Jurgen Appelo is a software engineer, trainer, entrepreneur, author, speaker and traveler, who has been driving agility in companies. One of his works, “ Management 3.0 ” , condenses a team management methodology so that they can survive amid chaos and fragility.
This model, based on Edgar Morin’s so-called complexity theory, is based on the notion that a system – a company, a government, a project – is not feasible to analyze as a mere sum of its component parts; rather, it is the relationships and interactions that give it meaning and momentum. To graph this, imagine a network, with interlocking threads connecting each component. These threads are the facts, actions, decisions, and interactions that make up the world.
That is why management has been seen for several years as a system of networks and people, of dynamic relationships, and not only about areas or departments, profits and processes. It is a living system, not machines that systematically replicate the same result.
Principles for energizing and developing talent
In its 3.0 model, Appelo shares several principles that serve to support the work of leaders and teams in today’s changing world. Here are some of them:
1. Energize people
To achieve this, it is necessary to know what it is that motivates them and that is part of their life purpose: the more consistent it is with the purpose of the organization, there will be a greater individual commitment and team cooperation. For the psychologist and professor Edward Deci, there are two types of motivations:
Extrinsic: stimuli that are provided from outside the person (for example, a performance bonus, constant congratulations from the leader, etc.).
Intrinsic: those stimuli that are internal and relevant to the person, even when it is not their primary goal (for example, a project in charge). However, if you find a meaning, a why in what you do, you connect better and there is your own reward.
Author Daniel Pink offers a similar look at intrinsic motivation in his book “Drive”, where he affirms that most people are moved more by this type of impulse than by extrinsic. In other words, in the end and in essence, people care more about satisfaction than external rewards, although they should not be lacking, and he explains that there are three factors that new management leaders need to take into account to boost talent: mastery -the desire of each one to be better in what is important to him-, autonomy -the impulse to guide his own life-; let me mention self-leadership-; and purpose – intention to serve something greater than ourselves.
2. Empower teams
To achieve this, the author of Management 3.0 points out that it is entirely possible for each team to organize itself, if it has the confidence of the leaders.
At this point, it is essential that those who lead people focus on doing their job and not on micro-management and that teams participate in collective decisions on relevant issues. In addition, it is necessary for everyone to understand that they are part of a joint system, and not the mere sum of individualities, and that the knowledge of market needs is not in the hands of a single person, but that there is a broader perspective of their needs.
To empower, there are four lines of action that are strategic to generate relationships of trust:
Let the leader trust his team.
Let the team trust their leader.
Let team members trust each other.
Let the leader trust himself.
3. Development of skills
We already know that it is difficult for any company to achieve results if its members are not trained; and the leaders are responsible for enabling the conditions for this process to take place. Some ways are:
Leading by example: living what is preached.
Promote self-learning: appreciate personal maturing time.
Coaching and mentoring: as transversal support and support tools throughout the organization.
Training and certification: to raise standards against the competition.
Collaborative learning: internal development, where everyone learns from each other.
Learning from error: doing retrospectives and tests in controlled environments.
Measure the results: feedback in the shortest possible cycles; use of keeping metrics on information radiators; indicators agreed between those who participate.
Smaller teams: the author recommends no more than 10 to 12 people.
4. Improve everything and observe the team environment
It is key in the management 3.0 model to focus on real continuous improvement, for which it is necessary to facilitate change processes and model the natural resistance that may appear.
Some suggestions for leaders are to observe the team environment, what they need, and let it be known that you are available; find cracks or faults and go to their roots to promote solutions that the team implements; define clear and specific goals and have great communication skills, a key factor of every good manager.
Many teams use Mind Maps to explore certain topics. Similarly you can use Personal Maps to explore your team itself. Personal Maps facilitate team collaboration and bonding in a rather distant world. With this video, you will learn how to use Personal Maps to break down the barriers of cubicles and longer distances, and then you may even learn how silly you were when you thought you had nothing in common! Here you can learn more about this Management 3.0 Workout: https://management30.com/product/work… Here’s a trick, instead of presenting your own, spark conversations by presenting each other! What are you waiting for? Try this 7-minute exercise out and tell us below how it went!
