Are Masks the New Accessory?

When COVID 19 first emerged, a number of us scrambled to get any mask we could. In our home, we first went for the standard paper mask from the Pharmacy. As COVID 19 got to be a bit more pervasive and scary, we upgraded to N95s or K95s and some double layered cloth masks, mostly in black. Now masks are getting better looking, so I thought I'd put some of my art on masks to see if there was a demand. Indeed there was. It seems the folks that want masks, want something nice to look at. Here are the masks that I'm offering. I can be reached through if you are so to give me feedback or even have one in your possession. You can see more of my art by clicking here

Acceleration of Decisions Helped by the Database of Now

Things were going along nicely until COVID 19 hit, and it was “game on” for rapid decision making. Executives were slammed from operational optimization with known decision/static models while transforming incrementally to digital towards a world of large amounts of decisions made in short time frames. The vast majority of organizations had not planned for this kind of scenario, thus not practiced to handling it. The acceleration to some form of digital and remote workers was instant. Our executives were bombarded with one critical decision opportunity after another, and many were up to the task thankfully. Is the question "Is this a one-off situation"? I would argue that maybe not the exact same scenario, but the beginning of many emergent situations at various corporate performance levels. How does the Database of Now help

Integrated Data to Support the Lateral Thinking in Decision Making

Traditionally decisions have been generated by new management goals and finding "aha" discoveries in data? While this approach will continue, forced innovation will be a necessity driven by these large-scale and emergent scenarios, such as changing markets, customer demands, extreme competition, and the desire for better outcomes. This shift will require more data and new complexities to continue to monitor, decide, and take appropriate actions. Intercepting the changing future will also drive towards adapting to and integrating new data resources, many of which will be cloud resident. The Database of Now supports dynamic and easy integration of new/emergent data sources.

Fast Data for Analytic Assistance, Guided, and Flexible Implementations

Decision-makers will demand assistance in making informed decisions fast and understanding the ultimate impact of their actions in planning and execution modes. The first demand will be for fast data supportive before the actual decision occurs. Lack of speed kills but so does speed without anticipating potential outcomes. Analytics will greatly assist decision-makers in understanding the possible consequences of their impending decisions. Once the decisions are made, the emergent effect will need to be tracked, monitored, and measured during rollout. Once implemented, fast feedback loops will help guide adjustments for better performance while sensing emergent patterns for potential new decisions. The Database of Now is designed for speed.

Smart Data that Leverages Machine Learning and Other Forms of AI

Today, most decision-makers are highly involved with the decision-making process unless they can be easily automated, usually using decision models. Typically these decisions are operational and static in nature, but there is a strong trend towards flexible change and emergent business outcomes are driven by new responses to integrated response scenarios. Either way, forms of AI can speed the decision-making process, starting with machine learning that watches conditions and outcomes. Deep learning can sharpen the focus for even better results. This kind of leverage is often described as smart data commonly used in supervised learning situations. With the advent of emergent and complex conditions driven by expected or unexpected events and emerging patterns, AI will take a more judgmental role in an unsupervised fashion. The Database of Now is ideal for having the most up to data and contextually sensitive data sources necessary for high intelligence.

Net: Net:

The future will require rapid decision making that needs speedy data that traverses many data types, monster data volumes, and growing complexity.  There will be quiet periods of optimization that will also benefit from the Database of Now, but get ready for waves of emergent situations potentially never seen before by the modern decision-makers. It may turn decision making on its head changing from only modeling operational decisions into crisp responses also to include emergent decisions dependent on complex, fast, and shifting data sources. Will you be ready for fast and effective decisions for customer needs and operational effectiveness?

•            Customer experience demands responsive and instantaneous data.
•            Business operations insights enable instant adaptations for changing market needs.

Additional Reading:

IncreasingCorporate Performance with the Database of Now  

Context: TheConnecting Clues for Data  

DeliveringSuccess with Smart Data Streams  

 

 

Increasing Corporate Performance with the Database of Now

Organizations no longer have the luxury of sitting back and waiting for an opportunity to react. Corporate performance depends on intercepting the emerging future quickly, thus putting a premium on the Database of Now. We can see many examples of the inability to pre-build strategic responses to emerging conditions such as inverted yield curves, new super competitors, hyper disinflation, currency shifts, pandemics, ECO events, and geopolitical shifts. So how do organizations take advantage of the database of now and build for interacting response cycles? The answer is to create a database of now and leverage it differently at different levels in the organization (See Figure 1) while trying to extend reaction to preemption. The interaction will be changed at different levels and cascading levels of strategy, tactics, and operations.

