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Deep Learning, Artificial Intelligence, Convolutional Network, Video Coding, Hevc.
reduction by EDCNN is from 1.77% to 12.06%, and 6.27% on average using low delay configuration. And the BDBR reduction by EDCNN is from 0.41% to 12.31%, and 6.62% on average using random access configuration. EDCNN outperforms SRResNet [26] and RHCNN [23] for both configurations. 4.2. Visual
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Quality Visual Quality The compared areas in HM16.9 have obvious artifacts, including ringing artifacts and color excursion. The other two algorithms can reduce most of artifacts, however, some obvious artifacts are still there. SRResNet [26] still contains some blocking artifacts while RHCNN [23]
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makes the image become more blurring, and lots of details in image crops are eliminated. 4.3. Computational Complexity Computational Complexity (%) Against Original HEVC On average, the proposed EDCNN increases the encoder complexity by 172% and 247% using low delay and random access configurations
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respectively. 4.4. Model Size and GPU Memory The model size and GPU memory of the proposed EDCNN are 18.2 MB and 5193 MB respectively which are both smaller than RHCNN. During the days of coronavirus, let me have a challenge of writing 30 stories again for this month ..? Is it good? This is the
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23rd story in this month. Thanks for visiting my story.. Reference [2020 TIP] [EDCNN] Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC Codec Filtering JPEG [ARCNN] [RED-Net] [DnCNN] [Li ICME’17] [MemNet] [MWCNN] HEVC [Lin DCC’16] [IFCNN] [VRCNN] [DCAD]
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I think in the age of AI, it is very important to understand where it all started. Linear regression is one of the simple powerful algorithms to start with. Linear regression is a supervised learning algorithm that is mainly used for regression tasks. For instance, predicting the house prices based
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on number of rooms. It attempts to fit a straight line through the data points to explain the relationship between two variables. In the case of house price prediction, the number of rooms and the price of the house. Source: [1] As we know the equation of the line is y = wx+b where y is the output,
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x is the input, w is the slope/weights and b is y-intercept/bias. So our linear model can be written as below: Linear model or line equation In linear regression, we need to find appropriate w and b in such a way that we can fit a straight line that is going to be an approximate representation of
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the data points. In order to identify the best w and b, we need to define the cost function, J(w,b). The cost function gives how good is the model with a specific value of w and b. The cost function is computed by taking the squared error of the difference between predicted, y and ground truth y of
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all the samples in input, x. To normalise, we divide the final value by 2m. This type of cost function is called mean squared error. The cost function of linear regression We can then rewrite this formula in the following forms: The cost function of linear regression expanded Now question how we
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find out a good value for w and b where the mean squared error or cost is minimal. In order to do that, we utilize the gradient descent algorithm. Gradient descent and its variants are widely used in machine learning. Gradient descent algorithm, Source: [2] The gradient descent algorithm is a
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first-order iterative optimisation to find out the minimum value in the cost function. In this approach, we initialise w(weights) with a random value and find out the gradient. Then we update the w in the opposite direction of the gradient. And, this process continues until the cost minimum is
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found. The gradient always points towards the direction of the greatest increase, so in order to find the minimum or descent point, we update the weights in the opposite direction of the gradient. We apply the same approach to find the minimum of b as well. Pseudocode of the gradient descent
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algorithm Update the weight in the opposite direction of the gradient. The equation has w as weights, alpha as the learning rate and the gradient of the cost function j(w,b). Note: Even though we use a partial derivative of the cost function J, we call it a derivative or gradient. 2. Similarly, we
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update the b (bias) in the opposite direction of the gradient. 3. Repeat steps 1 & 2 until w & b converges. Now, we have the algorithm in derivative form as shown above. We can expand the derivative further to make it ready for implementation. Firstly, we apply the full equation of the cost
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function, J(w) Then, we derive further by expanding the function f in the form of a linear equation. Finally, we apply the derivative rule to get the final equation that is ready for implementation in the code. Similarly, we apply the above derivation to get the implementation-ready equation of the
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partial derivative of cost function with respect to b. Once we use the derived formula in the gradient descent algorithm (pseudocode ) described above, we can find the appropriate values of w and b to fit a straight line over data points. If you like my write-up, follow me on Github, Linkedin,
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and/or Medium profile. Reference https://livebook.manning.com/book/grokking-machine-learning/3-2-the-solution-building-a-regression-model-for-housing-prices/v-4/ https://sebastianraschka.com/faq/docs/gradient-optimization.html
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Organizational Design, Team Topologies, Platform Ecosystem, Ecosystem, Micro Enterprise.
