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

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

Art for the 3rd Quarter 2021

 Another creative quarter has past and the works keep coming. This time I did a fun one that tried a method that employed painter's tape and layers. Hope you get entertained with these pieces :)  If you want to see the rest of my collection, click here.

Costa Hummingbird

Yikes Stripes 

Tornado 

Crab Attack 

UFO

If you see something here or in my collection that stirs you, send me an email at jim.sinur@gmail.com. 

What are People Reading in 2021?

 We have three-quarters completed in 2021 and people are shifting their topics of interest. Here is a visualization of the activity on my blog. The trends are shifting based on readers' interests. Jumping to the number one issue is Results Oriented Communications in the age of Hybrid Work. While Customer Journeys are still of interest, they slipped to the number two issue. The next highest topic of interest is Management Visibility represented by the Management Cockpit and Real-Time Fast Boards. The next trend revolves around just what is Digital Transformation and where to put Digital Investments. The last in the top five trends revolve around Data Sprawl and Data Fabrics.  If you think there are other high-priority topics, you can put a comment on this post or hit me up on Linkedin. Below is a graphic depicting the activity for the last 12 months.

There is an interesting trend in offshore interests. Northern and Eastern Europe activity is on a marked increase for the year. The Nordic countries tend to be on the leading edge along with Germany. See the activity by a specific country other than the US & Russia below.

Looking at the last 6 months the digital investment topics seem to be on the rise.  

Net; Net:

I'm thankful for my loyal readership and would love any feedback you have on my analysis or ideas for new blogs. The activity is headed towards 800K in 2022 cumulative since mid-2013.  

Data Infrastructure Debt is Hampering Business Returns

There is a great deal of effort and cost associated with keeping technological components up to date and easy to use for more organizational leverage. Some folks estimate that this can reach near 90% of the budget allocated for technology. Every time we add another technological component, the more debt an organization builds. It is excruciating for data infrastructures as data can be used repeatedly to leverage business benefits. It gets in the way of delivering the newly desired outcomes of better customer journeys, more automation for cost savings, and leveraging an organization's resources in new and innovative ways. This post will concentrate on the data infrastructure portion of the technical dept. At the same time, it explores the causes, adverse effects, and how to solve them for data infrastructures and the ever-growing data sprawl.

Data Infrastructure Debt Causes

•        An industry trend implies that modern applications need to be built on top of one or more special-purpose databases, thus adding more technical debt for each database. What makes this particularly difficult when combining and translating data for more use and leverage.
•        Over the past decade, applications have become more data-hungry themselves. As a result, they require particular data dynamics, analytics, and models, implying more cross-use of existing data and new and unique databases.
•         The explosion of free software tempts folks to leverage what software is already there, leading to more unique data infrastructure components. As a result, it creates a specialty database explosion.

Effects of Data Infrastructure Debt

•         Data infrastructure debt is insidious and accumulates fast, and is very difficult to undo. It is a multiplier to the existing debt dragging back initiatives that organizations want.
•         Initiatives that are given the green light are slowed down by the need to aggregate and translate data from many more sources than it would typically take. 
•        Data infrastructure debt keeps the CIO on the sidelines instead of in the critical driver seat for digital efforts in the future. When the CIO is fighting a cost war instead of a results war, digital efforts wane in priority. 
•         We have to hire all the specialty skills to keep the variety of special purpose and legacy databases.
•         The drag causes businesses to set up their own technology efforts. But, unfortunately, they are often naïve to the problems they are creating and throw the debt over the fence to the CIO after making decisions the CIO would not have allowed.

How to Start Solving Data Debt?       

•         It is essential to stop on-boarding new databases even though the software that comes with the new data might be free or low-cost. 
•         Assuming organizations do not want to rip and replace databases immediately but slowly retire many, buying a modern DBMS that works well in the cloud as a No-SQL approach while still supporting SQL commands and databases is ideal.
•         It is not by giving up on innovations and digital transformation efforts by prioritizing these great business outcome efforts overpaying past technical debt. However, it may mean not taking advantage of new features on old databases.

