Wednesday, July 1, 2020

Context: The Connecting Clues for Data

The “database of now” demands a quick understanding of data, particularly in context. There are many opportunities in understanding or misunderstanding data in terms of the contexts they participate in, or are connected to, or connected at the edge of a data neighborhood. Because each context has its own unique vocabulary, you can see the opportunity for misconnects in meaning by not understanding the full context of any statement or set of proven facts.

If someone says, "I like the blue one", how can you evaluate what that means? If it is a swimsuit on the beach, it means one thing, if it's a lobster from the same beach, that means a totally different thing. Context is what gives data real meaning. There are three primary forms of context that help understand the true meaning of the base data. One is the real world contextual meaning, the other is the contextual business meaning, and the other is the technical contextual meaning. Obviously, finding meaning in big or monster data is a challenge, but that difficulty increases as the speed increases, particularly if the data is hard to manage or access.


Figure 1 Representation of Interconnected Contexts.

 Real-World Context

Data has meaning in terms of its definitional domain. When you mention "blue", usually comes from the color domain. However, in the world of mental health, it means a kind of feeling or mood. So understanding the base context in which a data element exists is essential. If blue is associated with a human context, it could be physical and mean a lack of oxygen. It could also mean that the person is adorned in something blue. This is usually cleared up by understanding the base subject or entity that the data element is associated with by having a precise name, meaning, and basic subject area association. Underlying meaning can be tricky when just looking at the data value alone. Having proper meta-data and associations is the ideal solution to this problem.  

Business Context

The contextual areas that relate to business fall into three basic categories of meaning. Every data item or group of data items needs to be viewed in terms of the context they are being viewed in or from. Internal contexts where the vocabulary is understood in the context of the internal organization and defined within a particular organization. These internal contexts usually revolve around the organization and skill definitions. There is also the external context that represents the outside world irrespective of the organization itself. The third context is where the outside world touches the internal world. Listed below are the typical contexts in each of these categories:

Common External Contexts:

Communities, Brand/Reputation, Public, Legal Frameworks/Courts, Geographical Regions, Countries, Local Culture, Governmental Agencies, Industries, Dynamic Industry 4.0, Value Chains, Supply Chains, Service Vendors, Markets, Competitors, Prospects, and Competitors Customers.

Common Internal Contexts

Organizational Culture, Goals, Constraints, Boundaries, Actual Customers, Products, Services, Suppliers, Employees, Contractors, Departments, Divisions, General Accounts, Contracts, Physical Infrastructure, Technical Infrastructure, Properties, Investments, Intellectual capital, Business Competencies. Knowledge, Skills, Patents, Success Measures and Statements

Common Interactive Contexts:

Marketing Channels, Advertisements, Customer Journeys, Customer Experience, Loyalty, Satisfaction Scores, Processes, Applications, User interfaces, Websites, Webpages, and System Interfaces.

Technical Context

Data must also be understood in terms of physical contexts, limitations, and potential lag times. Data sources need to be understood in terms of their currency and ability to be integrated easily with other sources. While many views, interactions, and integrations work well at the logical level, physically, they may not be ready in terms of near-real-time capabilities, transformation potential, or performance levels on or off-prem. While meta-data may exist to understand possible joins and combinations, executing them fast enough to be useful in multiple business contexts may not be possible. The physical data types and file storage mechanisms may not be conducive to the demands of new usage scenarios. New low lag databases that are near real-time will become the standard, going forward.

Net; Net:

Data, information, knowledge are quite dependent on the context(s) they participate in or the perspective they are viewed from. Often Knowledge worlds interact; therefore, meanings can overlap and connect in ways that are essential for ultimate understanding, manipulation, or utilization. Knowing the context of your data is absolutely critical for leveraging understanding.  All of this is happening at greater speeds approaching the “database of now” speed necessary to make critical decisions, actions, adjustments or improvements. 



Tuesday, June 30, 2020

Generative AI+ Art is Gaining Momentum

I thought a post on generative art might be in the interest of all things AI. This kind of art is leveraging AI, algorithms, randomness, programs, and humans to create exciting and beautiful art. As you may know, I now collaborate with Fractal Software to develop compelling and award-winning artwork. In fact, some of my fractals are my best sellers. I have a great friend and fellow artist, Bob Weerts, who is pushing this collaboration even further. Below are two of his early generative pieces:












Bob employs lines as his fundamental stylistic element and incorporates a chance in determining line length, density, and color. He cedes some control over the work's final outcome to a process enabled by Software he's written allow the piece to "emerge" over time. He plans to let the Software take more control of these emergent pieces over time, letting AI/Algorithms expand some range. I find his early pieces quite pleasing and interesting already.

One source of Bob’s original inspiration is Casey Reas "Process Compendium," which, among other ideas, explored a synthesis of the Complexity Science notion of “emergence” and Generative Art in the early 2000s. An example of Reas Compendium work is below: (Click Here for Other Examples).

Reas is an internationally admired artist, but perhaps best known as the author, along with Ben Fry, of the graphical sketching too called "Processing," which is widely used in the domains of Art, Design, and Media. 

The significance of the generative art trend is perhaps exemplified by Christie's record of $432,500 sales of "Portrait of Belamy". The image is one of a series created by a group of young French students collaborating collectively as "Obvious".  Obvious borrowed heavily from open-source Generative Adversarial Network (GAN) algorithms specially developed by a then-high school graduate Robbie Barrat but originally conceived by the AI researcher Ian Goodfellow. This has the ball rolling, and there is new momentum under the "GAN" movement. Generative adversarial networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the other (thus the “adversarial”) in order to generate new, synthetic instances of data that can pass for real data. They are used widely in image generation, video generation, and voice generation.

