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.



Wednesday, May 26, 2021

What is a Digital Transformation?

I've recently been asked to describe and define transformation so that organizations, end-users, and vendors can claim transformation. According to the multiple definitions I found on the web, "A transformation is an extreme, radical change" So what do we deem extreme and radical? I would say that while pursuing a digital program, an organization discovers a new or dramatically extends a business model. An example would be an insurance company that became so great at insurance claims that it started a subsidiary to manage other organizations' claims. Another would be if an organization had a significant change in its competencies and skills that it looked and behaved radically different. However, there are incremental ways of sneaking up in these sweeping changes and transformations. So it might take a while to claim a true digital transformation.

While I don’t think that transformation ends, there are points in the transformation process to declare an organization transformed. To that end, I tried to develop a way to measure if the transformation effort is significant. Besides the softer sides of organizations like culture, organization, competencies, and skills that are harder to measure, there are five dimensions of change that I was able to noodle out to describe here. I'm sure this will morph over time, but this is my first stake in the ground, and I'll go from here. See Figure 1 for a spider diagram (aka Kiviat diagram) of the dimensions of transformation where I showed a typical traditional process or application measured on the diagram. The idea is to move the measurements to the edge as depicted by the red arrows. The five dimensions are described below. While the shape will vary by organization, a transformation would occur with an average of a "4" for each measure.

                                       Figure 1 Transformational Dimensions

Innovation: You can find many business leaders and business GURUs saying that innovation is the new area for competitive differentiation. I find this hard to argue with as many new digital technologies are emerging as business climates are changing and new/non-traditional competitors are entering many industries. So organizations that can match the many moving parts of customer need with the emergent set of digital technologies at the right time will be pretty innovative. Like it or not, change is accelerating, and how organizations deal with it will make the difference in the survive, thrive, and capitalize continuum. If you are reacting to table-stakes change, you might survive. If you are collaborating or ideating on better solutions, you can go beyond survival. If you are "built for change" and practice agile approaches, you are more likely to thrive. If you practice "Out of the Box thinking and implement it before others, you are likely to capitalize. Pushing this dimension to the edge requires a stomach for risk. Take the risk to become innovative.

Personalization: Today, if you know your customer and have much of the data accessible in one spot or as few as many, you have a good chance for survival. However, this is the minimum. You need to know more about your customer, which notably includes their overall goals and the goals of each interaction with your organization. Organizational goals will often be at odds with customer goals, so striking a balance between your organization's goals and your customers' goals will be essential. This goal confusion is where digital assistance and real-time analytics can help sharpen focus on what the customer really wants. Listening to the customer sentiment emerging in their voice and moving images can tell you a lot at the moment or over time. Customers do not just want standard transactions aimed at organizational outcomes; they want better practices aimed at their whole journey. This process includes transactions outside of your organization's scope at times. This process applies to employees, partners, and vendors as well. Pushing this dimension to the edge will imply more short-term costs, but the outcomes will be more profitable overall in terms of satisfaction and loyalty. Invest in your constituents.

Scope of Impact: Often, short-term costs and timing can be wrung out of departmental processes and workflows to the delight of the accountants and the department heads. However, cross-organizational methods that consider the goals conflicts between organizational units have proven to deliver more benefits over the long haul. The short-term benefits for any department may not be optimized, but the overall outcome will be better for all. Savvy organizations will look at their internal processes and consider comprehensive strategies that include external organizations. Some organizations have outsourced tasks and functions to make them cheaper at the cost of the end-to-end process. When something goes wrong in this case, the "finger-pointing starts."  More progressive organizations will look at complete value chains, entire supply chains, along customer/employee journeys. Pushing this dimension to the edge takes longer and costs more, but the overall solutions are better. Journeys constitute an important principle included in Industry 4.0 that pushes this dimension to the edge. Break down the walls inside or outside your organization.

Automation: Hyper-automation is a popular term today that combines the automation benefits of many digital tech streams. There are many benefits in this particular dimension that have driven BPM, RPA, and Mining. While this is a good direction, this automation needs to become intelligent and learn to become better over time. The collaboration of man and machine starts to emerge to augment the humans involved in the processes. These and future automation will be free to sense, decide, and act independently as they learn over time. However, automation will need to be driven by goals and guided by constraint guard rails. As more business conditions, events and patterns become emergent and changing; this dimension will travel to the edge over time. Free your automation to seek goals and be guided by constraints.