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You’ve probably heard business leaders justify their flat-footedness in a crisis by claiming that every organization is flying blind in times of deep uncertainty. But in fact some leaders know precisely where they’re going. They understand what’s required to chart a course through market turbulence, and they’ve built organizations with keen situational awareness.
When it comes to developing the ability to figure out where things are heading and respond nimbly to a changing environment, nothing is more important than analytics. Unfortunately, in recent years analytics (also known as data mining or business intelligence) has become the unloved stepchild of data sciences, overshadowed by machine learning and statistics. Those two disciplines layer mathematical sophistication on top of a foundation of human intuition, creating an appealing illusion of objectivity and deft steering. Ironically, of the three, analytics is the most essential competency for navigating crises.
Solutions based on AI and machine learning hum along well during stable times but fall apart when disaster strikes. These technologies automate tasks by extracting patterns from data and turning them into instructions. Such models can quickly become obsolete when the inputs to the system change. Analytics, in contrast, alerts you when the rules of the game are changing. Without that kind of a warning, automation solutions can quickly go off the rails, leaving you exposed to exogenous shocks.
Statistics has a similar shortcoming during a crisis. Statisticians help decision-makers get rigorous answers. But what if they’re asking the wrong questions? While statistical skills are required to test hypotheses, analysts have the acumen to come up with the right hypotheses in the first place. To attempt statistics without analytics, you’d need great confidence in your assumptions—the kind of confidence that’s foolhardy when a crisis pulls the rug out from under you.
Analysts thrive in ambiguity. Their talent is exploration, which makes them particularly good at foreseeing and responding to crises. By searching internal and external data sources for critical information, analysts keep a finger on the pulse of what’s going on. They scan the horizon for trends and formulate questions about what’s behind them. Their job is to inspire executives with thought-provoking yet qualified possibilities. Once the highest-priority hypotheses have been short-listed by leaders, then it’s time to call in a statistician to pressure-test them and separate true insights from red herrings.
During good times, leading organizations build analytics capabilities to strengthen their ability to innovate. Analysts’ ability to find clues to such things as shifting consumer tastes can help firms take advantage of opportunities before less-savvy competitors do. When the going gets tough, however, what looked like a nice-to-have innovation booster turns into a must-have safety net. To be sure, some events are impossible to see in advance—the true black swans—but addressing their fallout is a game best played with open eyes.
Unfortunately, it’s very hard to cobble together a mature analytics department on short notice. The technical skills that allow analysts to guzzle data with lightning speed merely increase the mass of information they encounter. Spotting a gem in it takes something more. Without domain knowledge, business acumen, and strong intuition about the practical value of discoveries—as well as the communication skills to convey them to decision-makers effectively—analysts will struggle to be useful. It takes time for them to learn to judge what’s important in addition to what’s interesting. You can’t expect them to be an instant solution to charting a course through your latest crisis. Instead, see them as an investment in your future nimbleness.
It also takes time to secure access to the promising data sources analysts need. Ideally, business leaders won’t wait for a big disruption to begin building relationships with data vendors, industry partners, and data collection specialists. Bear in mind that in the face of an extreme shock, your historical data sources may become obsolete. If your understanding of the past fails to give you a useful window on tomorrow’s world—perhaps because a pandemic has changed everything—it doesn’t matter how good your information was yesterday. You need new information. After the 2008 financial crash, for example, banks around the world recognized that there might be an advantage to analyzing nontraditional signals of creditworthiness, such as data from supermarket loyalty cards, but not all players were equally positioned to get access to them.