GAN's potential for both good and evil is huge because they can learn to mimic any distribution of data. GANs can be taught to create worlds eerily similar to our own in any domain: Images, music, speech, prose. They are robot artists in a sense, and their output is impressive. But they can also be used to generate fake media content of often called "deep fakes." 

Net; Net:

AI Generative Art is quite striking. Since the whole field is getting more towards AI and less from the artist/programmer, we can expect some exciting results in the future. I will likely pursue a more intimate collaboration with all kinds of generative art going forward. Keep your eye on Bob Weerts as he is a creative guy seeking this edge faster than many other artists.  

 

If you want to see my works, check out the fractals section here 

If you want to know more about my collaborations with Software to create, check out this post 

Read about more right-brained AI by clicking here 


 

 




 


Tuesday, June 16, 2020

Exploring Data Delivers

We hear about organizations mining data looking for benefits nearly every day now. Just like the prospectors of old, people are trying to mine gems out of the big patch of ground under their claim while searching adjacent areas. The case studies are abounding, so the appeal is strong. These mining efforts are this really paying off so how should one go about it?  Just start digging a big hole and hope for the best? With all the buzz around data mining and process mining, there are some proven paths to successful mining. 




Identify the Benefits of Data Mining 

It's pretty easy to justify the mining efforts on the promise of benefits today because there are so many success stories floating out there. The typical benefits that keep repeating include improved decision making, improved risk mitigation, improved planning, competitive advantage, cost reduction, customer acquisition, customer loyalty, new revenue streams, and new product/service development. The crucial step here is to find the benefits that will resound in your organization and situation. These days organizations are dealing with a multitude of challenges from plagues to politics. It is always a win to save costs, but there has to be more to it to create a compound set of appropriate benefits needed to justify mining efforts. 


Scoping Efforts Properly Delivers Better Results

While we all believe that data mining has the potential to improve and even transform organizations, the amount of data to mine is growing by the second and the number of advancements in making data smart is expanding. It's not difficult to understand that the majority of organizations are struggling to find the right strategy or solution. The first step is to discover where there is significant potential like miners do by drilling boreholes to discover the potential in the ground. That means organizations will have to sample areas of data that promise potential. To that end, many organizations start with process minging because it promises cost and time savings that often improve customer experiences leveraging smaller scopes. For those organizations that wanted an outside-in perspective, starting with customer journeys and large scoped processes that cross system boundaries have been quite successful. 


Incremental Learning is Essential to Continued Success

Starting small and expanding as success allows seems to be the most common model for mining. What is really popular at the moment is to use mining to find opportunities for more automation. Savvy organizations will look at adjacent systems, organizational units, and contexts. Feedback loops and iteration will teach the best lessons for mining results. Alternative visualization techniques such as timelines, animation, and limits do also help the learning process. Some organizations will also combine the visualization with discrete simulation to test alternative outcomes.   


Net; Net: 

If you are not practicing focused data mining and looking for productive patterns in your ever-growing data inventory, you are missing many opportunities. As successes emerge, the more savvy organizations are looking to widen their scopes and using approaches that cut through the jungle of their organization. Mining is here to stay and brings a valuable set of methods, techniques, and tools to leverage for organizations looking to thrive under all conditions. 








Tuesday, June 9, 2020

Organizations are a Jungle of Journeys

The simple idea of selling a product or a service for a price to make a profit is still the underpinning of most organizations. Still, it's gotten more sophisticated and intertwined than even five years ago. Many organizations participate in broader contexts like value and supply chains while dealing with dynamic change and emerging scenarios are driven by geopolitical or environmental trends/events. For continued organizational health, organizations will need to understand the journeys that exist and interact in their footprint of impact, learn the levers that can adapt their jungle to changing and in some cases, practice the response to the emergent conditions of "NOW." Listed below are the typical journeys that organizations need to participate in or manage in no particular order of importance:



Customer Journeys

The journey that a customer takes is a crucial journey to manage as it defines your organization's contribution to that journey that leaves an indelible memory of good or bad for all steps involved. It is essential to understand the customer's real journey, not just where a customer might touch your organization. Getting customers to be attracted and stay loyal to your organization depends significantly on your understanding of their real journey, not just the optimization or automation inside of your organization for cost savings. 

Work Journeys

Work arrives, gets assigned and moves through your organization, and is the key for cost and timing outcomes. Understanding where work gets stuck, deep in the innards of your organization, is essential for cost optimization and customer satisfaction improvements. It could be a competency skill deficiency, a data deficiency, an overburdened shared resource, or just a situation never contemplated for in the work design. These are some of the thickest vines in the jungle.

Employee Journeys

Employees are some of the most critical and expensive resources an organization manages. Making sure their time is optimized and used correctly is crucial for resource leverage with optimization reasons. Concurrent with employee participation with various journeys, they must be augmented and have enhanced/expanded skills. Assistance may occur through bot augmentation or knowledge turbocharging, but investment in employees is the often forgotten sub-journey. Lack of investment in employees is an easy way to lose in the long term.