Secure Digital Tech: Digital technology will need to emerge and mature. Organization's experiences with each technology stack, such as iBPMS, RPA, Machine Learning, Mining, Data Mesh, Hybrid Cloud, Deep Learning, Distributed Database, Chatbots, Knowledge-bots and Bots/Agents on the Edge will play an essential role in the future. These unique digital technologies have started to converge in profitable pairings and end up Digital Business and Technology Platforms that work well together. Over time they will become competent and help organizations self-adapt. Combine digital technologies into platforms for better leverage.

Net; Net:

There are no universally accepted transformation definitions that guide organizations today. This writing is my attempt to start one, and I hope it evolves. You will see me use the above dimensions to rate example implementations to show if a transformation is impactful enough to be declared a transformation. Until then, each vendor will claim transformation victory, and organizations will make changes incrementally. Remember that closer to the edge means real transformation. Also, remember to give your organization credit for softer progress implied by skill-building that leads to competencies.

 

 


  

Tuesday, May 18, 2021

Attaining Real-Time Strategy Adjustment

It was pretty much a given that strategy was done on an infrequent basis from one to three years regularly. The static approach to strategy is no longer feasible or even advisable with the amount of change occurring in the real world. The days of steady-state for long periods are numbered. We see supply chain delays, geopolitical shifts, environmental events, plagues, and competitive landscape shifts, all expecting management to deal with the strategy adjustments. These kinds of push events tend to be reactive and mostly unplanned for most organizations. It may mean reprioritizing efforts and introducing new technologies.

The data is coming on faster as we move from dashboards to fast boards on the pull side of strategy. Because management wants to be proactive on operational and tactical adjustments, there is also a push for aggressive actions highlighted by a management cockpit that enables visualization, understanding, and contextual analytics and predictions. The need is for understanding the current state in contexts and steer to the best outcomes delivered by a variety of solutions represented by new projected conditions. It is not to say that there won’t be operational challenges that need to be dealt with alongside strategy adjustments which could likely include managing work better, measuring performance, inspiring workers, and keeping up with trends. However, there could be potential culture changes, mergers/acquisitions, and leadership changes.

Addressing Real-Time Problems & Concerns:

Up until now, the advantage of real-time or near real-time results on the scorecards and dashboards just weren’t a common tactic. With the advent of real-time data meshes that grow in terms of problem and context scope on the cloud that is easy to link up to, the opportunity to address problems and concerns in a near-immediate fashion is real for many businesses today. Things are speeding up for organizations to cope with large amounts of change and even "big change" scopes.

Understanding Contextual Implications of Specific Situations

Understanding an event, a trigger, or a new pattern can also be much more insightful and associated with other moving parts of a situation that may only be emerging for the first time. Understanding a problem, an out-of-bounds pattern or alarm in its actual context and scope will significantly differentiate the excellence in resulting decisions and appropriate actions, both reactive and proactive.

Collaborate with Others for True Success

Now managers don’t have to observe and orient themselves in a vacuum. Collaborating with others quickly and responsive can also expand insights and test new insights for decisioning and taking intelligent actions. The more perspectives and experience a manager can apply to an emergent or repeating situation, the better the long-term outcome is for organizations.

Survive, Thrive and Capitalize with Innovation

Today innovation is turning into a new digital currency that does require taking unnecessary risks. Innovation, as well as decisions, can leverage the collaboration mentioned above. Being able to innovate on operational improvements, the tactical angles for competition, and new products and services is the typical way organizations succeed. Using key analytics for impact analysis helps the innovation process project results for future state management cockpit results, thus reducing risk.

Balance Management with Risk Guardrails

The balancing side of innovation and change is doing proper and more immediate risk analysis to anticipate both good and bad outcomes. Risk guidance keeps organizations from avoidable dangers. The same kind of insightful analytics can help set up the guardrails and tolerances for notification of violation.

Net; Net:

It is vital to anticipate, intercept and engage in change because the time to market response is essential for competitive advantage. Sitting still is not an option anymore because you will be facing reactive change at all levels; organizations will have to become adept at real-time strategy adjustments. Hopefully, your organization will practice this in a proactive fashion and know when to shift goals to make or keep them relevant. With the help of business strategy software such as a management cockpit, organizations will handle change well.

