Again, AI and Customer Journey topics dominated the activity. However, Results-oriented communications /collaboration is rising fast as a topic of interest. Surprisingly, Processes made the leaderboard for the first time in a while.
Blog hits in the 2nd Quarter of 2024
Offshore Activity in the 2nd Quarter 2024
Next year will be a year of focused revenue growth, streamlined time to results along the traditional cost optimizations. Organizations have learned where digital has delivered well consequently, they will be increasing their bets where results are more likely to carry the day. The emphasis on steady-state only results based on consistent KPIs will change to include more calculated innovations and emergent measures. Organizational visibility and insights will rule this year with stronger links measured from strategic direction to projects and operational results. Customer journeys, experiences, and feedback will be sharper, faster, and more frequent. Because of the skills scarcity and jobs becoming more complex base skills will have to be automated to free workers to take on more challenging tasks. Organizations have to be prepared for emergent scenarios as the world is much more dynamic and shifting. Innovation will be necessary at all levels even when pursuing focused goals and results because of change. Innovation will also be accelerated because of new easy-to-use digital capabilities.
Tight Integration of Direction with Results
Manging the synchronization between direction with the actual results is a constant challenge and both are changing dynamically. Not only must the direction be clear and repeated, but all must also understand how the direction plays out in operational results. The integration between actual results must be checked frequently and carried out with efficient communications. It means that the delegating of the monitoring and the adjustments necessary from operations must be in tune with the direction. It will require more than just collaborating managers with workers through a mish-mash of today's communications channels. The focus will require results-oriented communications that keep the stakeholder's results in the eyes and ears of everyone contributing. It is not only necessary for the business results, but for efforts and projects trying to improve the status quo. All of these are in constant flux and need constant attention. The emphasis for 2022 and beyond will be stakeholder-driven outcomes.
Competitive Customer Experiences
Organizations will have to change from tepid surveys that are after the fact to real customer journeys and delightful experiences that may not be synchronized with the established processes and procedures that are carried out by customer service personnel that act as buffers from the real experience and the rocky support systems that are often numerous and not designed for customers. Even in self-service situations, most organizations are just delegating the data entry to their customers to make the current and rigid processes or systems work. This perpetuates organizational silos and puts the burden of service on the customer. Keeping in mind that a real customer journey starts before it reaches your organization's door steps and often doesn't complete there is the first step in creating a customer experience that beats the competition. At your door step sentiment analysis using voice and video will gain in popularity.
Increased Skill Augmentation
It is getting more difficult to find workers that will perform the basic skills of a job. There is a definite shortfall in the number of workers willing to do some of the basic tasks. The movement has started to replace these basic skills with robots and software bots. This is an unstoppable trend because there is no satisfaction for the workers to do this kind of work. The workers that are already doing the basics become opportunities to take on more challenging work assignments. This will likely require additional training or augmentation of base skill sets with forms of smart automation and knowledge extension with the help of AI, data mining, and other forms of augmenting technologies. workers will be given insights and guidance in the midst of their tasks. This may be audio or video assistance in synch with the work included in pre or post-activity pieces of training.
Practiced Scenarios & Agility
We all know that the world was caught "flat-footed" by COVID. We saw supply chains designed for optimal conditions and no warehouses fall of their face. It was because nothing of that magnitude was ever anticipated and there was a bet that folks could manage to redirect and adjust if anything substantial occurred. The days of laying back and hoping for the best are gone. COVID may have been an outlier for generations past, but there are more coming with climate change, geopolitical events, and natural disasters. Many organizations will start the discipline of scenario planning and practice for such emerging scenarios. This is more than a fluke "black swan" event. We have been lucky in the past and our luck will run out.
Continued Business Innovation
Innovation is not only about dreaming about new ways to please customers and perform efficient business events/transactions. Innovation is applying creative approaches to solving real problems that get in the way of results. Digital technologies are no longer the domain of the technical, but they are tools in the hands of business professionals. Learning to leverage processes and data closer to business problems is now becoming a reality. As more technology emerges and business types pick it up, there is no telling how it will deliver results.
Net; Net:
Organizations now have experience in successful in deploying digital solutions or have at least seen other organizations show success. It is my bet that organizations will be focused on where success has been demonstrated and they will leverage proven success heavily in the next few years. The lessons learned from threatening business scenarios will be leveraged by savvy organizations to learn to be better prepared for emergent situations. Digital will help cope with the challenges and opportunities as it is proving itself helpful on a number of fronts now.