Product/Service Journeys

Every product or service must be designed with the greatest of care and the best knowledge/ skills available. The journey from design to production should be planned, managed, built, and tested with the greatest of attention as they are often the competitive differentiator along with the customer journey and experience. Organizations tend to be very good at these kinds of journeys except when they become out of touch with trends or their customers, partners, and employees. 

Infrastructure Journeys

Organizations have to build and establish the infrastructure necessary to support the business. Service software has to run somewhere and needs to be built/maintained and supported by infrastructural software. These are part of the infrastructure that must be carefully and made promptly and retired if necessary as time progresses. The support will need to be built, maintained, or outsourced to other organizations if it is a product. Managing the portfolio of infrastructure during the building and maintenance periods are journeys to manage.  

Capital/Funds Journeys

Organizations are usually very concerned with money, how it is raised, how it's used, and what becomes of excesses or losses. While these journeys are better established and repeatable, they often try to dictate the level of investment in the other journeys. Visionary management will satisfy short term results expectations along with building for the future, thus fund incrementally in various journeys. Having a proper governance journey or two is essential for the investors. 

Community Journeys

All organizations participating in physical and logical communities that can affect them positively or negatively, the reputation and the operation of an organization. As organizations join in legal frameworks, the best are necessary to plan and execute the journeys that fit those contexts. The results will affect the kind of outside direct or indirect governance for organizations and may set the policies or rules for other remarkable journeys or processes.  

Net: Net:

Each journey must be thought through and managed collectively and individually. Traditionally only portions of individual journeys participated in digital optimization or automation. For organizations to thrive, these journeys need to be served digitally from one end to another. The interaction between these journeys will show where organizational friction will occur over time. Also, the interaction within these journeys must be orchestrated in the context of continuous foresight with emerging expected and unexpected scenarios.

The good news is that new digital business platforms(DBPs) are emerging to integrate digital functions to service journeys better. Some will help with process fabrics; others will manage the intelligence well for better decisions, or reaction/guidance; some will manage data integration, and still, others will work at the edge to manage emergence.

 

 


Thursday, June 4, 2020

Delivering Success with Smart Data Streams

It is becoming clear that AI will be a critical competitive differentiator for organizations, industries, and even countries. It is also clear that many are looking for success stories to leverage into learning opportunities. As AI embeds its intelligence throughout organizations, the sophistication of the data usage will increase to a point where traditional data approaches will need to extend to include real-time data streams of images, videos, speech, events, and operational data. It means that new data approaches will be necessary. As AI gets more sophisticated at speed, its hunger for complex data becomes insatiable. As organizations learn to leverage AI, emergent problems can now be attempted. You can find strong case studies of emergent AI acting on data streams by clicking here.



Leveraging AI Starting with Machine Learning (ML)

Machine learning allows applications to learn from the data in order to make better decisions at speed. There is significant value in creating predictive applications that can smartly select smart actions that meet or intercept emergent data from multiple and intersecting contexts. This iterative learning and improvement cycles are driven by emergent data, shifting goals, and guardrails that are invaluable for organizations that want to stay in step or ahead of their market place and constituents.

Intermediate AI Applications Leverage Smart Streaming

As AI gets more sophisticated in its learning ability by applying deep learning and even cognitive thinking leveraging interpretation, recognition, scoring, intuition, reasoning, and judgment, the hunger for faster multiple data sources will grow. Streams of complex and evolving data will need to be utilized in solving both static and emerging problems.

Putting a Premium on Emergent, Fast and Agile Data Sources

Looking to the past is valuable, but today's demands require organizations to get in front of business events, constituents, and competitors. The data sources will include traditional and non-traditional data such as voice, video, and images. The speed and mixes of data types and sources will be dynamic and agile. Instant integration and transformation will be the norm to satisfy prediction and intelligence needs fueled by AI and analytics.

Net; Net:

AI is gaining momentum and is taking on predictive applications that leverage fast and agile data sources. As AI migrates to the edge over time, the notion of fast streams of event and pattern data will grow along with traditional big and fast operational data. Organizations that want to thrive and capitalize on leveraging AI and smart streams will get ahead of the curve by learning from successful implementations. Please click here to access an E-book for some impressive case studies that leverage AI-enabled smart data streams.

 

Click here for the E-Book entitled "The Future Starts Now" subtitled "Achieving Successful Operations of ML & AI-Driven Applications."

 

This blog and this breakthrough E-book are sponsored by MemSQL(an agile real-time database).

 


Tuesday, June 2, 2020

AI Devours Data!

Those who have worked on Machine Learning (ML) projects know that ML requires a large amount of data to train the resulting algorithms. Some would say you can never have too much data. There is usually a correlation between the amount of data and the sophistication of the resulting ML model. This data hunger is only going to get more intense as AI progresses towards new benefit pools while leveraging more sophisticated AI capabilities. Since there are other contributing trends bedsides the sophistication of AI, the question looms for organizations is, "do they have the right data to fuel successful AI efforts?" If they don't have enough, should they inventory more in anticipation of the AI feast?




Figure 1:  The AI / Data Continuum

It’s not likely that all that big data that organizations have been hoarding is the correct data, but understanding where AI is going will give an organization a "leg up" on culling and collecting more of the correct data as AI progresses during the next decades.