 

 

 

 

Tuesday, May 11, 2021

Speed, Scale & Agility Delivered with Distributed Joins

Organizations are driving towards faster decisions and actions across more comprehensive ranging data sources than ever. Broader scope means multiple data sites because of business drivers alone. The distributed join is a query operator that combines two relations stored at different locations. Because the cloud-based distributed database creates many more data storage sites, the trend towards distributed joins is strong. The implication is there will be many more distributed joins in your future. This situation puts a premium on handling larger/broader scales of data and dynamic join capabilities. 


Why the Move to Distributed Databases?

We all know that distributed databases allow local users or bots to manage and access the data in the local databases while providing global data management that provides global users with a global view of the data. Because distributed databases store data across multiple computers, distributed databases may improve performance at end-user worksites by allowing transactions to be processed on many machines instead of limited to one. Increased foresight with tuned distributed databases can be used for business transactions plus analytical-driven business strategy and tactics. The drive to the cloud leveraging incremental relocation and more operations occurring at the edge with intelligent automation all feed the distributed database trend.

Advantages of Distributed Databases

Distributed databases provide some real benefits in the agile world and fall typically into these four categories:

·        Better Transparency: Users have the freedom from the operational details of the network, the replication (multiple copies of the data), or fragmentation issues in the data.

·        Increased Reliability/Availability: Because data can be distributed over many sites, one site can fail, and the data usage can continue.

·        Easier Expansion: The expansion of the system in adding more data sources, increasing data size, or adding more processors is much easier.

·        Improved Performance: A distributed DBMS can achieve interquery and intraquery parallelism by executing multiple queries at different sites by breaking a query into several subqueries that run in parallel.

Distributed Joins 

To make distributed joins scalable for high throughput workloads, it’s best to avoid data movement as much as possible. Some options for doing this are:

·        Make small and rarely updated tables that you regularly join against into reference tables, thus avoiding broadcasting these small tables around.

·        Try to choose shared key columns that are commonly joined upon regularly. This approach will promote using local joins to minimize data movement and promote parallel joins.

·        Try to restrict the number of rows in joins that cause any of the joined tables to reshuffle.

Net; Net:

Most users of SQL databases have a good understanding of the join algorithms in a single process server environment. They understand the trade-offs and uses for nested loop joins, and hash joins. Distributed join algorithms tend not to be understood and require a much different set of trade-offs to account for table data spread amongst a cluster of machines. The data movement trade-offs are key here, so designing them into the user views and the joins they imply is crucial. It was once thought that you could not cost-effectively scale distributed relational databases. Or, in other words, have a scale-out relational database. This is now possible and this type of modern database is table stakes. Modern databases are distributed-native and also combine NoSQL and SQL data access patterns, thus reducing the need for special-purpose datastores.

 

 

 

 

 

Tuesday, April 27, 2021

Is a Management Cockpit for Real?

Despite the past hype on management cockpits and the valiant attempts by BI, Process Dashboards, and Decision Management Tools, there hasn’t been an authentic management cockpit for organizations to see the current conditions to take decisive action in an appropriate time frame. A real management cockpit doesn’t just apply high-quality graphics to selected data so that all can see. A management cockpit allows management to grasp complex situations quickly by integrating all the pertinent data, promoting the collaboration of many individual views necessary to make the required decisions as soon as possible, thus taking timely and proper actions. The answer is "Yes," but let’s dig into some of the details to understand more.


What Does a Real Management Cockpit Do?

 

Besides giving a highly integrated visualization of complex interactions, it enables insight analysis either on-demand or in an automated fashion. Please refer to the decision journey in Figure 1 below. The decision journey can include descriptive analytics to further understand the situation or drill-downs into specific aspects of data for the decision(s). Managers can understand the implications of decisions by leveraging predictive analytics and collaboration with other managers or workers. Some collaborations might involve knowledge bots/agents to understand the impact of potential decisions further. Insights can be further refined with more insight analytics and collaboration.

