What Were Folks Reading in 2021

 It's always a thrill to see what people are interested in these days of intense business challenges and digital progress. There seems to be a keen interest in automation to drive the benefits train of digital progress. There are plenty of things to read on automation and hyper-automation, so I focused on emerging topics in 2021. There seems to be a general drive towards speedy management of both operations and initiatives that are driven by strategic directions. This drove the number of hits up for topics like Results-Oriented Communications (ROC) that links strategy to detailed activity support those strategies. It also piqued the interest in Real-Time Dashboards and Management Cockpits. There was plenty of debate around the meaning of Digital Transformation and the Digital Business Platform (DBP). An emerging theme seems to revolve around accountability and transparency of outcomes. results, and improvement. Most driven by Customer Journeys and now Employee Journeys in the Hybrid Era as Communications are in Chaos with the speed of business and the distances resources are from each other. All of this is highly dependent on data sources that are lacking accuracy and are growing in unimaginable ways putting a premium on Reducing Data Sprawl and Building a Data Mesh on a Unified Database. There has been a shift in activity in Europe toward the northern countries where innovation often starts. It's been a good year for learning about the early results of digital innovation setting the stage for 2022.  Take a peek at the graphs below to pick out some reading in advance of 2022 and all the best in your digital lives. Click on the Images for a blow-up of the visuals. If you have topic ideas for 2022, comment on this post or contact me through Linkedin.

 

Net; Net:

Organizations have been picking low-hanging fruit off the digital tree and have been combing the very best yielding technologies to get solid benefits. These include process management, RPA, Machine Learning (Easy AI), Data Mining, and Low Code. All of this while trying to be opportunistic with new data and new data types. 

Merry Christmas & Happy Holidays

Our warmest wishes for you and yours in this holiday season. Even if you don't celebrate, we hope you have a restful and peaceful time. It blows my mind that God would send his son into our world to wear skin for 30+ years to experience human pain, trials, and joy so that we might experience a close relationship with him. No matter your belief peace, and love from our family to yours. Sherry and Stella are enjoying the season. We are all fortunate to be healthy and blessed in this time with rampant diseases that are picking off the weak easily and the strong occasionally. 

 

Art for the 4th Quarter 2021

Another year comes to a close, but with more time spent at home, the art keeps coming. The painting below is a depiction of Bell Rock (mountain) in Sedona AZ and its spiritual appeal. It's one of the rare spots on this earth where many can feel a spiritual pull. Native Americans leave their prayers under prayer stones usually in the shape of a circle at the base of the rock. I personally have felt the spiritual pull there as well as at Spirit Lake in Wisconsin, the Garden Tomb in Isreal, Mt Masada near the Dead Sea, and Notre-Dame Basilica in Montreal, Canada. I'm sure we all have felt spiritual pulls in our lives, but this place is one of my favs. The rest of the art consists of some compelling fractals done in a relaxed manner. If you are interested in my creative outlets, please click here. 

Digital Black Hole

 Ice Heart

Fusion

Curly Smoke

Fire Mask

If you have a better name for my pieces or are stirred to purchase one, please contact me at jim.sinur@gmail.com. 

Let’s Get Data Tastic in 2022

It would be easy to focus on the data challenges facing organizations today and respond reactively to them. However, we all see the problem of massive amounts of data coming in faster than we can deal with it. We are all learning how to cope, but I think 2022 is the year organizations make some headway on getting ahead of this problem and start making new opportunities for themselves. I want to enumerate some practical things we can do in this post. One is to work towards building a better data foundation by augmenting and modernizing towards an authentic data fabric/mesh, and another is enabling automated learning to find the data nuggets that are candidates to leverage and finally looking at new ways to gain business advantage by leveraging data.

Build, Augment and Modernize

All organizations can make their data resources better. The opportunity here is immediate and multi-faceted as organizations build toward data fabrics and meshes. The core of a proper data fabric would be a unified database that can handle multiple different data styles of operations, including transactional and analytical with the same data. It will help reduce the number of copies of data necessary for different kinds of operations. A database that can work efficiently and seamlessly with hybrid and multi-cloud situations is a minimum price of admission these days. A database that can mix real-time data with archival data is a must. It requires organizations to move from specialty databases on-prem to a real-time catalog-driven approach that finds and data with different speeds, formats, and data types and leverages that data. All of this is for better decisions and better handling of emergent situations while dealing with limitless speeds/feeds. This technology exists and is growing more capable of supporting an actual data fabric/mesh needs to cope with businesses.