The Progression of AI Changes the Data Game

While ML requires significant amounts of data to self-modify its behavior, the appetite of AI increases quickly as the sophistication of the AI capabilities increase. There is a big step from machine learning to Deep Learning (DL) in that DL requires much more data than ML. The reason being that DL is usually only able to identify concept differences with the layers of neural networks. DL determines the edges of concepts when exposed to millions of data points. DL allows machines to represent concepts via neural networks as the human brain does, thus allowing more complex problem-solving. AI can also work on fuzzier problems where the answers are more uncertain or ambiguous. These are typically judgment or recognition problems that can extend to the creation or other right-brained activities. This again requires more data, which in some cases may be emergent or real-time in nature.

The Shift from Data-Driven to Outcome Driven

As AI moves up in the sophistication of the problems its assists or solves, it will become data-driven and goal/outcome-driven. It means that the AI may request data on the fly that it needs to solve a particular problem or make a specific deduction, thus complicating data management. It may involve the interaction of inductive data-driven portions of a solution with the deductive needs for data based on a hypothesis to reach a target. This kind of dynamic interaction is needed for outcome-oriented problems. It is much different than just interrogating the data looking for interesting events and patterns. Decision driven approaches fit right in the middle of these two distinct approaches. Some decisions are operationally focused and improved through matching data with outcomes. More strategic decisions will pick up on both inductive and deductive approaches. This is just another demand channel to boost data usage.

The Shifting Problem Scopes Impact Data Needs

The scope of AI solutions are will typically start narrow and move to wider scope over time, thus requiring more data. Complex solutions typically target more than one answer and will require more data to support the tributary solution sets, contributing to a complex/hybrid result. As the scope of decisions, actions, and outcomes span more contexts inside and outside an organization, more data will need to be obtained to understand each context and their interactions. Each of these contexts could be changing and morphing at different rates, therefore, requiring more data yet.

Net; Net:

It's clear that more data will be the hallmark of AI-assisted solutions. The data appetite might come from more challenging problems, the better leverage of advanced AI/analytics, or growing end to end value chains. One thing is for sure. Organizations had better get ready for the new world of “AI/Data Interaction”. It could change or extend data management policies, methods, techniques or technologies.

 

 


Thursday, May 14, 2020

Dealing with Emergent Data


The recent and ongoing battle with COVID-19 has raised a goodly number of issues in and around getting surprised, arguments around emergent data, and slow/appropriate responses. The lessons learned so far are pretty rich, but I think there is more to discover. Scenario planning seemed to be lacking, the early warning systems seemed to have broken down, responses seemed slow and unpracticed once the denial hurdle was overcome. This was not a "Black Swan" event, so why did this pandemic seem to throw a monkey wrench into humankind's systems and processes? It may a bit early as all the dust hasn’t settled yet, but there are some obvious conclusions even now. What can we say now?





Scenario Planning


While pandemics are a practiced and expected scenario, the level of detail in this kind of scenario was tested in new ways. There were new discoveries in how trends in supply chains were working against us. With longer supply chains for medical supplies and equipment, the stress put on existing supply chains because of panic buying of many items, including food, and how do deal with rescinding demand in finely tuned supply chains. The detailed scenario planning and modeling really seemed off the mark this time on a worldwide basis. There were also some emergent geopolitical effects not completely thought through for sure. Scenario planning needs to handle models with more emergence in a fine-tuned fashion. Businesses and individuals need to up their game in this arena as well.

Early Warning

It is not surprising that less than effective scenario planning would lead to missing emergent data that was not expected, but as the emergent data morphed/changed, there were shadow events, signals, and patterns that took longer to recognize. Early warning needs to be able to recognize event patterns that go beyond expected events. These events and patterns need to participate in more complexity theory and real-time recognition that understands emergence in complex and interconnected systems and supply/value chains. This starts out at endpoint detection and merges with associated and real-time unassociated events to create emergent patterns. Agents/bots sniffing at the edge and event responding in the case of known situations and contexts is minimal, but merging and emergent complexities need to be tested, and models/scenarios need to be updated in near real-time.

Appropriate Responses

It seemed pretty clear that many responses were unpracticed, emergency stores were over-whelmed and tactical responses were being invented on the fly. Since appropriate responses are dependent on in scenarios and early warning, the compounding effect on responses were evident. There were some pretty impressive examples of human creativity/inventiveness and sacrifice to make up for the deficiencies, but can we plan on this always happening for the good of all? We saw governments taking over supply chains, people quarantined late and long, and decisions walking the razor edge between mortality and economic suffocation.
  
Net; Net:

We can do better.  We have to do better as more negative scenarios are emerging as nature deals out an accelerating frequency of earthquakes, hurricanes, volcanoes, pandemics, regional famines, and shifting geopolitical events. I suggest we invest in Emergent Data Recognition (EDR) tied to improvements in scenario planning and practiced responses from a strategic and tactical perspective. There are lessons in emergent data that we have to be ready to leverage. The more prepared we are, the better it will be for us all. It's worth the investment in terms of life and livelihood.

Monday, May 11, 2020

Scenario Planning Is Essential to Survive & Thrive

I have heard many say that it takes bravery to complete a strategic plan and I would agree. I would also say that NOT having a strategic plan that stands up to many practiced scenarios is dangerous. We can see that in the case of COVID-19 in 2020 shows the importance of scenario planning. It's not just the fact that scenarios may have been planned for, they certainly seemed dusty and rusty.  I'm sure there has been some innovative thinking applied to our in-flight adaptation, but the rollout has had more than a few bumps in terms of recognition, speed, and coordination. The results have life-changing for all of us and sadly life-ending for too many.