The hybrid work model is now ubiquitous and has significant momentum. It is a model that blends new work styles that enables employees to work from different locations dynamically: home, office, or on the go. It encourages autonomy and flexibility but keeping it on track puts a significant burden on effective and results-oriented communications. Hybrid models also leverage many traditional and new communication channels. Linking these channels and all their communications is challenging and necessary to deliver the demanded higher performance with great flexibility. Hybrid work models and results-oriented communications go hand in hand. Still, few organizations have a handle on linking results to communications that are the lifeblood of the hybrid work model. Click here for more on results-oriented communications.
Benefits of the Hybrid Work Model:
The benefits of the hybrid work model for the COVID era were plentiful because safety was the number one issue for all involved. Analyzing the benefits now we are down the road a bit yields a clear picture. There are reduced overhead costs exemplified by lowered rent, utilities, office supplies, and such. There is often boosted productivity as office chit-chat is diminished and people are no longer interrupted in a face-to-face fashion. There is also a great reduction in micro-management, which is the bane of real productivity. The commuting is often significantly reduced, and employee well-being is increased because they control their work schedule. Team building occurs quite naturally enabled by better collaboration tools. The benefits are substantial for both the organization and employees; however, it is not all hearts and flowers. Some challenges start to emerge.
Challenges of the Hybrid Work Model:
Both the organization and the employees must up their planning
game. Meeting types have to be matched with the kind of supports that is
needed. Each participant will have to be considered for remote vs. live. Better
resource scheduling by skill type will have to be considered. The level of
dress and formality will need to be considered as well. After the meetings, the
archiving of issues and solutions linked to actions will need to be recorded
and shared will also need a plan. Rethinking the workplace is also necessary.
What kind of work can be done remotely, and what should be done in the office
is an issue that third-party office spaces can temper? Management visibility becomes a balancing act.
It would be easy to error in the direction of invasive surveillance, so
watching deliverables is a better practice. On the other hand, remote workers
can easily fall into the trap of thinking they are second-class workers, and
fair pay emerges as some locations require more pay than cheaper locations.
Net; Net:
Hybrid Work Models need speedy and innovative solutions that
drive organizations towards results-oriented communication infrastructures and
tools. Deliverables become the focus of optimizing hybrid work results. These
deliverables need to be tied to stakeholder outcomes, requiring technologies
that can archive team results and link them to desired outcomes in a shared way.
There will be a targeted application of new kinds of collaboration tools that
don't just support low-level random communications but link everything to
results and outcomes. Hybrid models are here to stay even if COVID dissipates,
and we no longer have to deal with threatening scenarios. Hybrid models also
optimize on opportunities while linking strategy to operations as they both
evolve and optimize.
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.
It makes no difference if an organization is attempting new opportunities or dealing with emerging threats, stakeholder collaboration, with significant visibility, will deliver improved competitive positions and more sustainable businesses. Even if the change efforts are more focused on continuous process improvement, better customer experiences, or incremental digital transformation, the importance of stakeholder collaboration is significant. Stakeholders can be at the executive level or just innovative business managers in search of significant results as long as they don't sub-optimize on small organizational units to the detraction of others. Stakeholder collaboration provides great insurance for overall results. What are the typical stakeholders for integrated and large impact change in organizational customer journeys and processes? Let's explore the following collaborating roles for business outcomes.
Core Stakeholders
Stakeholders are the key drivers for management and change that measure true business outcomes throughout the defined scope of efforts to innovate or improve. They typically are innovative and hard-driving groups or individuals that want to improve outcomes. For large scoped high impact efforts typically customers, social communities, partners. investors and vendors are engaged and involved. For more localized efforts, most of the stakeholders are internal but looking for cross-organizational outcomes. Stakeholders often charge optimization agents in seasons of low change to deliver better incremental results and change agents for sure in seasons of high change.
Optimization Agents
Journey and process managers are key collaborators for the core stakeholders and help manage the process operators to ever-improving results over time. This is what I call "Small Change" that constantly optimizes existing journeys or processes to make them better overall. Typically the changes are around tuning and optimizing the goals, guardrails, and performance indicators in existing processes and can be done without large and cascading impacts. Core stakeholders allow a greater level of freedom for these kinds of improvements, but visibility to the improvements is essential.
Change Agent:
"Big Change" is usually lead by change agents that seek bigger scopes and larger impacts, so the intensity and frequency of collaboration rise with the stakes. There is usually a significant communication plan to let all the participants know the progress even if the large change is bitten off piece by piece in an agile change and development environment. The change agent will be constantly collaborating with the Core Stakeholders as the risk-reward equation demands it. In fact "Fail Fast" proto-types, simulation efforts, and model work environments might have to be established to prove viability. The change agent plays a key role by working with optimization agents that represent present practices and the core stakeholders that want significant change or even a whole new model of work.
