2021 Top 10 Technical Trends

 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. 

2021 Top 10 Business Trends

We are all looking to put 2020 behind us, but there is still a significant hangover from last year. While COVID is enemy number one, we can't take our eyes off of business. To this end, I have identified the top 10 business trends that seem to be moving to the top of the list for your attention. These are not in any priority order, but these are trends to pay attention to going forward. While these trends might start in 2021, they will persist for years.

Integration of  Business Direction & Implementation

Managing the distance between direction and implementation is rising in importance as the time to respond shrinks for organizations to continue to thrive or even capitalize on emerging situations becomes crucial.  In the past, this has been a challenge, even in good times when organizations wish to make changes. Today organizations, especially those that do not plan for emerging scenarios and the triggers that indicate their emergence, are being forced to react quickly to implement change. New methods and tools that link stakeholder outcomes to implementation efforts are starting to emerge once change need is identified. 

Changeable & Balanced Stakeholder Driven Outcomes

Two issues need to be dealt with here. One notion is outcomes that are often driven by rigid existing organizations, processes, technologies, and skill sets and are hard to change or optimize. The other is that priority is almost always given to financial bottom lines with a short-term view. This will change as agility needs will demand a reorientation around goals that are balanced to include constituent goals along with financial outcomes. As underlying processes and systems become more flexible and faster to change by business experts, the power to adapt to positive change and skill expansion blooms.

Innovation Democracy Gains Traction

Speed to good innovation will require inclusion and collaboration with more folks. The driver behind this trend is the leverage of collaboration that is focused on more than accomplishing operational work at many levels reaching into change opportunities. This opens up innovation for tactics and strategy from management only control to all those who are involved or care about the outcomes of an organization. This goes beyond an electronic "suggestion box" to the blooming of ideas while implementing the change or executing products or services.

Strategic Agility Given Priority

Often results are aimed at implementation and run forward without a thought for future change. Implementations need to be instrumented with agility in mind to adjust operationally as fast a possible. This minimally helps with strategic shifts in react mode, but savvy organizations can preplan scenarios and have agile responses ready to implement change proactively. Of course, predictive methods and tools will be of great help here. 

The Emergence of  a Security Mesh

Security is transforming from a centralized internal set of safeguards managed by one central source to a distributed approach where each asset has a set of security safeguards to start the security efforts early before things get close to the mother ship. Each step closer to the source of data and assets will have additional and even redundant security applied at its level forming a security mesh. In addition, steps towards a better and foolproof digital identity will emerge, probably additionally driven by health goals on top of the existing financial security. 

Competitive Customer Journey Experience

This will be a huge change to encourage organizations to not just think customer experience is about standard transactions coming through their channels. The customer journey often starts before and continues beyond an organization's front door to include customer unique goals. These goals often get clustered into personas, but unique additional goals could be personalized. So the scope of customer experience is expanding from a user interface on a mobile device for instance to a holistic experience represented by a complete journey customized by a customer's individual or aggregated goals. 

Organizational Culture of Insights

Organizations are expecting to add more analytics to become more aware in advance of reactions. The emergence of fast boards (dashboards that combine real-time with archives) will be the first step to identify signals events or patterns of interest. Organizations that want to optimize their efforts will start with important scenarios or decisions and drive the analytics in those directions. Focused analytics will be the minimum, but organizations will encourage their associates to find emerging insights and innovate with these insights, 

360 Organizational View of Interactions

Organizations often only design surveys for after the fact understanding of how effective their organizations are on several fronts. While this is an appropriate approach but does not catch many important issues and trends. To add to the complexity of the effectiveness of feedback, organizations tend to only focus on customers. There will be an expansion to all constituents and a movement to catch sentiment in real-time to respond quickly instead of reactively dealing with negative social outlets' feedback. 

Skill Leaps Through Augmentation 

The slowest part of change is usually tied to humans and their ability to cope with change or have the proper skills to implement and live with the changes. Organizations are wise enough to not only depend on their people but also on video providers to get more skills. Organizations will invest in buying or building training videos minimally. Savvy organizations will create AI-driven bots that either give audio or video advice in the proper context. 

Real-Time Anywhere Operations

Completing work remotely is not new, but has grown in deployment rapidly in 2020 because of COVID. This trend will continue in earnest but will accelerate to real-time and dynamic. My expectation is that freelance customer service will likely become a growing trend. Dynamic labor pools and management will be a growing trend.  

Net; Net:

Thes above business trends are only tied to me and my network of associates, customers, and research. I hope these get you thinking. If you agree or disagree, I would love to hear from you. 

360 Smart View of Interactions at Scale

Nearly every organization is challenged with getting a grip on its effectiveness with all its constituents. The focus today surrounds customer interactions as the primary target these days. There is a challenge to get the story behind the ever-increasing number of exchanges from various channels/sources in many individual formats. This challenge is complicated by the speed, variability, quality, and quantity of this kind of emergent unstructured data. The task of automating the process of translating large volumes of unstructured text into quantitative data to uncover insights, trends, and patterns to make better decisions and take appropriate actions is before us all.

Multi-client Feedback is Essential

Getting a comprehensive 360 view of interactions usually starts with clients and usually revolves around customer-focused interactions. Commonly the 360 cycles are thought of as a singular customer cycle depicted in figure 1. Feedback is often really broader. Often done at the individual level to understand the satisfaction and personal customer goal satisfaction, there is additional information in the aggregation of many interactions and feedback into overall intentions, the sentiment, themes, categories, and trends.