Scenario Planning Must Stitch Together Strategy, Tactics and Operations

Scenario thinking and analysis generally uses simulation/gaming for policymakers to combine know facts with potential emerging situations in single or complex arenas of economic, geopolitical, demographic, military, resources, and industrial capabilities. This includes tapping models with data and combining them in a predictive fashion until a particular scenario seems to be imminent or happening with anticipated opportunities or threats. These can be detected by leveraging emergent data recognition by looking for signals, events, or patterns. For scenarios that are moving from possible to probable to active, links to emergent and real-time analysis and data sources. This will allow for adjustments to tactics and operations that flex to the best shape possible for the active scenario(s) with optimum operational actions.

Scenario Planning Must Consider Multiple and Ever-Changing Contexts

Often scenario planning can be blinded by a single laser-focused approach on a single probable scenario that leads to the desired future. There need to be exercises that leverage lateral thinking and hybrid sets of complex scenario combinations. The scenario planning effort is not a "one and done" effort, but needs to factor in emergence in the worst case and subtle changes in the typical case. There can be a quick shift from just a scenario to a real possible future and in rare instances probable scenarios. Best practices would expect organizations to identify all likely scenarios and even a few "black swan" scenarios.

Scenarios Must be Practiced to be Effective

Likely scenarios (all of the probable and some of the possible) should be practiced at a reasonable frequency. The practice should include surprise changes to test organizational skills, processes, and system agility/responsiveness. Even though these are necessary drills, all participants must be communicated to as if the situations are real. Many hospitals have evacuation plans that they practice
on a regular basis pretending there is a real event like a hurricane, chemical spill, or flood.

Net; Net:

There is a good lesson for organizations in watching the COVID-19 situation emerge. Organizations can no longer afford not to have up to date scenarios with practiced actions on the shelf that include both the tactics and operations. A communication program for practiced or active emergent scenarios is a must.

Thursday, May 7, 2020

Should We Start with Models or Measurement?


There is has been a running battle in and around measuring vs. modeling. It's come to a head with the COVID-19 Pandemic. There has been criticism on the models and their assumptions. There has been a cry for more data before releasing the economy and what speed the restrictions should be removed. While COVID is the issue today, there is a big issue going forward on any number of situations that involve Model vs. Measure. I would like to layout some guidance as a rule of thumb



Models are Great for Emergent Situations


In the absence of sufficient data for a situation that has rarely happened, models are ideal. Models are great for scenario planning, where there are numerous unknowns and untried situations. Models are helpful in operational planning where new approaches are being tried out before a significant investment of funds is necessary to change operations. Models are also helpful for people to understand complex aggregations of data from known situations. Models have a great impact when used properly and improved with real data over time.

Data is the Great Equalizer or Diviner of Truth

Having real data for known operational problems is the sweet spot of improvement and automation. There is little substitute for real data leveraged with know and true scientific methods or measures. While models might help visualize the data, the data is the key to assured decisions and lower risks. Some would say that all models are flawed; let's stick with the data. In fact, some would go as far as saying that the money and time used for modeling should be used to clean and normalize data and data sources. They have a good point for known and stable operational situations.

Models and Data Need Each Other

The real truth is that models nor data are ever perfect. Knowing this, we need to exercise wisdom in how to leverage both together, if possible. Models need more data to become more accurate, and data need models/algorithms to make the data digestible and in a form where the best the decisions have a chance to be made. I think the real issue is which one to start within any given situation. This is especially true where there is disagreement. Models let one try things out, and data, if correct and up to date, represent current conditions very well.

Net; Net:

Start with models for fuzzy and emergent situations. Start with data for known problem domains, hoping to discover opportunities.  Work towards a balanced and dynamic relationship between models and data no matter where you start.




Tuesday, May 5, 2020

Thank You For Your Support!

It's been seven full years since I left Gartner to pursue helping others progress with various forms of digital technologies. My commitment has been to help push digital technology forward in supporting real business goals, competencies, and progress. I am delighted to still be adding value to our collective efforts. I really want to thank my readers for continuing to be interested in my writings.

There were those who:

Learned something new
Were encouraged to think differently
Corrected my grammar, spelling, etc.
Corrected my thinking
Encouraged and extended my points
Pointed me to parallel efforts
Pointed to areas of opportunities

I want to thank you all and hope to continue to add value. So far my blog has had nearly 650,000 hits over the seven years spread across 537 posts yielding an average of over 1200 hits per post. My reach is now on Forbes and Data Decisioning where I have a pretty new presence. Some of the details of the activity are posted below. While most of the activity has been in the US (67%), there is a wide interest internationally. If you have ideas for further writings or just want to discuss something, please reach out to me at jim.sinur@gmail.com.



Net; Net:

Thanks for your continued support!!!  Stay in contact :)

Monday, April 27, 2020

The Deck is Stacked Against Customers

I always thought that customers were always right and serving them was always in the best interest of organizations and society as a whole. I had proof that this was the case in that organizations scrambled to install CRM systems, Mobile-first, Omni-Channel, and better processes with improved customer experiences. I actually expected that the organizations that were serving me would get better at customer excellence over time. Reality has set in recently as my service satisfaction keeps going down and I have to deal with websites that don't understand my needs.  Customer pain abounds as represented in a blog I wrote last year. Click here for the customer pain index.