Explore the role of the platform models in organizational development. Understand the convergence of team topologies and micro-enterprises. Discover how to streamline collaboration, enhance performance, and reduce cognitive load. Co-authored by Emanuele Quintarelli and Luca Ruggeri NOTE THAT: 👉
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This post is a re-publication of an original posted on our blog, check the original here. Subscribe here: https://boundaryless.io/newsletter_subscription_page/ This article is part of a series on the concept of a Platform Organization. Check out the first installment on the evolution toward the
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platform org, and the second one on choosing pilots. Introduction Organizations are made of teams and how to structure teams has been a focus of management theory for decades. Countless frameworks have focused on team structures and interactions, acknowledging W. Edwards Deming once expressed so
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clearly by saying: “A company could put a top man at every position and be swallowed by a competitor with people only half as good, but who are working together.” Deming’s point could be summarized more widely by saying that the performance of a system — such as an organization — largely depends on
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how well the parts fit together, not how well they perform individually. Understanding the recurring team types an organization needs and how they should communicate has become a top business challenge. This is due to the need to adapt to fast market changes and build adaptive organizations, able
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to distribute autonomy and overcome bureaucratic top-down management practices. Despite originating from agile software development, the Team Topologies metamodel has achieved tremendous success in recent years. It was introduced in the book “Team Topologies: Organizing Business and Technology
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Teams for Fast Flow,” by Matthew Skelton and Manuel Pais. This approach lists four team types and elucidates their interaction modes. It’s a useful guide on structuring teams and interactions to build and deliver software products efficiently and adaptively. These four team types are:
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Stream-aligned teams: aligned to a flow of work from (usually) a segment of the business domain, like a group of product features or a small product. Platform teams: a team (or grouping of teams) that provides a compelling internal product to accelerate delivery by Stream-aligned teams — for
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example, an authentication or identity service; Enabling teams: that help Stream-aligned teams to overcome obstacles, detect missing capabilities, and generally reduce cognitive load by providing coaching or domain-specific expertise; Complicated Subsystem teams: build and maintain systems
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requiring heavy specialist knowledge. The interaction between SA teams (that build customer-facing elements), Platform Teams, and Complicated Subsystem teams delivers a product value proposition to the market and customers. The Team Topologies metamodel also proposes to limit “interaction types”
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between teams to three major ones, aside from the team types: Collaboration: a temporary exchange with all teams equally contributing. This can account for co-creative innovation, knowledge, and skill sharing; X-as-a-Service (XaaS): An interaction pattern where one team provides something as a
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service to another — for example, through an API. Here, responsibilities and expectations are clearly defined; Facilitating: i.e. the typical interaction happening between enabling teams and Stream-Aligned — where one team assists another in advancing their capabilities to reach self-sufficiency.
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This removes long-term dependencies and allows for autonomy among teams. Many software-centric organizations (a lot these days) that are grappling with rapid changes and need consistent performance end up with numerous teams and unclear roles and responsibilities. Consequently, many have adopted
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the Team Topologies metamodel — or derived approaches — to derive benefits. According to Team Topologies, the main outcomes of adopting such a team discipline are: a reduced Cognitive Load on teams thanks to well-defined, manageable work scopes that reduce the stress of having to figure out your
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role in system-wide complexity; an optimized structure to achieve Fast Flow thanks to clear interaction models ensuring smoother, quicker workflow and faster feedback; enhanced collaboration because clear interaction modes and defined responsibilities give teams a better understanding of when and
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how to collaborate, creating a learning environment for ongoing improvement. Unsurprisingly, the Team Topologies approach has inspired companies to adopt similar patterns beyond pure software development, in search of the promised benefits. To what extent is the idea of adopting a number of
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recurring team types and predefined interaction models applicable beyond software? In principle, it seems plausible that any organization could benefit from the implementation of a similar framework. At the end of the day, certain teams will be focused on delivering specific elements of a value
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proposition (stream-aligned), while others can be focused on supporting with services or enabling with competencies. We hinted in a previous article that explored evolutions towards product-centric organizations that the shape of the particular team topology that your company could adopt heavily
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depends on the product and business model you’re delivering to the market and on your strategic focus. The archetypes of Stream-aligned teams, platform teams, and enabling teams hold extraordinarily well in different contexts. For example, the Dutch healthcare company Buurtzorg is, to some extent,
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characterized by similar archetypes with stream-aligned teams of nurses, supported by coaches (enabling) and using a particular software platform. While software companies have unique needs, like the need for long-term stream-aligned teams (which usually have ownership of a feature group or
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product) others may have different ones. For example, you may want your teams to be assigned to a certain workspaces for shorter times in different contexts. For example, in a consulting company, you may want your stream-aligned consulting teams learn from many verticals. In such a case, you may
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want to push them to work on different projects and different industries. Overall, we can conclude that adopting a team topology at your company may reduce cognitive load, enhance collaboration, and achieve better agility. The archetypes provided by the Team Topologies approach may be a good
