay for purchases. That announcement made me wonder: What are the potential implications for fraud and identity theft using biometric data taken from images?
Technology continues to improve rapidly, which often means, “Where there is the will, there’s a way.”
There are a surprising number of ways to accurately identify someone from a photo or video. Moreover, technology can copy fingerprints from social media photos taken up to three meters away. New technology has been proven effective at using 3D printing technology to create “fake fingerprints” that bypass many fingerprint scanners. Other factors may be at play (such as temperature), but that is speculation at this point.
Since fingerprints can be copied from photos taken up to three meters away, can a palm print be copied from a photo taken 5-10 meters away? That question led to an interesting but unscientific experiment where I took pictures of my hand, enlarged them, measured the distance between the ridges and furrows of my fingers and my palm, and compared the results. Spoiler – probably not (yet) – but likely not far away given the rapid advancement of AI.
Several areas showed similar distances for both my fingers and palm. But there were also areas on my palm where the average distance between “landmarks” was 3-5+ times greater. It turns out that a palm image is often segmented into 3-4 distinct regions for identification purposes, likely due to this type of variation. This link helped me understand the process.
This research led to an idea for a chip-based embedded filter for smart devices and laptops. It would obfuscate key biometric information when extracting the data for display without affecting the integrity of the original stored image. This functionality would automatically provide an additional layer of privacy and data protection. It would require optimized object detection capabilities (possibly R-CNN) that were highly efficient and run on a capable but low-energy processor like the ARM Cortex-M. Retraining and upgrades would be accomplished with firmware updates.
Edit 2020-10-13: This article on “Tiny ML” from Medium.com is the perfect tie-in to the abovementioned idea.
While Amazon’s technology is much newer and presumably at least partially based on their 2019 Patent Application (which does look impressive), it makes you wonder how susceptible these devices might be to fraud given reports of the scans occurring “almost instantaneously.” Speed is one aspect of successful large-scale commercial adoption, but from my perspective, the system’s accuracy and integrity matter far more.
Time will tell how robust and foolproof Amazon’s new technology really is. Given their reach, this could occur sooner rather than later. Ultimately, multiple forms of biometric scans (such as a full handprint with shape, palm, and fingerprints or a retina scan 2-3 minutes prior to the palm scan to maintain performance) may be required for enhanced security, especially with mobile devices.
Additional Resources:
- An in-depth NIH document that helps explain the process and issues.
- A Simplified Guide to Fingerprint Analysis
- This is a 2012 Fingerprint and Palmprint Recognition Approach Report from the European Journal of Scientific Research that has many images and very good explanations.
- “A Textural Approach to Palmprint Identification“
The Coming Changes to Manufacturing
Recently, I spoke with someone on a team analyzing ways to “mitigate the risk of exclusive manufacturing in China” without fully divesting their business interests in a growing and potentially lucrative market. This bifurcation exercise got me thinking about how many other companies are evaluating their supply chain relationships, inventory management, and the predictability of their cost of goods sold.
In the mid-1990s, I had done a lot of work with the MK manufacturing software that ran on the Ingres database. Some issues were performance-related and fixed by database tuning; some were fixed by using average costs instead of a full Bill of Materials (BOM) explosion with dozens of screws in a window; but some were more interesting and more business-focused.
After NAFTA became law, one manufacturer built a facility in Mexico and started manufacturing a few basic but important parts. When I arrived as a Consultant, the main problem they faced was a reject rate of roughly 20% and additional related QA costs. My suggestion was to treat this part (a single piece of steel, like the rotor from a disk brake system) as a component and build in the cost of both scrap and QA. They could then benchmark the costs against other suppliers in an apples-to-apples comparison to determine if they really saved money. That approach worked well for them.
While that approach helped manage costs, it did not address the timeliness of orders or lead time required – important aspects of Just-in-Time (JIT) manufacturing. Additionally, it should be possible to estimate shipping costs by considering changes in petroleum costs or anticipated changes in demand or capacity.
Systems out there claim to estimate the cost and availability of commodities based on various global factors and leading indicators. It is tricky, to say the least, and we can’t anticipate an event like a pandemic. But companies that manage their inventory and production risk best will likely be the ones that succeed in the long run. They will become the most reliable suppliers and have increased profits to invest in further growth and improvement.
The next 2-3 years will be very interesting due to technological advances (especially AI) and geopolitical changes. Those companies that embrace change and focus on real transformation will likely emerge as the new leaders in their segments by 2025.
New Perspectives on Changing Business Ecosystems
One of the many changes resulting from the COVID-19 pandemic has been a sea change in thoughts and goals around Supply Chain Management (SCM). Existing SCM systems were upended in mere months as procuring raw materials and components became challenging, manufacturing shifted to meet new, unanticipated needs, and logistics challenges arose from health-related staffing issues, safe working distances, and limited shipping options and availability. In short, things are a mess!
Foundational business changes will require modern approaches to Change Management. Change is not easy – especially at scale – so having ongoing support from the top down and providing incentives to motivate the right behaviors, actions, and outcomes will be especially critical to the success of those initiatives. And remember, “What gets measured gets managed,” so focusing on the aspects of business and change that matter will become more important.
Business Intelligence systems will be especially important for Descriptive Analysis. Machine Learning will likely play a larger role as organizations seek a more comprehensive understanding of patterns and work toward accurate Predictive Analysis. And, of course, Artificial Intelligence / Deep Learning / Neural Networks capabilities will accelerate, filling several gaps, including the need for enhanced Prescriptive Analysis. Technology will provide many of the insights business leaders need to make the best decisions in the shortest time, while accounting for the complexity of cost/benefits/risk/competing efforts.
This is also the right time to consider upgrading to a collaborative, agile business ecosystem that can expand and adapt quickly and cost-effectively to whatever comes next. Click on this link to see more of the benefits of this type of model.
Whether you like it or not, change is coming. So, why not take a proactive posture to help ensure this change has a positive impact and meets your company’s or organization’s objectives?
Changes like this are all-encompassing, so it helps to start with the mindset, “Win together, lose together.” In general, having all areas of an organization moving in lockstep toward a common goal is ideal. Critical junctures like these are what require change and drive success.
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