Tuesday, June 15, 2021

Art for the 2nd Quarter 2021

 It's always fun publishing creative works for me. COVID has been a boon for us creatives because we have all kinds of time to create. Besides the half dozen new songs that are in progress with my teammates, managed a couple of commissioned paintings and several new fractals. I hope you find some of these as eye candy for you. I even got to do a corporate logo :)

If you want to see my whole collection of art, please click here

If you want to hear my completed songs, please click here 


                                                   Moon Struck Owl 


                                                  Corporate Logo #1


                                             Rainbow Stallion



                                                      Crispy Layers


                                                     Fire n Ice

Tuesday, June 8, 2021

The Need for Real-Time Fast Boards

 

It is becoming painfully apparent that traditional dashboards are too slow and static to compete in our changing world. While COVID-19 has driven the point home, there are more changes to come that will be demanding faster and more innovative solutions for competitive decisions. Traditional dashboards fail to deliver on the data-driven culture as data speeds up and sits in the cloud for new AI and advanced analytic assisted decisioning. According to the Harvard Business Review in 2020, 84% of frontline workers report a poor experience with today's analytical solutions, and 67% of executives are not comfortable using data from their existing data resources. It implies that the problem with decisioning is that there is more than keeping up with known or emergent KPIs.


The Need for Speed

Traditional dashboards do not deliver modern world speed because they are fragile and use old data. Most decision-makers are dealing with data that is 4-5 business days old at best. If a decision-maker wants a new insight or tries another decision-making approach, it takes too long to change the existing dashboard. The data is slow; the dashboard changes are time-consuming and expensive. It is no wonder only a small group of executives believe their existing analytic solutions are mature enough to keep up with the organization. Faster decision data in the cloud is one step in the right direction.

The Need for Complete Context

It is so easy to focus on fragments of a decision and sub-optimize. Often getting data about critical neighboring contexts and downstream impacts of decisions are overlooked, or the time-pressure causes this step to be skipped. Often siloed analysis contributes to a fragmented insight situation. It can be caused by organizational boundaries, various tools, and incomplete data sources. Therefore organizations are moving from a patchwork of disconnected dashboards to management cockpits.

The Need for Collaboration

A wise decision-maker will often check with other fine minds and experience bases to attain decision excellence. Collaboration alone can help knock down some of the organizational walls and resist a significant decision, and take appropriate actions with speed. It is beneficial in verifying the data and the potential downstream outcomes of a decision and the resulting actions/impacts. Often the collaboration tools are devoid of decision contexts and are a separate disconnected toolset.

The Need for More Smarts

Even these fast boards need assistance with detecting necessary signals, events, and patterns. It is mainly an issue for emergent situations where the signals, tolerances, and patterns were not anticipated. Since there is no way for the human eye to catch these speedy emergent patterns, there is a crying need for AI and Analytics with insights to detect these patterns in real-time minimally and predict the emergent trends ideally.  These same smarts will help us personalize the analytic outcomes and new fast bords that play in a management cockpit.

The Need for Dynamic Experimentation

Once detected, the decision-maker can orient his/her options with the help of collaborators. Some of these collaborators should be AI and advanced analytical models that can project the impact of a decision and resulting changes. The ability to experiment with a range of analytics producing various outcomes is a must in a world of increasing speed. It may imply re-using models and analytics that have proven helpful in the past. All of these contribute to an immediate use-case development environment.  In today’s market only a few players seem to understand what decision-makers want including SAS, Tibco, ThoughtSpot, and Wizly

Net; Net:

The tension between dynamic data and static siloed dashboards has to be resolved in a world of change. We are no longer in a steady-state, and I expect more issues to respond to with fast boards applied most intelligently. If we can’t do that, we will never scratch the surface of all the big and complicated data we are capturing today and storing for tomorrow. It’s time to modernize decision-making to help optimize operational outcomes while sharpening organizational tactics within emerging opportunities and threats that may deliver new strategies.

 

 


Thursday, June 3, 2021

What’s Driving Data-Intensive Applications?

Today and in the foreseeable future, huge waves of data-intensive applications are breaking over us, with more waves to come.

It’s not just the data volume, often referred to as "Big Data" or "Monster Data," which pushes opportunities in the direction of organizations. It’s also the demand-pull of applications, processes, and journeys growing in importance for organizations to compete. These data sources are often measured in terabytes or petabytes, but “being large” is just the obvious, in-your-face description of what comprises a data-intensive application. In these apps, the data is commonly persisted in several formats and distributed in many locations and must be cared for in various ways for organizations to flourish. Coping mechanisms will be described in future posts, but this post identifies the drivers of data-intensive applications.