What are the key best practices to enable and even enhance these key collaborations? Let's explore three that I have seen work in multiple organizations.
Incented Collaboration is a Best Practice
Getting the change core stakeholders wants in the best possible way requires a change in the reward system. Individuals should be incented to take reasonable risks so the fail-fast approach gets some momentum. Most people dislike change so the chance for runaway fail-fast efforts is unlikely. It is also important to report the learnings of failed efforts, so no punishment for failures is also a key incentive.
Holistic Visibility is a Best Practice
It is important to have visibility into results, collaborations, and learnings in near real-time so that key collaborators and core stakeholders are aware of the state of progress, so they have a chance to inquire and potentially provide a forward block for the change experimenters and implementers. This visibility is likely to kick off more collaborations that are in the public eye, so good ideas will bubble to the top.
Capturing Innovation Ideas is Best Practice
Often good ideas are tossed onto the refuse heap because they don't immediately assist the efforts in progress. Innovative organizations will likely want to see, hear and store innovation ideas for the future and further core stakeholder collaborations. Instead of a distraction, these ideas are a coveted source of future gems of progress. Some will make the grade and some will not, but losing them in the hustle to completion is not acceptable.
Net; Net:
The modern and sustainable business must employ collaboration. The savviest of the organizations will collect, inspect and evaluate these collaborations for contributions to desired outcomes. These organizations will employ the very best methods and tools to focus these collaborations into progress now or in the future. An example of a collaboration platform that not only gets this approach but promotes it is Parallel. Check out Working in parallel
Today we find that collaboration is rapidly flourishing throughout many organizations large, medium, or small. We see collaboration as a major activity in emails, forums, chat, project management, and generalized chat tools. Where is all this collaboration and communication going? Is it to chaos or results? Let's change these questions to be focused on forward momentum and appreciating collaborations and coordinated communications through goal-driven approaches. By making goals and guardrails crucial for guiding and empowering teams, organizations will experience a significant appreciation of value in their collaborations and communications.
Benefits of Goal-Driven Collaborations/Communications
There are a set of significant benefits that emerge from guided collaboration and communication. Besides cutting out the noise from collaborations that do not contribute to desired outcomes, there are real positive results from communication activity that is focused on the prize and keeps folks from slipping into known ditches of unproductive activity. Goals and guardrails along with group learning are key to forward momentum and results.
Continuous Improvement
Organizations can no longer stand pat on decisions and actions of the past. This means that they must be aimed at getting better all the time. This often happens in guiding decisions and actions through smart processes that can adapt to rule changes senses by a team during both the building and maintenance of both decision and action-focused processes.
Learning Continuously
In order to stay on top of markets, products, and changing business conditions, organizations through teams or management need to be learning and sharing at all times. It could be as simple as recognizing an emerging signal, event, or pattern in the business that one team member sees and shares. It could be a manager or a team member recognizing an opportunity to change tactics to optimize outcomes within a current business scenario. It also could be a recognition of a need to change strategy to match emerging conditions.
Empowered Teams
Giving teams goals to achieve outcomes allows them to operate with the mind of management without management overlooking every detail. Giving teams guardrails also keeps them from relearning lessons that came out of negative situations from the past or new and emerging dangers. Freedom levels can be given to teams to allow them to flourish and keep mistakes to a minimum.
Innovation & Creativity
Great ideas are like fleeting moments. They are hard to capture in context and often are not captured. Collaboration can allow innovations to be captured and even shared for additional growth and testing. Groupthink on emergent change and ideas is a helpful starting point for the learning organization.
Stakeholder Influence
Often results are different for different stakeholders inside or outside the organization. Implementors need the voice of stakeholders in their everyday activities to strike the balance between seemingly conflicting outcomes. Early detection of deviance from stakeholder goals is a major benefit of goal-driven collaborations.
Setting the Necessary Goals & Guardrails
Goals need to be established and linked to important outcomes. Since much of continuous improvement is tied to processes, linking the collaborations/communications to processes is necessary. The problem with process improvement is that it is often managed by a core set of process geeks instead of spreading out innovation and change to everyone. This democratizes improvement efforts to a larger pool of innovation sources. Stakeholders' desires and goals are often lost in the translation of building and maintaining processes. This is why constant stakeholder representation can be maintained by linking collaborations to individual or groups of stakeholders. Guardrails can also set up in advance to avoid major blunders, but new learnings can be baked into new goals and guardrails.