Additionally, your internal data stores may require special processing before analysts can mine them, so it’s worth thinking about hiring supporting data engineers. If analytics is the discipline of making data useful, then data engineering is the discipline of making data usable; it provides behind-the-scenes infrastructure that makes machine logs and colossal data stores compatible with analytics tool kits.
When I began speaking at conferences about the importance of analytics, I found that convincing an audience of its value was the easy part. The mood changed when I explained the catch: Analytics is a time investment. You can’t count on getting something useful out of every foray into a data set. To succeed at exploration, your organization needs a culture of no-strings-attached analytics. As the leader, you are responsible for setting the scope (which data sources should be looked at) and the time frame (“You have two weeks to explore this database”). Then you must ensure that analysts aren’t punished for coming back empty-handed.
During an extreme shock, your historical data sources may become obsolete. Then it doesn’t matter how good your information was yesterday. You need new information.
Once business leaders accept that analytics represents an investment that may not immediately pay off, I hit the next stumbling block: the perception that only a large and technologically sophisticated company such as Alphabet can afford it. This is nonsense. In my experience you’re more likely to find analytics thriving in start-ups than at well-established behemoths.
Start-ups naturally invest in analytics as they try to navigate a new market, with several generalists taking on a share of the exploratory work. Then as the venture grows, the culture changes. Workers are trusted less and made more accountable for return on their efforts, and overzealous management stifles opportunities for analytics to thrive. Analysts hired into this culture rarely get to enjoy the most interesting part of their work—exploration—and instead serve as human search engines and dashboard janitors. Many quit out of frustration as their potential is squandered.
Creating a culture where analytics flourishes takes thoughtful leadership. As organizations grow toward incumbency, only the most visionary will have the courage to nurture a true analytics department and make sure that business leaders have access to it and are influenced by it. Industries that have been burned by a previous crisis — banking is a good example — are especially likely to invest in analytics and apply it to risk management.
Becoming a leader in analytics takes a commitment to trust your analysts and give them space to do their work. Their job, after all, will be to reveal threats that you never even imagined should be on your radar. That sort of work can’t be managed with a stopwatch and a checklist.
Crises such as a pandemic—when no one has the answers, and uncertainty is high—remind us of the importance of asking the right questions. Analytics gives firms an edge in learning and adapting. When the world is suddenly upended, those who can learn the fastest are best positioned to succeed. Smart companies will invest in analytics today to get ahead of whatever is coming tomorrow.
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Organizations today are witnessing an increase in data volumes across various industries that need addressing to maintain a differentiated data management practice and stay competitive. The cloud offers capabilities to address any data management need; however, not all workloads can migrate to the cloud easily. This could be due to legacy application dependencies residing on-premises, data residency regulations or low-latency computation needs, such as in healthcare, financial and manufacturing industries.
Read on to discover:
Constraints that keep data tied to on-premises environments
Why companies should embrace hybrid data management practice
How AWS Outposts meets your hybrid data needs
Data management constraints organizations face
Data residency regulations, low-latency requirements, and complex application migrations are some of the main issues surrounding the management of data. The journey to the cloud also creates challenges for data infrastructure and development teams to design data management models that provide consistent and reliable cloud services on-premises. These challenges can vary depending on the specific industry and operational requirements, but include:
Hybrid cloud benefits
Organizations can deploy cloud infrastructure on-premises, determine data processing priorities, and when ready, migrate towards the cloud.
1. Cloud capabilities on-premises
Amazon EC2 instances featuring Intel® Xeon® Scalable processors brings the same cloud capabilities on-premises.
2.Seamless migration to the cloud
Build an application once and deploy it in the cloud, on-premises, or in a hybrid architecture with consistent performance.
3. Accelerated modernization
Companies can accelerate the adoption of cloud services on-premises across teams.
4.Focus on what matters
Reduce the time, resources, operational risk, and maintenance downtime required for managing IT infrastructure, giving you the ability to focus on what differentiates your business.