Prospects are Treated Better than Loyal Customers

Buyer beware is an essential business principle that is always in play. Today there is a trend that is permeating in many industries. This is where the prospects get the good upfront deals and the loyal customers get raises in costs for no apparent reasons. In fact, many companies are now hiding the detail by sending summary invoices or just grabbing a payment from one of your accounts hoping you don't see or take the time to question the increase. Is this the way to treat your good base of customers? No, but it is happening all over.

Organizations are Just Putting Make-Up on Ugly Systems

While we are seeing better and better interfaces to package and legacy bespoke systems, the systems underneath are quite silo in behavior and data. While some of the underlying complexities can be hidden, there is only so much an organization can do to buffer customers from organizational and system complexity. While this is getting better, there still is a fundamental problem with the lack of supporting real customer journeys that cross organizational boundaries, Even customer aimed poster child, Amazon, can't track packages well once they hit the United States Postal Service.

Organizations Prioritize Their Goals Over Customer Goals

If you really knew how organizations incent customer service representatives, you would be shocked. They get bonuses and awards based on the number and duration of the calls. This is why they make you feel that you are just in their way. The really good reps figure out how to fool the system by putting you on hold or passing you to another person to help you get your outcomes while preserving their rewards. In some organizations, there are punishments for those who take longer on calls or handle under the average number of calls. Customer goals are rarely in play unless they happen to match the happy paths for organizations.

Customer Service Playbooks are Too Narrow

Organizations design guidance to customer service representatives in very specific and narrow ways. Rarely are exceptions considered even though they fool you by saying "the calls are recorded". The recordings are used to feed managers and CPAs with optimization of organizational goals in mind. The numbers are aimed at meeting internal optimization goals and not the customer's needs. The recording should be used to expand the playbooks too.

Systems and Supports are not Aimed at Real Customer Journeys

Real customer journeys go beyond individual departments to an across the departmental experience. Most organizations believe that the customer's experience starts and stops with their organization except to collect the payments. Organizations rarely look at the whole customer journey that is driven by their individual or aggregate goals. Some organizations actually measure the real journey within their complete organization, but few look beyond their scope of service. Fewer still look at individual customer's goals and really measure customer success.

Net; Net:

Organizations are patching all of the customer potholes over with surveys that just make you want to vote for that poor service representative that may have managed to make it work for you. Most of the surveys are designed to keep the systems as is so that rewards land in the pockets of the managers and sometimes the customer service representatives.  We are a long way from customer transparency and empathy. Any agility is used for profit motives and it looks to get worse. The CPAs are winning and the customers are losing. Click here to find out why.






Tuesday, April 21, 2020

How Can AI Super Charge RPA?


We have seen a rush to automation for time, money, and elimination of grief. Robotic Process Automation (RPA) has been on the cutting edge of this wave of automation benefits. RPA is changing its focus from just automating assisting or eliminating mundane work to seeding automated worker bots into processes and systems. The level of intelligence of these bots or agents is going up, thanks to AI. This post will concentrate on the new kinds of work that RPA is taking on now and in the future.


Today without AI


Today RPA is good at mundane tasks and reducing nasty work for people.  Typical tasks would include auto-keying, screen/form integration, application or data integration, automated decisions, and rudimentary task management. It can reach to simple task sequencing and simple resource orchestration. 

Today with Machine Learning, Mining & Analytics

As RPA moves towards straight-through processing, it will have to do forms of event and pattern recognition, informed decisions, and smart actions. This means that process instances, events, and other forms of journey data will have to be inspected and learned from at greater speeds. RPA may also act in an unsupervised fashion to adapt to change.

Tomorrow with NLP and Unstructured Content

Tasks that are knowledge-intensive will also need the help of the combination of AI & RPA. Natural Language Processing (NLP) can search structured and unstructured data for the presence of knowledge represented by the presence of entities and relations. This emergent knowledge can be captured by flexible knowledge taxonomies that can be leveraged in journeys, processes, or systems. The mix of unstructured data will also likely include image, voice, and video

Tomorrow with Deep Learning & Deductive Analytics

As AI advances to include right-brained activities such as judgment, particularly in context, RPA can make informed decisions based on leveraging the combination of AI & analytics. Deductions can be made after integrated information sources and knowledge worlds are run through advanced algorithms to take smarter action that considers multiple contexts.

Tomorrow with Cognitive AI & Predictive Analytics

AI can learn to think, learn, and project by employing predictive analytics, RPA should be able to intercept exceptions and match these patterns or events to expected or unexpected, opportunities, and threats. This puts organizations in a position to think through and respond to emergent behaviors and markets.

Net; Net:

As the combination of AI & RPA progress over time, the agents/bots will play a bigger role in making more automation possible to the point of completing more complicated work and shifting more satisfying work to human partners. At a minimum, these automation agents will assist employees and customers better than before.



Thursday, April 16, 2020

The Role of Models in the Emergent COVID-19 Pandemic

Watch Ed Peters, Irene Lyakovetsky, and I discuss the role of models in emergent situations where the data might not be complete or correct. One could speculate that we overreacted because of a flawed model on one hand or speculate that we save countless numbers of lives because we acted when we did; a flawed model or not. The economic impact of shutting down a 20 trillion dollar economy steaming along is immense. Also, the tremendous potential loss of life with no intervention is soul-shaking. Did we do it right? Because models are often flawed, should we cast them aside or should freshen them with real-time data like we did and are continuing to do? Review our Videos listed below or watch the full episode by clicking here



Role of  Models in COVID-19

All Models are Wrong, but Some are Useful
We can Learn from Flawed Models 
Localizing Models and Supply Chains
Boosting Digital Transformation


Make sure you follow or like our videos that appeal to you. Comments are appreciated to either  jim.sinur@gmail.com or  irene@saugatuckworldwide.com. You can comment on the post as well. 