Figure 1 Interacting Response Cycles

Organizations are running in an automatic mode within normal conditions; they take actions without a lot of thinking or bother. The problem today is that automatic mode is not happening consistently with profitability like it has in the past because of emergent conditions. These conditions can emerge from a variety of sources represented by fast and large growing sources of data. These conditions can come from outside the organization in either an anticipated or unanticipated manner where they are not as controllable. These conditions can come from inside the organization to optimize business outcomes through observation or management influence in a controlled fashion. See figure 2 for the common sources of new conditions. The causes include changes in data, patterns, contexts, decision parameters, results from actions, changes in goals, or new risk management desires/demands. These changes are occurring on a more frequent basis and at a faster speed, thus creating the need for the Database of Now. 

Figure 2. Sources of Emergent Conditions

Keep in mind that each level's triggers in Figure 1 will likely be different, iterative, and possibly influenced/interconnected by other levels.

Operations of Now:

Operations are focused on completing business events, customer journeys, and work journeys with the support of humans, software, bots, and physical infrastructure. The operations are often iterative and monitored in a near real-time fashion. Today's operations require a Database of Now where the dashboards reflect actual progress/completion of work. When exceptions emerge, responses are required within the constraints of existing operational goals to make minor adjustments. Also, significant adjustments need projects that may leverage a fail-fast approach to make corrections. Operational goals are often influenced by changes initiated by tactical and strategic decisions and adjustments. Analysis and reporting help make appropriate adjustments without unseating other operations.

Tactics of Now:

Tactical management within the constraints of strategy tends to optimize interrelated outcomes that may look across multiple operational domains. Real-time forecasting based on real-time data is essential to predict the direction of aggregated operations. The Database of Now plays a crucial role in making better decisions by quickly changing rules to optimize business outcomes in support of strategic goals and directions. Tactical changes may imply shifting resources, changing rules, goals, and constraints of aggregated operations. Often key projects identified at this level, like recognizing patterns that might indicate the need for a new product or service. This is also the level that decides the amount and type of automation that will help reach the currently selected strategy.

The Strategy of Now:

Strategies tend to stay stable and are highly linked to the organization's missional operations within Its typical communities and common scenarios. Predictive and prescriptive analytics help shape expected scenarios that may be sitting on the shelf with their associated tactics and operations ready to jump in at a moment's notice. Of course, the Database of Now can point to a playbook for switching scenarios when expected patterns emerge. Still, unexpected patterns can generate the need to apply new scenarios generated by more predictive and prescriptive analytics. 

Net; Net:

It is pretty easy to see that fast monster data will create the need for the Database of Now necessary for better performance at all levels (strategy, tactics, and operations). It is also clear that fast data without time lags generated by too many synchronizations and transformations is necessary for better corporate performance while keeping all contributing resources aimed at business outcomes. 

 

 

Best Visual Options for Process Mining

Until bots can be cognitive enough to complete closed-loop improvements on processes or data stores on their own, visualization for humans will be key for making process improvements. Today many of those improvements are made through data mining in real-time or after the fact by humans. They do it by setting tolerances and monitoring outcomes or looking at the visualization of process instances that travel through processes or collaborations. The best visual options for any organization will depend on their culture, maturity, and desired business outcomes. I've laid out three categories of process mining visualization techniques that typically match maturity levels. I have used examples from vendors to help sort out the options, so your favorite vendor may have been left out of this post. 

Basic Visualizations

Basic visual analysis sometimes starts with an ideal process, sometimes called a "happy path", and look for the actual paths taken by a process. Organizations sometimes start with the outliers and try to reign them in closer to the ideal. Other organizations start with clusters of most common deviant paths and try to improve them. See the visualization below for a representation of this approach. Most organizations do a before and after to measure change effects, also depicted below. This shows the process before changes are made and the resulting process with deltas in certain instances. 

Intermediate Vizualizations

More mature organizations try to add important business contexts to show the actual delivery made by processes in terms of key measures. One of the more important contexts, shown below, are the actions shown on a timeline. This gives "time to results" a high priority while counting key costs and resource utilization specifics. This is an effective way to eye-ball opportunities. Another key approach is to show the process instances in light of desired outcomes versus real outcomes usually represented by dashboards or scorecards also depicted below. This is the start of the journey to adding more intelligence to the process of mining efforts. Simple Step through visualization with or without simulation of proposed changes is another nifty approach pictured below. 