Net; Net:

Smart CIOs are reaching for single modern, scalable relational databases that can support the many needs of applications. It would include mixing real-time data with operational, warehouse, big, and archive data with ease. These databases can operate across cloud providers and on-premises. There is a big trend towards capable databases that can act as a data mesh while reducing the data infrastructure technical debt that promotes unnecessary data sprawl and the difficulties with protecting and leveraging that same data.

Management By Wire is just Around the Corner.

Management by wire has been the wish of many organizations starting back in the 1990s after the first practical application of fly by wire was delivered by Airbus in the late 1980s. The notion of giving management assists in holistically managing their organizations with software assists for single-loop learning situations grew fast. The parallel was evident and desirable, but until recently, it has not been delivered uniformly. This post will investigate the progress towards "management by wire" and when we will see it. 

                               Figure 1 Feedback Driven Learning Loop Approaches

What is Management by Wire?

Management by wire is a strategy in which managers rely on the organization's "information representation" and feedback loops (see Figure 1) generated by software and data working together to inform management of progress towards goals and outcomes while sensing any potential shift in conditions in and around that organization. It borrows from the idea of managing an airplane under various states and conditions while heading to a destination safely. It allows managers to not just "follow their gut feelings and experience" but provide detailed information for status and change for potential actions in a shortened time window. Because organizations are complex like airplanes, many measures are coming at the managers with different velocities and combinations that may be beyond a manager's capability at any one moment in time because of the breadth and depth of their responsibilities for managing goals, initiatives, and outcomes. 

Why are the Stars Aligning for it Now?

Digital technologies are giving us so much more capability than we had in the past. Here are some of the trends that point to attaining management by wire sooner than expected.

Better Visibility

We now have fast boards available to replace slow and lethargic dashboards where speed counts. Fast boards give managers up to the second views into essential issues. We now have management cockpits that can give integrated views of performance, states of crucial processes, and the success of any automation applied for better optimization.

Better Insights

With the help of better-integrated visibility, the manager can be equipped to leverage their experiences and gut feeling while surrounding them with additional analytical & AI assist combinations that I like to call poly-analytics. It can allow the managers to try different options before deciding and acting. It would be true for real-time operational adjustments all the way through to strategy adjustments.

A Better Data Mesh

Because data is getting easier to access with better integrations, dynamic transformations, and not having to worry about location, any manager can get the view they need to manage. While there are still challenges with data quality, the fly-by-wire notion will identify the priority projects for more data cleansing.

When Will Management by Wire Arrive?

Organizations are gaining experience with the building blocks for creating a manage by wire environment. There has been significant progress in managing data better logically as well in various physical locations. Because the management cockpit focuses on the crucial outcomes, data that feeds the cockpit gets better quicker. Integrated visualizations that leverage fast boards are growing by leaps and bounds. I expect organizations will have examples of portions of their business operating using management by wire principles by the end of this year and complete end-to-end business leverage of fly by wire in the next few years. Better get started soon or get left in the dust.

Net; Net: 

Since flexibility and responsiveness now rule the marketplace, today's successful organizations focus on sensing, orienting, deciding, and responding to the immediate need for change. Change necessary to be ready for new customer needs and shifting business environmental needs. Our world is now emergent in nature, so we need new approaches powered by better information technology that assists management in giving a holistic and integrated view of their organizations to start—inevitably leading to better interpretation of feedback information, better decisions, and improved actions. Even if we live in a steady-state world, optimizing organizations' responses will pay back handsomely.