 

Further contextual analysis could show the interaction and effects to other contexts to avoid suboptimal decisions or interference/influence on intertwined contexts. For known decisions around normal conditions, the amount of analysis can be lower than emergent decisions around new experience situations. This analysis and collaboration will be completed in a more real-time fashion, shifting the organization's focus to responding quickly to meet threats or opportunities while maintaining business outcomes.


                                                 Figure 1 The Decision Journey

  

What Are the Challenges to Deliver a Management Cockpit?

 

There is any number of roadblocks to attaining a management cockpit that works. First and foremost is the data. Often the data sources are not readily consumable as they are in Excel and PowerPoint, and the critical information usually comes from multiple technology and data sources. Much labor is used to condense data into actionable insights. This process is cumbersome, time-consuming, and error-prone. Also, it is challenging to collate feedback, comments, and actions necessary from multiple management stakeholders. Managers also need to collaborate while finding solutions and making recommendations, especially in remote worker scenarios.

 

Also, applying the proper insight analytics in the right sequence can affect the quality and timing. Without good insights, decisions are not optimal. Getting into analysis paralysis and not getting the proper overview for the management to base their decisions on can cause a significant delay in making and executing decisions. In the worse situation making the wrong decision entirely. The critical problem is that Business Managers need a way to gain insight into issues and challenges they face quickly. Those challenges may lie in the external landscape such as products, competition, market changes, sources of raw materials, storms, logistical problems, etc., or an organization's strategy, business processes, risks, etc.


What Does a Real Management Cockpit Consist of?

 

A management Cockpit is a platform that consists of several moving and connected parts. See figure 2. for a visual description of the details. I will summarize the significant components in the text below.



                                     Figure 2 Management Cockpit Platform Logical Architecture

 

In order to deliver the results on the right side of the chart above, the following components need to be smoothly operating:

 

·        View Management:

 

View Management is where organizations can set up roles and associate a pre-architected view for each position. Of course, custom roles can be delivered as needed. Typically, these views indicate the level of detail and types in visualizations proven helpful. Of course, customizations can be made for individuals.                        

 

·        Visualization Management:

 

Visualization management contains the basic visualization that can be leveraged with types of data sources and analytical outputs. These components can be organized into a role view, dashboard view, or any other output form.

 

 

·        Goal Management:

 

Goal management contains the declaration of the desired business outcomes. These represent the stakeholder's take on performance success in terms of trends as well as detailed outcomes. Often tolerances and trigger points can be declared and set in goal management. It is also an umbrella outcome that aggregates critical performance indicators (KPIs) managed in Performance Management. 

 

·        Collaboration Management:

 

Collaboration management manages the individuals and or groups allowed to collaborate securely. CM will enable notifications, locations, time zones, contact numbers, and notes with permissions. Collaborations can be aggregated and published by the author, issue, stakeholder, etc. In some cases, knowledge bots/agents can be legitimate collaborators.

 

·        Action Management:

 

Action management will contain the typical responses to a decision, like changing an explicit rule, adding a new bot, change a process/system, keep a decision audit trail, keep a collaboration pattern for future efforts, kick off a new project, contact partners for changes, etc. 

 

·        Process Management:

 

Process management plays two roles here. One is the operational process that delivers the goals and outcomes daily. The other is to capture specific decision processes for future leverage and reusability. 

 

·        Performance Management:

 

Performance management keeps track of and highlights out-of-tolerance situations for known processes, systems, or integrations. It contains guardrails and rules for success. PM is where detailed KPIs are managed.

 

·        Insight Management:

 

Insight management is where all the insight analytic components are registered and described for future use. IM is also where successful combinations of analytics and AI are saved and documented for future use capitalization.


 

·        Automation Management:

 

Automation management is where the standard reusable low code implementations, microservices, audit procedures, etc., can be cataloged and described.

 

·        Secure Data Mesh

 

The data mesh is where logical data views are dynamically linked to an event, patterns, operational, and archived data as the data/information engine that supports the management cockpit. The DM does care if the data source is on the cloud or not and links across cloud resources.  

 

Net; Net:

 

Yes, the management cockpit now exists, but it is still growing and evolving. It will be a key specialized digital business platform now and in the future. Few vendors come close to the above architecture, but some of my favorite candidates pursuing this architecture are Wizly & Tibco. Feel free to click the links provided above.