Managing the Fluidity of Change
We are living in a world of constant change and the only thing we know is the pace of change is accelerating. This puts a premium on speed and agility within the context of a fail-fast world. This means that the goals and guard rails have to be adjusted on a frequent basis and the ripple effect has to be communicated fast to the implementing and operational teams along with their management. This presents a dynamic environment where teams have to be enabled with focused information quickly to optimize outcomes.
Net; Net:
We are in the age of pressure for continuous improvement that can be adversely affected by change both internal and external to organizations. The coping mechanisms for learning organizations that want to thrive and capitalize on improvement and change are going to have to change. While there are communication and collaboration tools aplenty, few are linked to the goals, processes, and stakeholders in and around organizations. There is a new breed of methods, techniques, and tools emerging. Here is a link to one of these emerging vendors There will be more for sure. Salesforce bought Slack to head in this direction.
The new year brings new hope for technology to accelerate progress for businesses in this challenging and changing world. I have identified the tech trends that will give organizations not only an opportunity for digital advancement but traction for better business and customer outcomes. I would suggest readers take a glimpse at my 2021 Top Business Trends for the business context that these tech trends play out in for 2021.
Hyperautomation Hits High Gear
With the pressure to do more with less in 2021, hyperautomation gets the early nod in 2021. The taste that RPA gave businesses for savings gets a big boost as other technologies play in the automation game too. This puts a premium on reality-based automation that gets an assist from data/process mining that desperately needs AI to suggest areas of opportunity through the ability to learn quickly from emergent data patterns. In addition, automation will start to collaborate as a full partner in completing work with workers and customers. Hyperautomation will be combining smarter capabilities with users and developers to push low code to the front lines to help with the "innovation democracy".
AI in Everything
Smarter and faster organizations will reap the benefits over others. This means that AI and its cousin analytics will play a key role as they become inline and real-time in assisting businesses to attain outcomes. This means an explosion of smart to every corner of the organization. The IQ may not be the highest, but AI is so much faster than the human eye can see or that the human mind can calculate. Over time, emergent complexity or complications will increase the IQ of AI needed to kick off a race to results through smarter delivery of balanced outcomes.
Sentiment Analysis Drives the Empathy Focused Organization
The need for organizations to be plugged into the feeling of their constituencies, especially customers will create a huge vacuum for voice-driven sentiment analysis. This will be interaction driven in real-time to create better interactions and relationships. These very listening post capabilities will also allow organizations to catch trends such as mentions of the completion as well as alternative products or services. At a very minimum, the present products or services can evolve incrementally through this analysis process.
Composable Implementations
The dream of the composable organization from technical infrastructure to applications to processes to the knowledge bases will start to take off in 2021. The driver for this trend is not wanting to recreate anything that already exists. By using what is there in different combinations and for different goals has always been an "End Game" target for many architectural approaches. This may start at microservices and common APIs, but it will reach business components and templates. The constraining issue has always been discoverability for the user who doesn't know of the "reusable bit". Through better data structures and AI discoverability, this hurdle will start to lower.
Computer Vision: A New Source of Learning
Computer vision has always had a place in manufacturing for inspection of parts for flaws, but now things are going to expand past static images to images in motion to look for success and failure patterns. Computer vision will be used to learn how the most productive workers deliver in an assembly or service environment to train others in better practices initially which could evolve to machine/bot enhancement. Putting vision at the edge will allow for autonomous adjustments in any movement focused applications from security to dynamic assembly. This will allow for more deep learning opportunities for AI as well.
Monster Data Explodes
Monster data represents data that is overwhelmingly large, unduly complex, can’t be trusted for accuracy. Typically it is composed of multiple kinds of data including structured, unstructured text, voice, image, or video. Some monster data may be unknown or emergent, making it scary to deal with for most individuals, technologies, or organizations. The growth of all sources of data is going to create volume issues along with the IoT awakening. The hunger for human behavioral data of all kinds will drive more data complications and complexity. The amount of inaccurate data will become mind-boggling, but the tolerance for errors will become looser for some applications.
The Internet of Behaviors Emerges
Watching how people behave has been the source of many studies for sales and marketing opportunities. Not only will this trend continue, but it will also accelerate and complicate because of new channels and new sources of behavior mining from the IoT with complex and complicated data sources. Additionally, behaviors will be mined and aggregated for better customer relationships to create customers for life. On the dark side, behaviors will also be mined for potential security threats and other bad behaviors.