Often rich voice-based data is used as a source to mine, parse, identify, extract, categorize, cluster, and understand syntactically for linking/chaining. Of course, there are text data sources such as social comments and correspondence to infuse feedback leverage.

                                   Figure 1: Typical Customer 360 View

 Insights into Praises, Problems or Broken Promises

It is essential to know what we did right and what we did wrong, but it’s a bit more complicated than a transaction event view. While it is crucial to know if an organization accomplishes its outcomes on each transaction or event, there is a bigger picture. Often, any organization's contribution to the customers' overall journey is a fraction of the contribution to real customer satisfaction. Savvy organizations look at the total journey and what it takes to get to realistic customer goals. The journey may include looking at the partners, vendors, and out-tasked steps, contributing to overall satisfaction.

The broader scope will point out the real trends and patterns associated with the full 360 views. The visualizations and sentiments will be quite different in this expanded view that follows the entire customer journey versus the transactions handled in the real customers' journey and overall customer desired outcomes.

 Acting Quicker with Armed with Intelligence

By analyzing the proper scopes of simple Interactions in the context of cross-functional processes, journeys, and hybrid organizational/customer outcomes, organizations can react faster and get ahead of the game. This process will require aggregating big/monster data insights created by analytics and machine learning. These visualizations, insights, and understandings can guide intelligent actions that may change the processes with customers' corrections and innovations. In some cases, new emergent behaviors can play in prediction through additional statistics or models to deploy appropriate actions to intercept recent trends.

Net; Net:

We already know organizations can leverage sentiment to adjust their behavior to increase customer retention and increase their reputation as a beloved partner. It may be accomplished via social media monitoring, competitive intelligence, and process improvement. The more kinds and amounts of data leveraged in a 360 view, the smarter the organization will be to accomplish its desired outcomes while gaining momentum plus a reputation for positive hybrid results.  

 

 

 

 

 

Get Ready for the Big Shift to the Data Mesh

 Today many organizations are stuck in data muck. Simultaneously, organizations are drowning in vast amounts of new data while the business needs more speed in digesting data that are often aggregated with other data. In today's outcome-driven world, management doesn’t need more dashboards. They need fast boards that tell the data story by incorporating in-the-moment operational data with historical data to create the business context needed for decision-making. One of the main obstacles that organizations have is their data infrastructure sprawl. Organizations need to simplify to accelerate by changing their data landscape to be re-oriented to the data mesh architectural concept.

The Data Iceberg Needs Better Management

Like it or not, our data management efforts are getting overwhelmed as the amount, complexity, and contexts of data are emerging fast. In contrast, slow legacy data sources are a hidden danger just under the surface. When someone has to traverse the end-to-end data journey from start to outcomes, the complexity is almost overwhelming even if things don't change. The problem is that they are changing and changing fast, creating a need for more data to manage. Now, imagine a goodly sum of the data migrating to the cloud as the data sources further distribute and include new signals, events, patterns, and contexts. Figure 1 attempts to identify all the sources, emergent or not, that either create or contain data to manage to service outcome-driven organizations. 

                                                       Figure 1 The Data Iceberg

We need a dynamic data-view maker with all the sources to configure to their dynamic needs and outcomes. Today organizations have to over-specify and create the data view ahead of time. A real unified dynamic data experience is what is needed. Today’s approach is to think about the data lifecycle in terms of the functional role of the datastore. That is, new data gets created by applications and stored in that application’s OLTP database. Then, for analytical purposes, that data is copied and moved to an OLAP database for reporting. These days, there are more application types and systems generating more types of structured, semi-structured, and unstructured data and storing and processing that data in a greater variety of single-purpose datastores types, for instance, a document database for product catalog information. This causes unnecessary latency where real-time needs are important but also presents a greater management cost by maintaining the variety of datastores and maintaining the skill sets needed to design and operate the datastore variety.  A data mesh approach reorients the data management efforts to align the 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-use. A data mesh manages both fast and slow data, whether it is organized in a centralized or distributed fashion. Within a data mesh, you have what’s known as “nodes”. Each node corresponds to a data product and defines all the data, metadata, consumers, and providers of that data product. When realizing these “nodes”, there’s an opportunity to gain efficiency by selecting fewer datastores and use them for a broader range of workloads. For instance, there are now modern, cloud-native, distributed SQL databases that support what I call “Monster Data” while storing and processing real-time streaming data and historical data simultaneously. These can be used in a data mesh to reduce the number of skills set and reduce the data infrastructure sprawl, ultimately resulting in a simpler and less costly data landscape.  In essence, a data mash unifies and simplifies data management coupled with a modern, cloud-native distributed SQL database can lower the cost for more extensive monster data sources that move at lightning speeds. See Figure 2 for a depiction of a data mesh.

                                                          Figure 2 Sample Data Mesh

Net; Net:

The data inventory to manage has gotten unwieldy and continues to be like a monster to tame. Organizations will need new architectures including distributed data cells that carry the intelligence to self-manage to play well with other data sources from various contexts. As organizations try to leverage cloud data economically, they have to watch out for the pitfalls of hidden cloud costs. All of this is a must while simplifying the access to the new and emergent data combined with legacy data types or sources. This transformation is a significant accelerator to digital business transformation.