AWS offers a hybrid solution to meet data management needs
AWS Outposts catalog includes options supporting the latest generation Intel powered EC2 instance types with or without local instance storage. Organizations can choose from a range of pre-validated Outposts configurations offering a mix of EC2 and EBS capacity which are designed to meet a variety of data management needs.
AWS Outposts options:
Innovate with AWS
Healthcare use case:
Medical professionals manually collect structured data to store and analyze in vital fields such as cancer staging, medical/family history and patient-reported symptoms. AWS Cloud services automates data collection, where using machine learning inference models amplify data processing and extraction of valuable insights. AWS provides the tools, services and APIs to deliver real-time video analytics and pattern matching, while delivering on-premises flexibility and access to cloud capabilities when needed.
Finance use case:
Financial or government institutions that need to comply to specific data regulations use hybrid cloud to meet their contractual obligations with their customers and demonstrate compliance with legal policies. AWS Outposts allow these organizations to maintain data visibility, process sensitive data locally, including collecting local cache and filtering, and when needed connect to Local Zones or send it to AWS Region.
Security use case:
Companies that are interested in using Outposts to run physical security environments, such as video surveillance, badging systems or security systems, can build and run these workflows on Outposts, archiving relevant data to S3/Glacier within the AWS Region for forensic analysis.
Getting started with AWS
With a consistent set of infrastructure, services, tools, and APIs, AWS simplifies your data management and data migration process, reducing the effort and complexity involved. Leverage the latest Intel technology innovations to accelerate modernization at your edge too. Find out more about hybrid data management for your organization using AWS Outposts in our full guide here.
AWS infrastructure solutions allow enterprises across all industries the opportunity to bring AWS services closer to where it’s needed, such as on-premises with AWS Outposts, in large metro areas with AWS Local Zones, or at the edge of 5G networks with AWS Wavelength. These solutions offer enterprises the capability to deliver innovative applications and immersive next-generation experiences using AWS cloud services where they need it. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, speed time to market, and become more dynamic. To learn more about AWS infrastructure solutions, visit aws.amazon.com.
Enterprises are rapidly adopting the cloud for greater agility and cost savings. However, they often find that some applications need to be re-architected or “”modernized”” before they can be moved to the cloud. Others need to remain on-premise due to low-latency or data processing requirements. As a result, enterprises are looking to hybrid cloud architectures to integrate their on-premises and cloud operations to support a broad spectrum of hybrid use cases, such as data center extension, VMware cloud migration, or building and managing applications using a common set of cloud services and APIs across on-premises and cloud environments. In this tech talk, you will learn how you can build your hybrid cloud architecture with AWS. We will cover our extensive portfolio of services that offer seamless integration between your on-premises and cloud environments for any hybrid use case. Learning Objectives: – Discover AWS services that offer seamless integration across on-premises and cloud environments – See how to build the hybrid cloud architecture to support your use case – Learn about new services that bring cloud services on-premises
COVID-19 forced many companies to unexpectedly make fundamental shifts to their business models over a very short time period. Each of their pivots—from a transition to online transactions to new supply chain models, fulfillment approaches, or finance arrangements—has downstream tax implications. This, in turn, increases the complexity and workload for tax departments.
As the pandemic continues and businesses adjust to the new normal, we are seeing some common themes emerge. First, most business leaders are accelerating existing plans around digital transformation, particularly related to cloud. Second, many are re-evaluating their operating models. Some are choosing to refocus internal efforts on core competencies, cutting resources and budgets for enabling areas to reduce costs. This trend extends to tax departments.
These colliding factors are creating a paradox for tax leaders: At a time of increased complexity and need, they have fewer resources to meet the demands. Yet, this dynamic also presents opportunities to accelerate change and transform in fundamental new ways. Those that are nimble will have a greater ability to thrive.