Automated Learning

With the recent strong growth of AI and Data Mining, organizations are tasting some of the early benefits of learning from data leveraging emerging digital technologies. Getting a handle on this automated learning will be table stakes for survival going forward. Even armed with algorithms, people can't keep up with the influx, speed, and variety of new data. Trained machine learning algorithms can understand many data channels such as email, chat, speech, and image sources. This kind of AI, combined with computational statistics, can help find opportunities or threats in the future. Data mining can explore data leveraging unsupervised learning to visualize and provide patterns of interest. Advanced data sifting can employ neural networks to find opportunities in the tidal wave of data.

Gaining Business Advantage

I think organizations have done a great job of using data to gather leads and close sales to increase the odds of growing revenue. Still, there are more opportunities beyond micromanaging resources. While optimizing resources is still a top priority, organizations are encouraged to expand their view to include opportunities for automation, better interact with customers, use digital twin opportunities for better visibility, and leverage data to better manage emerging situations detected in existing or new data sources (voice, image, video, GPS, etc.). It is the more difficult area to move forward in as a leader because there will be some pioneering and risk unless your competition does it first to show the way.

Net; Net:

Organizations must have activity on all three of the above paths to better data utilization; however, the priorities will vary by organizational culture (risk-averse or not). For example, assertive organizations that want to capitalize on data will gain business advantage first. Still, most organizations will grow into an advantage from the bottom up by learning to leverage a new generation of databases inside a morphing data fabric/ mesh. In parallel, most organizations will be growing their capabilities in automated learning opportunities starting with low-hanging fruit.



Additional Reading:

Data-Intensive Applications

Reducing Data Sprawl

Data Infrastructure Debt

Unified Databases

Real-Time Data

Linking Strategy to Operations

There is a constant balancing act between strategy and operations, and it gets even more challenging during periods of change or near impossible during significant chaotic events. COVID has brought this issue front and center with the increased speed of decisions and intelligent actions on several fronts. We aren’t through this threat scenario yet, but we can expect more threat and opportunity situations to emerge globally and locally. It puts a premium on superior insights, optimal decisions, and an excellent management overview. It means that the links between strategy and operations need to be optimal while allowing for emergent change. The days of the steady course are numbered at worst and only temporal at best. It means that an "Insight First" approach based on "Contextual Insights" to stakeholders is needed.

What Are the Links Between Operations Strategy and Business Strategy?

The business strategy is the overall business vision looking further ahead and anticipating the direction and business wants over a long period. The operations strategy is to provide a plan for the operational functions to make the best use of an organization's resources. Therefore, operations strategy must be aligned with its business strategy to enable the company to achieve its long-term plan. In addition, it means that its operations must be agile enough to support strategy changes while feeding monitoring information that might indicate trends that imply a change to the current strategy.

Example Checklist for Methods Linking Strategy to Operations

The recipe for linking strategy to operations has some essential ingredients that much be put together to support optimal results in several changing contexts. First, it means that connecting “what to how” is critical to be kept optimal and ready for change. Ideally, each company should have an integrated method and supporting toolset. The methodology must link the what and the how in a well-woven way across organizational stovepipes and business boundaries.

WHAT FACTORS:

Vision and Mission

Strategy / Scenarios

Critical Success Factors

Risks / Patterns / Events


HOW FACTORS:

Policy / Rules / Boundaries

Objectives / Goals

Projects / Initiatives / Milestones

Processes / Orchestrations

Organization / Partners


Example Checklist of Functionality in Tools Linking Strategy to Operations

Supporting a comprehensive methodology that links strategy and operations that delivers results should be a tool suite, generally a digital business platform that focuses on results, change opportunity, and change management. This platform/tools suite should link/integrate many functions and features that deliver business outcomes. This highly integrated platform has to work seamlessly with the method across functional silos and help focus participants on results. A management cockpit often visualizes the results that build a base for automated management functionality built on top of this integrated platform.

Strategy Planning Features

Strategy Performance Reporting

Balanced Scorecards

Operational Performance

Contextual Analytics

BI Dashboards / Fast boards

Collaboration Features Organized by Results

Real-Time Chat That is Visible to All

Audit Trails by Data Point

Action Plans Linked to Stakeholders

Integrated Risk Management

Integrated Process Management

Integrated Low Code Features

Integrated Process Mining

Net; Net:


There is a delicate balance between operations and strategy. At the same time, the obvious and traditional approach is to lay a plan and implement it optionally at the operational level. It works well in periods of stability, and the active monitoring points to more optimization. However, in a more emergent world with change, operations can point out emergent signals and patterns that may suggest a need for change. A solid methodology linked with an integrated platform will allow for both proactive and reactive strategy changes and monitor the effect of major and minor changes. Organizations should be pursuing this balance with the help of an integrated method supported by a corporate performance digital platform that embraces the management cockpit.

 
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

AutoPilot Management

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

(414) 326-9781

Others Who Have Passed

Dad

Andy

Beth

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