Monday, November 22, 2021

Mom is Back with Her True Loves

 Carol E Sinur went to be with her beloved Lord, husband, and family on November 17, 2021, at the age of 94. While mom was legally blind, had hearing troubles, and short-term memory issues, she was still making friends and singing hymns to the workers at the nursing home. Mom was beautiful inside and out and lived out her priorities with great enthusiasm. She loved her Lord and sang about him, so I expect her in the heavenly choir. She loved my dad who passed on in May 2017 and she spent seven months here in Phoenix trying a new location. She wanted to finish out her time in the beloved home on the lake in northern Wisconsin because dad loved it. She wanted independence overall, so we surrounded her with the best visiting nurse in Julie and handyman Dean. She loved her friends at her local church and lived to go every Sunday she could. After battling COVID once, Pneumonia twice, and early sepsis three times, her body finally gave out. She was such a patient and loving person. Many will remember her humble life and the lessons she taught with her loving actions. Mom's hobbies were gardening, bird watching, painting by number, and completing puzzles until her sight left her. Here are some pictures of mom for your viewing pleasure.  

Mom in Her Early Years



Mom Loved Her Man



Mom Loved Her Family









Mom Loved Animals and the North Woods




Mom Loved Going to Church to Worship


Mom Was and Still is an Angel


Memorial Service 

Date December 3rd, 2021 Time 12 Noon  

Krause Funeral Home

9000 W. Capitol Dr

Milwaukee, WI 53222


Others Who Have Passed

Dad

Andy

Beth



Tuesday, November 16, 2021

The Rise of the Unified Database

We are living in the days of ever-expanding data sprawl caused by many hard to control factors. Data sizes are monstrous, the speed of data ingestion is ever increasing, the demand for better latencies is insatiable, and the complexity of the data is increasing. All of this while the need for custom views accessing the same information is exploding.

There are almost too many options that require specialized technical skills to create complex integrations that produce performance bottlenecks when working. It becomes even more nightmarish when testing and troubleshooting. Is the problem hopeless for organizations trying to manage and utilize their data? The answer lies in unified data and unified databases. Let's dig a bit into unified databases and how they relate to the holy grail of an effective data mesh.



What is Unified Data/Database?


A unified data model is a crucial piece to simpler data architecture and supports the data mesh. A unified data model offers an opportunity for organizations to analyze and operate on data from multiple sources in the context of shared views supporting shared business initiatives. The unified data model bridges different ecosystems, allowing organizations to contextualize data sources across various services. The task is statically or dynamically mapping each dataset to a more singular schema or view.

The Benefits of Unified Data/Databases

The benefit of data unification is that it provides a more holistic and accurate view of your many data sources while operating on them in different modes with different performance characteristics. The unified database can simplify the multiple tool and database environment and create a common denominator for data use without worrying about the scale. Unified databases can make data more practical and actionable while helping the data accuracy with the least energy by users and the data architecture keepers.

Downsides of Todays Data Architectures

Today organizations most often run-on specialty databases that have to be synchronized periodically. With the demand for more real-time management, this becomes a copy carousel that gets in the way. It leads to high-latency data makes always-on systems a nightmare. Because different data sources on different data architectures will require redundant transformation logic during the copying and transformation processes, often it isn't easy to guarantee consistency. It also becomes a challenge for data security and privacy programs. What's worse is that some of these copy/transformation processes fail, creating cascading delays and errors. Because our databases do specialized work enumerated below, the copying process is a never-ending nightmare. A unified database that can nearly do all the work of these specialized databases makes for a simpler world.

Generalized Databases

Typically generalized databases are for analytical purposes and rarely scale for transactional purposes. They are often easy to use and handle lower scales of data. Unfortunately, the complex uses and monster data tend to bog these analytically focused generalized databases.

Transactional Databases


Typically, transactional databases are focused on high-end operations where volume and efficiency are often desired. They can stand up to volumes generated by nano-second demands and work well in long logical work units for tasks and processes. However, they aren't for end users who often desire analytical feats leveraging siloed operational data where lateral views are not easily supported.

Analytical Databases

These databases are perfect for high-end analytical work where many sources are mainly focused on reading data but not necessarily creation, updates, or cascading deletes. However, they aren't as easy to use as generalized databases, so they are their own breed

Net; Net:

The unified database does a great job of spanning all these specialized databases. It often ends up as a hub through which many divergent views are connected to many divergent data sources of various types. It simplifies work and gets organizations off the copy carousel. The Unified database is an obvious choice as an infrastructural centerpiece for an organization's data mesh. It makes the shift from inflexible centralized data infrastructures to a data mesh that supports distributed “data-as-a-product” This is particularly important with a distributed data mesh that spans the clouds and on-prem.