Unsurprisingly, business leaders increasingly recognize that their existing technologies, processes, and data-management approaches are dated. Newer technologies and capabilities offer better, faster, and cheaper ways of doing things. One area in which we are seeing a significant acceleration is cloud-based enterprise resource planning (ERP) solutions.
As businesses move to cloud-based ERPs in virtually every organization, it’s important to consider that tax is the greatest consumer of enterprise data. Almost every business process has a tax implication. If you are a CEO, CFO, or CIO, one of your top priorities should be ensuring that your tax department leaders have a seat at the table when developing your digital transformation road map.
Done right, the shift to cloud-based ERP systems should save money, reduce complexity, and enable better risk management. By providing a common data source, recorded in a standard language and syntax, cloud-based ERPs should eliminate most of the heavy lifting and data mining that most tax functions perform manually today. This increases confidence in the data and frees up people to focus on analytics, scenario-planning, and strategic advice to the business.
Agile Operating Models
Tax leaders today are confronted by the high cost of digital transformation, talent gaps in expertise and capacity (which may have increased due to cost cutting measures), and the ongoing economic uncertainty intensified by COVID-19. While it is becoming evident the old ways of working will no longer suffice, there is also an increasing recognition that in many industries, the capital investment required to transform the tax department won’t be coming any time soon. Out of necessity, many have begun to rethink their operating models. They are increasingly seeing co-sourcing and outsourcing as a way to access innovative technology solutions, expertise, and capacity at a lower cost.
The good news is there is a spectrum of operating models—from maintaining an in-house model for all activities, to a model in which some activities are completed partially or fully by a third party, to a completely outsourced model in which a provider “operates” the entire function.
While co-sourcing and outsourcing may be more familiar, the “operate” model represents a more fundamental shift. Companies no longer pay to maintain their own systems, similar to a subscription model that allows access to the capabilities, skills, and resources of a third party for a set fee. Technology companies shifted to this managed service model years ago when they introduced licensing. Consider the example of Microsoft Office: Organizations buy a license to get access to a suite of Microsoft’s tools, but Microsoft maintains the software on their behalf.
In the tax function, when considering cosourcing or outsourcing, activities are evaluated on two dimensions: value to the company and need for institutional knowledge. Work that is low value and routine, where the output needs to be high quality and efficient, are strong candidates for co-sourcing or outsourcing. This may include compliance activities and data management tasks, or work that is highly specialized but ad hoc and does not require significant institutional knowledge to complete, such as transfer pricing documentation or tax controversy matters.
Accelerating The Future
Resilient tax leaders recognize the complexities emerging from the pandemic amid so many other global forces, from the changing implications of globalization, the rise of Asia, the reduction of the shared economy, and the screeching halt on international travel due to closed borders. With so much in flux, the tax function becomes even more critical in building efficient and sustainable organizations over the long-term. To do so, tax leaders must assess their current models and consider the continuum of options to most effectively run their departments.
This massively disruptive event, which hindered businesses’ ability to serve clients and customers in the same way they did in the past, is also proving to be an important catalyst for change. It has dislodged longstanding inertia behind how, where, and when work gets done. The impossible is now possible. The once unthinkable is now open for consideration. Opportunities abound. How will you use the momentum and mindset shift to accelerate the future of your tax organization?
Based in London, Philip Mills is the Global Tax & Legal leader at Deloitte. Prior to this, he led the Global Business Tax practice for two years and the UK Business Tax practice for seven years, amongst other roles. Philip also leads the Global Tax & Legal Executive and is a member of the Global Executive Committee. He has a Physics Bachelor of Science degree from Liverpool University, is a member of the Institute of Chartered Accountants in England and Wales and is a member of the Institute of Tax.
For nearly 20 years, Philip focused on M&A tax, particularly on Private Equity, Real Estate and Hedge Funds. He has worked on some of the more significant, large and complex European transactions in recent years as well as supporting the Fund advisers. Most recently, he took on advisory roles to some of Deloitte’s largest multinational corporate clients.
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