                                    

Advanced Visualizations

One of the proven visualization techniques is animations that attract humans to opportunities through either speed or color indicators. This typically shows choke points and bottlenecks, but there are additional uses to simulate alternatives to show the value of different change opportunities. See below for an example. Predictive analytics combined with virtual reality can be used to visualize points of view or personas to fine-tune processes from different perspectives walking through a process or journey as depicted below. For those organizations that want to learn as they go, they can add machine or deep learning to improve processes as depicted below. 

Net; Net:

The visualization approaches can have a great impact on the resulting processes and finding opportunities for more automation, tuning for better results, and trying alternatives without the negative impacts of breaking or breaking optimized processes. Your chosen visualization might be a personal preference, but as organizations mature more sophisticated visualizations will be needed until the smart autonomous bots or agents can do this work as a partner or autonomously.  

Is Your Data Smart Enough?

The state of data affairs over the last ten years or so revolved around big data. Of course, size matters, but big data promises to morph to monster data as more data sources hit the cloud with more tributaries like voice, video, IoT, events, and business patterns. So what about all this parked data? Are we going to keep storing it and bragging about how much cloud space it consumes?  Are you going to make it cleaner and smarter or just admire it? I would suggest we make data more intelligent and faster than just figuring out how to catalog and park it, so we can use it later. Making it faster means treating the data as a database of now, now of the future. Making data smarter can be tricky, but it is worth it.

Gleaning Data is Basic Intelligence.

Capturing data of different types and classifying them is pretty normal. Deciding how long to and where to keep it is essential. Determining if it is worthy of a long time archiving is doing data a solid. Knowing some basics about the data source and cost of acquisition and relative purity is pretty much a given these days. Some data cleansing and organization will help usage down the road.

Giving Data Meaning is Average Intelligence

Knowing the data about the data (AKA meta-data) is essential for interpreting it. The simplest is understating the data’s domain and its relative relationship to other data (logically or physically). Data representation and transformation options are pretty essential when combining with other data. Knowing the key or identifier of groups of related data is pretty standard. This step is where some of the impurities can be dealt with before heavy use. First use usually revolves around visualization and reporting to find actionable insights. This step is turning descriptive data into a prescription at times.

Granting Data Representation in Its Context is Very Smart

Most data is gathered and used within one or two base contexts. One is undoubtedly timing/frequency, and the other is the primary home of the data. For instance, the entity family it belongs to like product data. Sophisticated context representation will go beyond an original context or source to include others that have a neighborhood relationship with the data grouping/entity. An example would be a product within multiple markets and channels. This level is where statistical and predictive models enable more actions to either react or intercept the trends indicated in the data. This level is turning prescription to prediction to create/place data, event, or pattern sentinels on processes or the edge to look for prediction completion or variants.

 Grinding Data to a Fine Edge is Smarter

We are interrogating data to learn the need for important adjustments to goals, rules, or constraints for operating processes that include humans, software systems, or machines. This level can build a change to work in a supervised or unsupervised change process. This level starts with machine learning and extends to deep leading, which peels back layers and interrogates more data. In extreme cases, the data can be used to support judgment, reason, and creativity. The worm turns from data-driven to goal-driven, established by cognitive collaborations with management principles, guidelines, and guardrails.

Grappling with Data in Motion Right Now is Brilliance

The pinnacle of smart data is where the data coming in fresh is used to create the “database of now”.  At this level, all of the approaches above can be applied in a hybrid/complex fashion in a near time/ real-time basis. This level uses the combined IQ of all the AI and algorithm-driven approaches in a poly-analytical way that leverages the brainpower combined with fast data. A dynamic smart parts creation and dynamic assembly line would be a non-combat example. 

Net; Net:

Data: Use it or lose it, but let the data lead to the learnings that sense, decide, and suggest responses appropriate to action windows necessary to meet the timing need. If it is a sub-second focused problem domain, the patterns in the data and intelligent methods may make the decisions and take action with governance constraints. If not subs-second focused, let smart notifications or options be presented to humans supervising the actions. Don't leave all the precious data parked for the future only. 

Art for 2Q 2020

I hope you and yours are safe and healthy during these pandemic days. I delivered on a promise to my Granddaughter and painted Gabriella a beach scene that we designed on the phone right around her birthday when she called to thanks us for her birthday gift. It was a fun piece to do even though it is difficult to do sea scenes. To say the least, I learned some lessons for the next one, but she was quite pleased with the results. 

It was a great quarter for art sales. I sold seven pieces to two collectors. Two were fractals and the rest were paintings, which is quite different than my normal. Fractals have sold two to one in the past  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

                                                         Serenity Beach

                                                              Happy Koi

                                           

                             

                                                             Circle Saw

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.