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 Sue.”
My user interfaces were generally liked due to layout, workflow, help screens, etc. But, a new hire in the Customer Service team was consistently running 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 Sue was a gift and not a problem. She had an incredible knack for finding obscure flaws and breaking things. I embraced this, bought her lunch, and asked her if she would help improve my software. She was excited to be able to help, and eventually, we laughed about our initial encounters.
Wendy Sue 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, these changes were appreciated by everyone, not just Wendy Sue. She helped me become a better programmer and analyst, and I provided her with an experience that led to her becoming 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 all application is far more challenging today because of this simple fact. But, it is essential to success.
People today tend to move on to something else when their experiences fail to match their expectations. Investing in your system’s “Wendy Sue proofing” can become a competitive advantage. I have long held the belief that, “People buy easy.”
If one person encounters a problem, others will likely follow unless a remedy is implemented. 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, which provides the context for other quantitative analyses. It can also be utilized when developing strategies and tactics due to 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, which can 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 monitor data and activities proactively by understanding the recurring themes and triggers. That is 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, which is challenging due to the complexity of taxonomies and ontologies. Now, that system more accurately understands what is happening to make accurate predictions. Add in 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.
From a practical perspective, keeping these thoughts in mind will help you see details others have missed. That makes for better analysis, better strategies, and better execution.
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 when shopping. 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, there is technology to 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.
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.
There are several areas where that distance was similar 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 was helpful in understanding 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 the accuracy and integrity of the system are far more important from my perspective.
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:
- In-depth NIH document that is helpful in understanding 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 a person on a team analyzing ways to “mitigate the risk of exclusive manufacturing in China” while not 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 of the 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 using dozens of screws in a window, but some were more interesting and also 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 the scrap and the QA. They could then benchmark the costs against other suppliers in an apples-to-apples comparison to determine if they saved money. That approach ended up working 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.
There are systems out there that 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 are able to manage their inventory and production risk the 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 the further growth and improvement of their businesses.
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 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 up-ended in mere months as it has become challenging to procure raw materials to components, manufacturing has shifted to meet new unanticipated needs, and logistics challenges have arisen 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 a greater focus.
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 should accelerate as the need for Prescriptive Analysis grows. Technology will provide many of the insights needed for business leaders to make the best decisions in the shortest amount of time, which is both possible and prudent.
This is also the right time to consider upgrading to a collaborative, agile business ecosystem that can quickly and cost-effectively expand and adapt 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 that this change is good and meets the objectives your company or organization needs.
Changes like this are all-encompassing, so it is helpful to begin with the mindset, “Win together, Lose together.” In general, it helps to have all areas of an organization moving in lockstep towards a common goal, but at a critical juncture like this, that is no longer an option.
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