Additional Reading:

Get Ready for the Big Shift to the Data Mesh

Convergent Data is Here to Stay

Real-Time Data Mixes Well with Archival Data Now

Leveraging Hybrid Cloud & Multi-Cloud

Speed, Scale & Agility Delivered with Distributed Joins

What’s Driving Data-Intensive Applications?

Reducing Data Sprawl

Wednesday, November 10, 2021

Attaining Autopilot Management

There are areas where automation is scary but necessary. It is true of management and driving vehicles. Both are on the way to more automation. We are seeing a revolution in driverless cars that is taking the driving tasks from the drivers and slowly passing them on to forms of automation combined with AI that learns effectively in real-time. This automation journey is bearing fruit now with driver-less taxis and driver-less freight hauling. The maturity of automated freight movement is taking off rapidly right now. Automation may just assist us in the nick of time with messed-up supply chains and driver shortages. There is an emerging parallel for managing organizations that is early and also taking off. There is a maturity to this autopilot management journey that I will try to convey here. Much of this revolves around making better decisions and taking appropriate actions speedily. This post will investigate the progress towards "Autopilot Management" and when we might see it. This post is building off some earlier posts on management by wire and the management cockpit.




What is Autopilot Management?

Autopilot management automates the management processes that observe, decide, act and watch for the expected or unexpected outcomes. It will not happen overnight and will evolve from simple automation to highly informed and interactive automatons. The quest for 100% automation might be a pipe dream. Still, great leaps of progress can be expected over the next few decades making business agility a powerful ally in the competition game. It is now possible because of the leaps in digital technologies, including AI that learns and postulates alternative courses at the strategic level, alternative rules at the tactical level, and focused actions at the operational level. The definition will have levels of maturity that I have described below.

Level 1 Notify:

Notification is the lowest level of automation. Events, patterns, and actions are aggregated and visualized, focused on known decision points. The management cockpit plays a key role here for integrated visualization. Automation will notify the manager(s) of abnormal or out-of-bounds conditions or situations. It is purely advisory and will grow to identify emergent events and patterns to sense potential emergent conditions related to threats and opportunities.

Level 2 Suggest:

Moving up the autopilot maturity curve, the management cockpit will provide/suggest alternative decisions and actions. In some cases, the management cockpit will suggest further analysis and collaboration to deal with expected or unexpected situations. It may be as simple as continuous improvement suggestions at the operational level or an adjustment of rules and boundaries for customer experiences or processes at the tactical level. However, it could draw attention to a potential shift in strategic direction or identify new opportunities and threats.

Level 3 Decide & Advise:

Kicking the intelligence factor up a notch to decide and offer alternative actions for automated action before action is taken. The best decision that automation is capable of is taken, but the manager still determines the proper course of action. In this case, the manager would still have the finals say to the measures necessary in the situation detected or emerging.

Level 4 Decide, Act & Offer Overrides:

Here the level of freedom the automation is given has increased. It decides, but it will take action if an override is not selected within a reasonable time frame. A manager had better have a solid reason not to take the intelligence's advice in the automation tested and established. It might really be necessary for an emergent condition that might need further analysis, particularly at the strategic level.

Level 5 Full Autopilot Management:

It is where the automation would not require a manager's attention to the dynamic management conditions. Instead, the automation would be free to do anything an experienced manager could do within boundaries and constraints preestablished. Finally, it is where the law, ethics, and corporate charter play a significant role.

Net; Net:

Since flexibility and responsiveness now rule the marketplace for today's successful organizations, autopilot management has and will continue to become an increasing focus for organizations. Remember that any organization might only apply total autopilot to selected problem domains and give less freedom to other problem domains. The amount of change and emergence we have seen and will continue to see is going to accelerate. Just look at the impact on our interconnected world that COVID has had so far. While we are early in the automation of management, we can expect much more as managers become more comfortable with the automation of their internal tasks for managing their domain. Also, managers will look to automate interactions with external environments and the traditional other internal domains.

Please Help with A Survey On Management Cockpits by Clicking Here You will receive a summary of the results if you leave an email address in February. Please be patient with the initial screen and use your down arrow on the drop-down selections.

Additional Reading:

Frictionless Management

Real-Time Fast Boards

Management Cockpits

Real-Time Strategy

Management by Wire