Tuesday, April 14, 2020

Journey Intelligence is Under-Valued


It is a known fact that 75% of our customers are will to walk away from our respective organizations. Besides, almost 90% of customers say that poor service damages the impression of any brand. I think organizations believe these statistics and want to do something about these strong trends; however, they may not be going about it in the best way. We see organizations jumping on the bandwagons of CRM, retail online, mobile-first, omnichannel, customer-experience (CX), chatbots, smart digital-assistants customer journeys, and hyper-personalization. The question for organizations is, what should they do first, and how to sustain a true customer excellence program?



I think the key to increased customer satisfaction and sustained loyalty is Journey Intelligence. Understanding your customers and how they are shifting, almost in real-time, is quite dependent on journey intelligence. This isn't just about collecting and storing a reservoir of the customer and journey data as part of a big data push. It means truly listening to customers and matching it to their behavior on their customer journey, which may start before your organization's process and for activity beyond them. If organizations are serious about their true customer experience and want to guide their customer excellence programs, they will be spooling up a true Journey intelligence effort.


This means expanding the collection of data or information about the personas that the customers play over and above simple segmentation, the journey interactions of all personas involved with striking an outcome balance between the organization and the customers, the processes and systems leveraged, the technologies leveraged and the people resources at every touchpoint. Once this expansion of customer rich data has been completed, the next step is to add appropriate intelligence by mapping on the customer events on timelines, customer goal progressions, and the effectiveness of all resource support of customer goals.

This group of analysis/inference results will be over and above the normal metrics of revenue, retention, acquisition, churn, repeat purchases, and other traditional analysis. These enable teams and functional areas to not just understand who, what when, and where, why customers did what they did. This results in a turbo charge customer intelligence and even enable new forms of customer journey analysis. Organizations will also learn more about their customer classes and individual tendencies to inform future interactions. Journey intelligence will guide the efforts needed to up the customer excellence game plan. This is an ongoing challenge and set of efforts that will emerge. 

Net; Net:

Leaving Journey Intelligence out of the mix is a real danger for organizations as they falsely believe that they know what the real customer journey is for their customers. This is not necessarily a sin of commission, bit more of omission. They generally only consider the standard paths they over and assume the complete journey occurs in and around their particular organization. If organizations truly orchestrate relevant and consistently great experiences, they will both see reduced costs over time and revenue increases from refugees from the competition.




Thursday, April 9, 2020

For Those Who Want to Celebrate This Season

Peace to all.

During the spring of 2020 and this trying COVID 19 Pandemic, there is an opportunity to appreciate and celebrate the convergence of Easter and Passover. Both holidays celebrate deliverance offered by God. We Christians are praying for the Passover of this current pandemic for all.

The first Passover was a deadly angel of death that killed all the firstborn in homes not covered by the blood of sacrifice. Good Friday is about the blood of Christ sacrificing for all of us. Easter Sunday is about the resurrection of Christ who is the firstborn to pave the way to eternity for all that believe. I praise God for the free will to chose him or not. Respect to all that consider worshiping the Lord no matter the decision. Celebration for those who chose to worship the Lord. I hope for all to survive the COVID 19 Pandemic. Thanks to God for both acts of deliverance.

The Good Friday God:


The Passover Yahweh:



The Easter God:





Tuesday, April 7, 2020

AI at the Edge: Creating Coordinated Autonomy


Today organizations have to deal with so many emergent behaviors that the notion of central control as the only coping mechanism seems to be receding as a dominant management model.  Freedom must be doled out further from the centrist idea by creating goals, constraints, boundaries, and allowable edge behaviors. Someday, software and hardware agents will negotiate their contribution to business outcomes on their own, but until then, organizations will have to prepare themselves by managing coordinated autonomy.





Learning About "The potential" at the Edge

Edge computing is a form of distributed computing that brings computation and data storage closer to the location where it is needed, to improve response times and provide better actions. Now, AI on Edge can offer a whole lot of new possibilities. In Edge AI, the AI and other algorithms are processed locally on a hardware device or a distributed software agent. It uses data that is generated from the device/agent and processes it to give real-time insights in less than a few milliseconds and allows for pattern recognition, fast decisions, and better actions to deal with emergent conditions. We have seen practical applications in smart buildings, smart cities, and intelligent industry 4.0 supply chains. While most of the visible examples are in and around physical infrastructure, AI at the edge is starting to work at the customer, partner, and employee edge interfaces as well. This is leading to more utilization of software bots, assistants, and agents.

Trusting AI & Algorithms to Increase Freedom Levels

Today machines and software are programmed with rules that are preplanned and inflexible for changing conditions. Somebody needs to program those rules, decisions, and actions ahead of time. Low code or no code shortens the time to change for emergent conditions. Another great approach is to learn for the emergence and adapt the rules, decisions, and actions inflight. This requires a different trust level than in the past, particularly with unsupervised learning. By giving hardware and software goals and constraints, these freedom levels can broaden to deal with faster emergence. Organizations will have to learn new freedom and trust levels to take advantage of the speeds necessary to compete.