Spanning the Data Divide with the Data Mesh
Data management needs a boost to cope with all the new sources and uses, so the data mesh will emerge this year and gain momentum. A data mesh approach reorients data management efforts to align consumers of “data products”. Instead of thinking about many data pipelines and datastore types, a data mesh provides a unified view and architecture for organizational outcomes supported by applications, processes, or dashboards. It combines data inside the cloud with data outside the cloud. A data mesh hides the complexity and variety of data from the end-user. All of this while the speed of business approaches real-time.
Distributed Cloud Advances the Edge
Edge computing allows for the handling of certain work or events at the edge. This means that data and applications work together at the edge without the interference of central control under allowed freedom levels guided by goals and guardrails. While the transparency of behavior will likely reach central observation, decisions or actions can take place close to the point of origin. The distributed cloud will enable this kind of computing behavior for both expected work and unexpected events or patterns.
Putting Bets on New Maturing Advanced Technologies
Because of competitive pressures and emerging business scenarios, organizations will be more aggressive in placing bets on technologies that are emergent in nature and not fully baked for their respective industries. These technologies that have this kind of allure are 5G, Blockchain, IoT, Quantum, and Autonomous Smart Bots. Organizations can't afford to do all of these at once, so the leaders of savvy organizations will pick one or two of these to extend their competitive advantage.
Net; Net:
2021 will be a watershed year. With a tech-savvy incoming leadership in the west and the need for an organization to recover from 2020, I expect these trends to take off along with the advancement of automation to glean savings to drive these efforts. Simultaneously there will a drive for inclusive or better customer journeys driven by a new desire for organizational empathy needed to under-served customers or markets.
While we all know that real-time applications and analytics are constrained to respond in the order of microseconds, real-time systems have been difficult to justify and attain until recently. There is a whole new class of real-time analytics that are now open to more organizations and applications with the advent of “The Database of Now." This blog investigates some of the new and emerging uses and is meant to give the reader some real-world examples. Hopefully, this list of successful uses will inspire others to follow suit, mainly where real-time dashboards and analytics deliver significant benefits.
Real-Time was for
Special Applications
Real-time computing started with operating systems and then
only a handful of applications because of specialized software costs. The first
implementations revolved around real-time networks and market-driven
applications where results demanded no significant delays or instant results.
Real-time was an excellent fit for physical systems that need instant responses
like fly-by-wire or ABS brakes or single purpose-focused applications. These
use cases required extreme correctness, deep concurrency, and durable stability
while being distributed and sometimes autonomous. Real-time was a limited set
of applications and systems until recently.
What Has Changed?
Business drivers require more speed. It used to be good
enough for businesses to have dashboards that were relatively up to date. Now
that kind of speed is not acceptable even if applications aren't hooked up to
devices on the internet's edge (IoT).
Almost all the leaders of business-focused software organizations
believe that speed is the new currency of business. We are in an era of extreme
competition and dynamic adaptability. Those organizations that can handle
emerging trends by sensing them, making rapid decisions, and implementing fast
are the ones that will emerge as the winners as long as they consider the voice
of the customer and other constituents. Savvy organizations will switch from
reactive to proactive by planning alternatives, practicing them, and put in
listening posts for an emergent change.
Technology enablers have been emerging to meet the need at a
lower cost and broader use cases. It started with complex events processing's
ability to sense signals, events, and patterns of interest and even respond in
limited situations. And now, fast forward to today,
and we see the accelerating trend of adapting to real-time applications with
the mainstream use of streaming data. Ventana research states that more than
50% of enterprises will leverage real-time streaming data in their enterprise
next year. It reaches full bloom now with databases that can handle
various kinds of complex monster data in the cloud-managed as a single logical
store rather than multiple special-purpose datastores, greatly simplifying and
accelerating data delivery. Organizations simplify the complexity that revolves
around data location, meaning, and transformation connected to the smart IoT,
often embedded in Industry 4.0 solutions facilitated thus increased speed.
Sample List of
Successful New Real-Time Use Cases
They are listed in
no particular order with common uses highlighted in bold text. All industries
will have emergent situations that will cry out for real-time assists.
Net; Net:
While there are many more industry examples not listed here,
the evidence is clear that it is time to rethink real-time utilization. The
Database of Now has lowered the hurdles keeping organizations from leveraging
real-time implementations. This is especially true of dashboards, analytics,
and other transparency rich situations. If you need to know about the status of
work or outcomes on the spot and right now, you should be considering the
real-time use cases that the Database of Now enables. Real-time is for
everybody now, so start planning and preparing for new competitive
implementations.