 

Additional Reading:

Frictionless Management

Fast Boards

Management Cockpits

Real-Time Strategy

 

 

 

Results-Oriented Communications (ROC) for Stakeholders

 Stakeholders should be partners in our organizations and should be treated with utmost care. Often they are at an increased disadvantage because they are often not directly involved with the day-to-day operations, initiative progress, and the incremental improvements that bolster confidence and trust. Each stakeholder type or individual wants different results and watches many milestones of progress. Results-Oriented Communications/Collaborations (ROC) can assist these very stakeholders in their roles. We will explore the various kinds of stakeholders and how each of these stakeholders can take advantage of ROC. There are both external and internal stakeholders associated with every organization/company. Three major kinds of stakeholders will be explored here.

Investor Stakeholder: One external type of stakeholder is associated with the investment aspect of an organization. These investors are keenly interested in any communication that would imply an effect on financial results. They spend time sorting through various communications and ratings to determine the health of their investments. ROC can help because stakeholders can be linked to all pertinent communications. Besides these links, there are typically goal/outcome visual summaries available to view at a summary level. Routinely investor stakeholders can look at essential outcomes/goals and see the progress towards them. Since all communications are linked to these outcomes and supporting initiatives, an interested investor can drill down to a very fine granular level to satisfy their interest and or generate inquiries to or collaborate with internal stakeholders or managers. It will support various styles of investors who range from managing by exception to micromanager types. The amount of time saved by looking at initiative summary dashboards is significant. Still, when sniffing down a trail of potentially negative impact on results, the time saved is immense because there is a repository of supporting documents and collaborations about these documents.

Customer Stakeholders: Another crucial external stakeholder interested in your organization is counting on your business to remain viable to become a source for future purchases of goods and services if the relationship generates loyalty. This loyalty depends entirely on your organization's relationship with these customers, which is generally a result of servicing experience and product performance. ROC can help because it can be leveraged to follow the journey of various kinds of customers and manage the initiatives to improve customer experience. Assuming an outside-in perspective, key customers selected to represent the customer experience and routinely check progress on improvements and drill down to the communications revolving around their specific needs and perspectives while sampling the new experiences along the way, thus generating collaboration opportunities. Some collaborations will occur by reviewing the proposed outcomes. Good customer experiences will represent customer goals at an operational level as well. ROC initiatives will typically aim for balanced customer/company goals and measures of success.

Internal Stakeholders: Internal stakeholders are the ones that represent the various roles and skills that an organization employs to operate the company and deliver desired outcomes for all stakeholders. Typically, initiative owners, managers, and skilled employees represent specialized competencies, skills, and knowledge. When tied together across skillsets through tsks and processes, these resources deliver the expected outcomes measured by operational targets and KPIs. These same resources are tasked with improving the customer experiences, processes, and tasks necessary to improve the effectiveness and efficiency of decisions and actions. ROC is essential in managing initiatives to enhance balanced outcomes, end-to-end journeys, end-to-end processes, and tasks. The progress of any endeavor can be tracked from all stakeholders’ perspectives making efforts transparent. In addition, all efforts to improve are linked to the supporting communications for detailed drill-downs to manage progress. ROC will get feedback through collaboration as tasks are planned, executed, and completed.

Net; Net:

Other stakeholders can be brought into the ROC tent, such as suppliers, communities, governments, and potential competitors, but the three most important influences are represented above. The day in the life of a stakeholder will be made better by the capabilities afforded by ROC methods, techniques, and technologies. As the ROC market develops and matures with the technology players, landmark case studies will emerge to emulate better results.

 

Reducing Data Sprawl

 Data sprawl is everywhere and is becoming a bigger problem by the second as we move into a near real-time world. It hits people and organizations, but organizations are on the leading edge to respond to it. Data sprawl refers to the ever-growing amount of data produced, dealt with or aggregated from various new contexts, events, and patterns. It is often mentioned as "big data," but I prefer calling it monster data because of its sizable increase and speed of propagation. It is usually spread over multiple data storage types, networks, and applications that grow as new technologies and data types are introduced. This short blog will cover the significant sources of data sprawl, the considerable effects of the sprawl, and the meaningful ways of dealing with sprawl.  