Enabling Digital Twins for Manage Adaptability

Every physical device and software agent will have a digital form of an interactive model that represents its logical self (twin). These models can be watched in interaction with other digital twins to observe, manage, and change their behavior. Mining the behavior of the digital twin will create an observable behavior that can be overlaid on timelines or other forms of observation. The digital twin is a practical way to understand if a hardware or software agent is behaving well or badly so that managers can take appropriate actions.

Net; Net:

Organizations will have to deal with emergent behavior whose footprints will likely be in big, fast, and dark data, events, or content. This will require new competencies, skills, and coordinated sets of digital technologies. Organizations that embrace and take advantage of emergence will likely leverage AI at the edge and manage it well for their competitive differentiation. 



Wednesday, April 1, 2020

Art for 1Q 2020

I hope you and yours are safe and healthy during these pandemic days. I delivered on a promise to my Grandson and painted Kale a dragon that we kind of designed on our last visit to Austin before Christmas. It was a fun piece to do even though there is a bit of a sinister feel to it. I also completed a couple more fractals for you to see. If you are interested in seeing my portfolio or buying a piece, please click here


                                                            Kales Dragon




                                                             Circle of Life



                                                            Bright Agent 

Thursday, March 26, 2020

Content Intelligence is Real


Today when organizations talk about content analytics, they generally are talking about the effectiveness of marketing efforts or website effectiveness. This is often productive but narrow leverage of content. Content is often the forgotten large vein of big data that has more potential for finding nuggets of business benefits. I would suggest there is a larger category of leveraging content than content analytics applied to revenue generation. I call this Content Intelligence.

Content Intelligence is the act of applying natural language processing (NLP), machine learning (ML), deep learning (DL), business intelligence (BI) and business analytics (BA) practices to a variety of digital content. Companies use content Intelligence software to provide visibility into the amount of content that is being created, the nature of that content, and how it is used.




The Unprecedented Growth of Unstructured Content:


While the has been a steady growth of traditional content and the importance of enterprise content management (ECM), the use of this content is being leveraged better in the new digital world. While digital methods have increased the productivity of the first mile of content-rich process flows, there is much more to leverage as we move to an increasingly digital world. Organizations are looking to turn audio and video into actionable insights in order to respond to issues in real-time, uncovering insights in large archives, boost the leverage with machine learning ad accelerating decision making.

Increasing the Utilization of Unstructured Content:

We see increased leverage of knowledge by systematically using NLP to find key nouns, relationships, and new leverage points. Imagine mining knowledge out of operations manuals to point to organizational knowledge sources or to create helpful new videos for better outcomes. Organizations are doing this right now. Imagine searching for moving images for defective parts of managing costs and increasing employee safety. This is happening today. Imagine equipping emergency personnel with active real-time maps of buildings and infrastructure during emergencies like active shooter situations. This is being done today. We are seeing the emergence of smart home and smart infrastructures in cities leveraging audio and video. You can expect more leverage of live and active content going forward because of the ability to mine nouns, relationships, contexts, and interactions. 

Digital Publishing is Utilizing New Digital Assists:

Creating content is now easier by guiding authors with better grammar, finding parallel writing that might indicate plagiarism, and creating topical indexes for finding useful resources or references. This is very helpful for traditional marketing and lead-generation over and above traditional research and "publish or perish" situations. Consider story mining of narratives to find successful approaches to creating better practices.

Net; Net:

While revenue generation is essential and analytics applied to revenue efforts, content is often an underutilized pocket of big data for organizations. We are in the early days of leveraging intelligence with various kinds of content. There is so much more to do with content intelligence, so it's time to increase your content IQ.



Friday, March 20, 2020

Covid 19 Videos: Too Much Data and Not Enough Focus

We have been hearing all kinds of data and analysis in and around the Coronavirus. In this set of sessions, we discuss the importance of setting goals before collecting and massaging data. Right now it's about not breaking the capacity of the healthcare system, but there are a number of other important things to optimize on as well. If you want to watch the full video, click here   If you want smaller snippets, click on one of the topics below:



AI, Data and the Coronavirus Video Snippets:

Outcome-Driven Responses
Build for Surges?
AI: Good or Evil
Models or Data?




Make sure you follow or like our videos that appeal to you. Comments are appreciated to either  jim.sinur@gmail.com or  irene@saugatuckworldwide.com. You can comment on the post as well. 



Wednesday, March 18, 2020

Beth Sinur; It's Been a Hard Year Without You

I think I speak for all who knew you. You have been greatly missed and we are all still hurting without your warm spirit. You lit up every room that you entered because of your positive spirit and attitude. People always loved when you showed up because you were so full of fun and surprises. We never knew what you would say or do, except when it came to having compassion for others. You loved hard and you were a front seat participant in life. We all miss you for sure.


You sang your way through life's trials and entertained us all. You loved "Pink" with all your being


Your beauty started in your heart and shone through often


The fun started early and you kept it going to the end


You so loved your family starting with your twin brother


The rest of the kids enjoyed your spunk as well. Nobody treated her grandparents better





While you were a storming jock that loved soccer, you cleaned up well



You loved all animals, but you had a special love for dolphins


We will all continue on without you, but we know you are watching and near. I hope you like the collage I made to hang on the wall of our home to remember you along with the pictures nearby. It's "Pink" and some pink dolphins to combine two of your many loves.


MISS YA GIRL !!!!

Family and friends in glory today especially Dad, Andy and Beth Sinur.