Additional Reading:
Corporate Performance in Real-Time
Organizations are now challenged in new ways; therefore, they must budget very carefully, as we advance. There will be a tug of war between accelerating digital and dealing with budget reductions for IT investment. Gartner, affectionately known as "The Big G" in my circles, has predicted IT budget reductions. At the same time, businesses are being pushed to accelerate digital transformation. Savvy organizations will save with technology to invest in technology to break through this set of apparent conflicting goals. Organizations will be careful in deciding what to invest in to survive, thrive, and capitalize on these dynamic and challenging times. I will try to lay out the five most important technologies to invest in to keep these conflicting goals in balance.
Continuous
Intelligent Automation & Cost Optimization
Automation has come on strong through the use of RPA, Workflow/iBPMS, and Low Code solutions as of late. Now I
see an extension of the accelerated use of both guided by both Process Mining and AI. Continuously, process mining offers extreme visibility for opportunities
to handle outliers or optimize on process/case yielding time and labor savings.
Machine and Deep Learning will also play a guiding role in finding more
optimization opportunities over and above what the human eye can detect across
various mining visualizations. The pressure for quick improvements with fast
feedback cycles will push more detection of options to intelligent software or
machines as responsible AI continues to develop. The savings from these
profitable efforts can be applied to future digitization efforts. These efforts
can be multiplied by using Software as a Service (SaaS) in some instances.
Human Augmentation
& Skills Expansion
As more automation pushes humans to higher-skilled pattern
detection, advanced Decision Intelligence,
and smarter actions, humans will use technology to enhance a person's cognitive
and physical experiences. There may be sensory
augmentation, perception
augmentation, and AI cognitive
assists in enabling higher-skilled work levels. In the case of physical
responses, appendage assistance, and
exoskeleton leverage may be enabled. Imagine having the assistance of
experienced experts in your ear, eye, or mind to accomplish more challenging
tasks. The new worker will have interactions with technologies that will enable
super skills and accelerated outcomes. This will start small and accelerated by
the end of 2021.
Immersive Experiences
& Visibility
All constituents will have more immersive and pleasing
experiences, making them more informed and satisfied. Customer Journey Mapping/Mining technologies will allow
organizations to get real-time and truthful feedback from their customers,
employees, partners, and vendors to help improve their experiences on a
continuous basis. Virtual Reality and
Mixed Reality has the potential to radically influence the direction of
improved customer experiences, product supply, and value chain services. These
new visibility assists can give customers a real sense of progress towards
their outcomes when balanced with organizational processes. Some organizations have seen the value in
onboarding and immersive training in a safe and realistic virtual environment.
Augmented &
Real-Time Data Management
The amount and speed of data are increasing faster than our
ability to manage it. Big data is
turning to a complex and multi-head monster of data types with various
different requirements. Managing all the data and data types will require
assistance. Data marketplaces and
Exchanges are emerging to add to the data chaos. Managing the various data
sources will need to leverage the Database
of Now integrating various data sources in the cloud, AI's ability to learn
from the incoming flood of data, and the metadata that defines it within its
various contexts and workloads are essential. Dark data will start to be better
understood. Data journeys and transparency will be assisted by practical Blockchain that enables data traceability.
Autonomous Bots &
Edge Computing
Autonomous bots/agents that bid on work at the maximum and
minimally perform activities on "the edge" with AI help. Edge
processing, data collections, and decisions are placed closer to the
information and activity source to sense, decide, and respond closer and in the
proper context. Often the IoT is
where this occurs when machines, sensors, and controllers are involved with
physical activity, but there are instances software, Digital Twin, or not have a presence at the edge. Often these
activities are semi-autonomous and supervised today, but we are moving to more
autonomy over time. Smart spaces, smart production, and smart value chains will
drive these kinds of efforts. Look for robots as a service (RaaS) to elevate
some of the data density issues.
Net; Net:
Every organization will have to match their operating plans
to the technologies above and decide what they want to take on within their
cultural and risk limits. The danger here is to focus on technologies that can
contribute to short term financial results to the detriment of the future. This
is true with short term cloud efforts without thinking of the total cost of
ownership. Grab some profits, but invest wisely to compete digitally in the
future. Negotiate with your financial folks, please or hope they get more
innovative.
Organizations no longer have the luxury of sitting back and
waiting for an opportunity to react. Corporate performance depends on
intercepting the emerging future quickly, thus putting a premium on the Database of Now. We can see many
examples of the inability to pre-build strategic responses to emerging
conditions such as inverted yield curves, new super competitors, hyper
disinflation, currency shifts, pandemics, ECO events, and geopolitical shifts. So
how do organizations take advantage of the database of now and build for
interacting response cycles? The answer is to create a database of now and
leverage it differently at different levels in the organization (See Figure 1)
while trying to extend reaction to preemption. The interaction will be changed
at different levels and cascading levels of strategy, tactics, and operations.