Primary Sources of Data Sprawl

Operational Applications:  There is often data sprawl built into many organizations because of application data redundancy. It's common to have many data sources for the main subject areas such as customers, products, services, vendors, partners, etc., in base operational data, including their archives that have been building for years or decades. As these systems struggle to keep up with change, they need additional sources of data.

Analytic Potential:  Organizations are constantly collecting data for future analysis to pick up on strategic trends, adjust tactical policies/rules, or look for operational tweaks for performance improvement. Often automation opportunities are hidden in the data generated by signals, events, and patterns occurring in typical contexts and, in some cases, divergent contexts. The wide variety of data types and context crossing requires emergent views and new data sources. Many data warehouses, data lakes, and oceans are being generated for hopefully valuable future analysis. It's complicated by new and emerging data types such as voice, image, and video.

Edge Requirements: As organizations are driven to make decisions earlier at the edge of their organizations, more immediate decisions, plus the data that support them, must be gathered. Also, data must be archived for future audits and management review of edge actions. IoT can complicate an organization's data management strategy because it drives data issues faster than traditional edge issues. Often the outcomes of edge decisions feed the analytic and operational data needs over time.

Significant Effects of Data Sprawl

Complexity:

For many organizations, data sprawl compromises the value of the data. For example, all business and technology professionals have to deal with data from multiple sources in multiple

formats, making operations and analysis difficult. In addition, data can be misinterpreted or, worse yet, corrupted during data leverage and rendering the efforts worthless or just plain wrong.

Security:

 This ever-growing data monster will be challenging to keep tabs on, thereby increasing data breaches and other security risks. In addition, it puts organizations at risk of facing strict penalties of emerging governance efforts such as GDPR, CCPA, or further data protection legislation for non-compliance.

Management/Costs:

Keeping all this data is costly and challenging to manage. The data professionals, owners, and stewards have their hands full, keeping on top of the morphing emerging data sources. All of this while assisting all the various uses of proven data, much less the data with potential whose value is unknown at any point in time.

Significant Solutions to Data Sprawl:

Shifting to the Cloud: The data discovery and classification that would occur in a cloud migration strategy would help organizations get their arms around what they have. At the same time, there would be a need to build and leverage a consolidated cloud repository where users and applications can access and store data files with ease. At the same time, silos of data can be reduced significantly by removing duplicate and irrelevant data. A Security audit can be done at the same time.

Building a Data Mesh/Fabric:  A data fabric is an architecture and a set of data services that provide consistent capabilities across a choice of data sources that are on-premises plus in multiple cloud environments. The fabric simplifies and integrates data management across cloud and on-premises data resources to accelerate long-term digital transformation while serving immediate uses. In addition, meshes/fabrics make building any data view needed easier and quicker. A significant first step in building a data mesh/fabric is acquiring a DBMS to support operational and analytical uses with the same data. It is now a real possibility that many organizations are acting on at this moment.

Building a Meta-Data Catalog: You can’t manage what you can't see or measure. It means that organizations need a data catalog with significant data descriptors for data and information resources (meta-data) that is up to date and customizable. The data discovery and classification required by cloud migration can be leveraged to build the varied catalog needed for effective data management in the digital world.

Net: Net:

We all know that getting ahead of the data sprawl is ideal where policies and procedures are in effect while gathering new data sources. Unfortunately, the reality of the day is that it's often too late for the data sources collected in the past. Organizations need to take steps now as it’s only going to get worse. We all know that new digital technologies are data-hungry, so getting them in shape for consumption is an essential business competency that needs to be grown in most cases. We also know that the hybrid work environment will generate volumes of new unstructured data to manage while change accelerates. To manage and govern our growing data sources, recent efforts around data will have to get top priority. If data is the energy source for digital progress, we have to get going now.