The Demand-Pull Drivers are Data Hungry

Because of the pressures on organizations to expand their views on the scope and impact of applications, there is a considerable demand for more data as focused and simplistic applications transition to intelligent large-span applications. In addition, the speed to detect emergent signals, events, and patterns is ever-increasing, putting pressure on follow-up decisions and appropriate actions. It’s much like a fighter plane that has to make decisions and take steps in seconds; however, management is used to working in days, weeks, or months.

Moving from Dashboards to Fast Boards

Today's organizations need to anticipate critical patterns to intercept opportunities and threats at more incredible speeds to make decisions and take appropriate actions. Some organizations are crafting technical sentinels that sit on the edge to sense and sometimes respond if given the freedom to do so.

Excellence with Management Cockpits

The idea of people watching many individual dashboards/fast boards and integrating their contexts with speed is somewhat an unrealistic expectation. Minimally, these need to be brought together into a management cockpit to grok the intersections of the visible measures. These measures range from KPIs to out-of-tolerance situations. Eventually, the management cockpit would be assisted by bots/agents to notify management of threats and opportunities. Ideally, these management cockpits could help in a "fly-by-wire" fashion within practiced business scenarios. 

Decision Management and Assistance

Besides the speed to sense, decide and respond, data has to be available to venture into new contexts to aid the decision-making process and play out the ramifications of any action about to be taken. Operational adjustments may require simple tuning or kick-off other individual efforts. Tactical moves require new versions of rules and critical adjustments of guardrails and constraints resulting from decisions. Strategic moves require some forms of advanced analytics and potentially gaming alternatives through a management cockpit. 

Value Chain & Supply Chain Extensions

Today, an awakening occurs that requires knocking down organizational/skill walls to eliminate silo thinking and actions. There is a race to kill silos in value-chain and supply-chain situations encouraged by businesses partnering to produce products or services. There is a premium on innovative collaboration that crosses all kinds of boundaries to create overarching goals and results while satisfying individual organization units at the same time. The goals, rules, policies, and constraints need to be tweaked simultaneously during operations in the middle of changing conditions. 

Supporting  Journeys

There is a considerable push to define constituent journeys, especially customer journeys, that are often integrated with employee/support journeys. Journeys require an outside-in perspective requiring more data to represent specific goals of the personas and individuals interacting with an organization. The customer experience is tracked, measured, and recorded with sentiment data, often represented by voice interactions when live via a representative or chatbots. The data around loyalty and satisfaction proliferate with an outside-in journey perspective.

 

The Data Push Drivers Overflow

Data offers opportunities in its new forms, amounts, locations, and captured contexts. Until now, the "Big Data'' headline has been driven mostly by the volume story. That is about to change with the new data types and formats that are entering the organization. In addition, there is a new generation of the distributed types and a movement that says that views can be constructed no matter where the data resides at the moment. While location complicates data quality and compatibility issues, there are alternative ways to cope with new tolerances for perfection depending on the usage described in the demand-pull section above.

 

Voice Data

Voice is a key new tributary to tap for organizations, thus tempting leverage for competitive advantage, particularly in servicing processes/applications. Voice can be leveraged to see how often competitors are mentioned in calls. Voice can also be analyzed for emotional reactions in the context of the servicing experience. It is often helpful in unscripted situations, which often occur with service representatives who are skill specialized. Now it’s not just NPS scores that count for customer satisfaction measurements.

Image Data

Images can be helpful when brought up in context. Not only can physical plant layouts and machinery be checked for safety purposes through image analysis, but broken machinery can also be detected before a significant or cascading problem occurs. Not only can out-of-bounds situations be seen, but optimal real-time planning can be enhanced by image detection. Real-time image help is a must for some jobs.

Video Data

Videos can be leveraged for better productivity, such as optimizing worker movement for better quality and faster processing. Video can be used to identify resources in action, such as people and machinery, for various kinds of operational optimization and training opportunities. Imagine showing an inexperienced worker a video on how to service a particular component such as a pump after taking a video of it in operation provides self-paced, on-demand training - without asking experienced workers for help.

Edge Computing Data

Data can be detected at the edge before it hits mainstream processes/applications. An unexpected event can trigger the notification of an emergent set of conditions or patterns for real-time decisions or actions in most cases. With the proper freedom levels, goals, and guardrails, this reporting creates a bevy of data for each node at the edge, whether a sentinel or an actor.

Distributed Meta-data

The data about the data is often called meta-data. With the sophistication of data storage and state combinations, meta-data is important to properly manage the distributed data sources. In addition to the physical state of data, such as its location (on-premise or cloud storage), its state of meaning, transformation, source, and context can be managed with meta-data.

Net; Net:

There are so many trends contributing to data-intensive applications that there will likely be another new one later today. New ways to manage traditional and new coping mechanisms will emerge over the next few years. I'm betting that both the demand-pull and the data push drivers will only accelerate. We are in the middle of a massive revolution for managing data differently. Be aware and get ready - data-intensive applications are coming for your organization.