Figure 1 Interacting
Response Cycles
Organizations are running in an automatic mode within normal
conditions; they take actions without a lot of thinking or bother. The problem today
is that automatic mode is not happening consistently with profitability like it
has in the past because of emergent conditions. These conditions can emerge
from a variety of sources represented by fast and large growing sources of
data. These conditions can come from outside the organization in either an
anticipated or unanticipated manner where they are not as controllable. These
conditions can come from inside the organization to optimize business outcomes
through observation or management influence in a controlled fashion. See figure
2 for the common sources of new conditions. The causes include changes in data,
patterns, contexts, decision parameters, results from actions, changes in
goals, or new risk management desires/demands. These changes are occurring on a
more frequent basis and at a faster speed, thus creating the need for the Database of Now.
Figure 2. Sources of
Emergent Conditions
Keep in mind that each level's triggers in Figure 1 will
likely be different, iterative, and possibly influenced/interconnected by other
levels.
Operations of Now:
Operations are focused on completing business events,
customer journeys, and work journeys with the support of humans, software,
bots, and physical infrastructure. The operations are often iterative and
monitored in a near real-time fashion. Today's operations require a Database of Now where the dashboards
reflect actual progress/completion of work. When exceptions emerge, responses
are required within the constraints of existing operational goals to make minor
adjustments. Also, significant adjustments need projects that may leverage a fail-fast
approach to make corrections. Operational goals are often influenced by changes
initiated by tactical and strategic decisions and adjustments. Analysis and
reporting help make appropriate adjustments without unseating other operations.
Tactics of Now:
Tactical management within the constraints of strategy tends to optimize interrelated outcomes that may look across multiple operational domains. Real-time forecasting based on real-time data is essential to predict the direction of aggregated operations. The Database of Now plays a crucial role in making better decisions by quickly changing rules to optimize business outcomes in support of strategic goals and directions. Tactical changes may imply shifting resources, changing rules, goals, and constraints of aggregated operations. Often key projects identified at this level, like recognizing patterns that might indicate the need for a new product or service. This is also the level that decides the amount and type of automation that will help reach the currently selected strategy.
The Strategy of Now:
Strategies tend to stay stable and are highly linked to the organization's
missional operations within Its typical communities and common scenarios.
Predictive and prescriptive analytics help shape expected scenarios that may be
sitting on the shelf with their associated tactics and operations ready to jump
in at a moment's notice. Of course, the Database
of Now can point to a playbook for switching scenarios when expected
patterns emerge. Still, unexpected patterns can generate the need to apply new
scenarios generated by more predictive and prescriptive analytics.
Net; Net:
It is pretty easy to see that fast monster data will create
the need for the Database of Now
necessary for better performance at all levels (strategy, tactics, and
operations). It is also clear that fast data without time lags generated by too
many synchronizations and transformations is necessary for better corporate
performance while keeping all contributing resources aimed at business
outcomes.
The state of data affairs over the last ten years or so
revolved around big data. Of course, size matters, but big data promises to
morph to monster data as more data sources hit the cloud with more tributaries
like voice, video, IoT, events, and business patterns. So what about all this
parked data? Are we going to keep storing it and bragging about how much cloud
space it consumes? Are you going to make
it cleaner and smarter or just admire it? I would suggest we make data more
intelligent and faster than just figuring out how to catalog and park it, so we
can use it later. Making it faster means treating the data as a database of now,
now of the future. Making data smarter can be tricky, but it is worth it.
Gleaning Data is
Basic Intelligence.
Capturing data of different types and classifying them is
pretty normal. Deciding how long to and where to keep it is essential.
Determining if it is worthy of a long time archiving is doing data a solid.
Knowing some basics about the data source and cost of acquisition and relative
purity is pretty much a given these days. Some data cleansing and organization
will help usage down the road.
Giving Data Meaning
is Average Intelligence
Knowing the data about the data (AKA meta-data) is essential
for interpreting it. The simplest is understating the data’s domain and its
relative relationship to other data (logically or physically). Data
representation and transformation options are pretty essential when combining
with other data. Knowing the key or identifier of groups of related data is
pretty standard. This step is where some of the impurities can be dealt with
before heavy use. First use usually revolves around visualization and reporting
to find actionable insights. This step is turning descriptive data into a
prescription at times.
Granting Data
Representation in Its Context is Very Smart
Most data is gathered and used within one or two base
contexts. One is undoubtedly timing/frequency, and the other is the primary
home of the data. For instance, the entity family it belongs to like product
data. Sophisticated context representation will go beyond an original context
or source to include others that have a neighborhood relationship with the data
grouping/entity. An example would be a product within multiple markets and
channels. This level is where statistical and predictive models enable more
actions to either react or intercept the trends indicated in the data. This
level is turning prescription to prediction to create/place data, event, or
pattern sentinels on processes or the edge to look for prediction completion or
variants.
Grinding Data to a Fine Edge is Smarter
We are interrogating data to learn the need for important
adjustments to goals, rules, or constraints for operating processes that
include humans, software systems, or machines. This level can build a change to
work in a supervised or unsupervised change process. This level starts with
machine learning and extends to deep leading, which peels back layers and
interrogates more data. In extreme cases, the data can be used to support
judgment, reason, and creativity. The worm turns from data-driven to
goal-driven, established by cognitive collaborations with management
principles, guidelines, and guardrails.
Grappling with Data
in Motion Right Now is Brilliance
The pinnacle of smart data is where the data coming in fresh
is used to create the “database of now”. At this level, all of the approaches above
can be applied in a hybrid/complex fashion in a near time/ real-time basis. This
level uses the combined IQ of all the AI and algorithm-driven approaches in a
poly-analytical way that leverages the brainpower combined with fast data. A
dynamic smart parts creation and dynamic assembly line would be a non-combat
example.
Net; Net:
Data: Use it or lose
it, but let the data lead to the learnings that sense, decide, and suggest
responses appropriate to action windows necessary to meet the timing need. If
it is a sub-second focused problem domain, the patterns in the data and
intelligent methods may make the decisions and take action with governance
constraints. If not subs-second focused, let smart notifications or options be
presented to humans supervising the actions. Don't leave all the precious data
parked for the future only.
I thought a post on generative art might be in the interest
of all things AI. This kind of art is leveraging AI, algorithms, randomness,
programs, and humans to create exciting and beautiful art. As you may know, I now
collaborate with Fractal Software to develop compelling and award-winning
artwork. In fact, some of my fractals are my best sellers. I have a great
friend and fellow artist, Bob Weerts, who is pushing this collaboration even
further. Below are two of his early generative pieces:
Bob employs lines as his fundamental stylistic element and
incorporates a chance in determining line length, density, and color. He cedes
some control over the work's final outcome to a process enabled by Software
he's written allow the piece to "emerge" over time. He plans to let
the Software take more control of these emergent pieces over time, letting
AI/Algorithms expand some range. I find his early pieces quite pleasing and
interesting already.
One source of Bob’s original inspiration is Casey Reas "Process Compendium," which, among other ideas, explored a synthesis of the Complexity Science notion of “emergence” and Generative Art in the early 2000s. An example of Reas Compendium work is below: (Click Here for Other Examples).
Reas is an internationally admired artist, but perhaps best
known as the author, along with Ben Fry, of the graphical sketching too called
"Processing," which is widely used in the domains of Art, Design, and
Media.
The significance of the generative art trend is perhaps exemplified by Christie's record of $432,500 sales of "Portrait of Belamy". The image is one of a series created by a group of young French students collaborating collectively as "Obvious". Obvious borrowed heavily from open-source Generative Adversarial Network (GAN) algorithms specially developed by a then-high school graduate Robbie Barrat but originally conceived by the AI researcher Ian Goodfellow. This has the ball rolling, and there is new momentum under the "GAN" movement. Generative adversarial networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the other (thus the “adversarial”) in order to generate new, synthetic instances of data that can pass for real data. They are used widely in image generation, video generation, and voice generation.
GAN's potential for both good and
evil is huge because they can learn to mimic any distribution of data. GANs can
be taught to create worlds eerily similar to our own in any domain: Images,
music, speech, prose. They are robot artists in a sense, and their output is
impressive. But they can also be used to generate fake media content of often called
"deep fakes."
Net; Net:
AI Generative Art is quite striking.
Since the whole field is getting more towards AI and less from the
artist/programmer, we can expect some exciting results in the future. I will
likely pursue a more intimate collaboration with all kinds of generative art
going forward. Keep your eye on Bob Weerts as he is a creative guy seeking this
edge faster than many other artists.
If you want to see my works, check out the fractals section here
If you want to know more about my collaborations with Software to create, check out this post
Read about more right-brained AI by clicking here