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starting point. As Martin Fowler said once: “George Box neatly quipped: “all models are wrong, some are useful”. Thus Team Topologies is wrong: complex organizations cannot be simply broken down into just four kinds of teams and three kinds of interactions. But constraints like this are what makes
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a model useful. Team Topologies is a tool that impels people to evolve their organization into a more effective way of operating, one that allows stream-aligned teams to maximize their flow by lightening their cognitive load.” From Team to Unit Topologies In parallel to defining team types and
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interactions, in the last few years there has been a convergence towards thinking of organizations as platform-powered ecosystems of independent product units — a sort of “common protocol of organizing”. In such models, the organization is often dubbed as “product-centric” or
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“platform-organization”. It is made of autonomous, swarming, and collaborating customer-facing units often called “Micro-Enterprises”, powered by shared services. This is what we call a 3EO (an Entrepreneurial and Ecosystem-Enabling Organization) at Boundaryless — a framework that we formalized
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through our partnership with Haier Group and inspired by their “Rendanheyi” model. We argue that the evolution from functional to divisional, matrix, and then platform model is a natural consequence of organizational evolution. As we explained before: “Adopting a platform organizational model right
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now offers compelling advantages, given the current nature and evolution of markets. Today’s market landscape demands agility, responsiveness, and the capacity to continuously innovate while leveraging digital capabilities to create new products and services. The Platform organization model
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emphasizes the product/service as the most vital element: small units produce a particular product or service, while internal platforms specialize in enabling services for internal units and sometimes third parties. By creating clear, actionable interfaces, the platform organization is structured
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to easily integrate with external — not just internal — contributors, something that is vital to align with the growing importance of business ecosystems: often, the birth of an internal enabling platform is subsequently externalized to cater for the birth of a third-party ecosystem of partners.”
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Our framework provides something we could call a “unit topology” based on three fundamental unit types and three fundamental interaction modes between them. These three unit types are defined as follows: Micro-Enterprises (ME): these are autonomous organizational units that are focused on producing
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a complete value proposition serving either an external customer (so-called “user MEs) or another unit (so-called “node-MEs”) in a B2B mode; Shared Services Platforms (SSP): these are organizational units missionized to provide enabling services of all kinds that have the characteristic of being
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appealing to all MEs (a good example could be F&A); Industry Platforms (IP): organizational elements focused on investing capital across the portfolio of options — continuously seeding new MEs — to increase diversity and support entrepreneurial employees. In this platform organization, MEs and SSPs
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are evolutions of organizational “divisions”, and run their own Profit and Loss statements. They are mini-organizations within the larger system. These unit types interact based on three patterns, either multi-party contracts or org-wide agreements: Service Provisioning Contracts/Agreements: the
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typical relationship between MEs and Shared Services Platforms, this type of collaboration can take different shapes depending on the context, some companies prefer to “tax” all their units’ revenues to pay for shared services — in a pattern that would be similar to that of citizens paying taxes to
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states to receive basic services — while others push for continuous point-to-point negotiations between the SSPs and the MEs; EMC or Ecosystem Micro Community Contract: i.e. multi-party contracts that often includes external partners and dictates how units collaborate to create a more complex
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market outcome or customer scenario that requires a multi-unit/product collaboration and all parties to do their best. In this case, the market outcomes can be connected to how the earnings and results of the collaboration are distributed to the various parties; VAM or Value Adjustment Mechanism
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Contracts: i.e. an investment contract through which capital allocation units (normally the IPs) invest in new or existing MEs and — more rarely — SSPs negotiate resource allocation that would lead to certain market objectives. Once unlocked — such outcomes impact how much the wealth of the ME is
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transferred to the ME owners and employees. This can take the form of salary enhancements, independence in managing the unit’s revenues, or assignment of the unit’s virtual or real equity (in case the ME is a separated legal entity). As we explained in a piece titled “The Key Promises of
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Organizational Unbundling”, transitioning to a platform organization model can provide huge benefits. In this model, the organization is “unbundled” into the unit topology explained above (specifically the adoption of P&L bearing MEs and SSPs). These benefits include: reduced organizational
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brittleness as a consequence of a lighter integration across silos; reduced Time to Market and distance to the customer as a consequence of having all units focused on serving their customers more tightly; enhanced offering coherence especially if organizational unbundling is coupled with the
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creation of a portfolio strategy; an optimized cost structure due to the economies of scale that can be generated through the adoption of common Shared Services Platforms. In a platform organization, one can also expect much higher employee engagement, attraction & retention due to the deeper
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responsibilities that operators at the edge of the organizations have to “run their own business”. This can catalyze stronger innovation capabilities. The Platform Portfolio Deep Dive is a 1-Day Experience designed for executives and builders who want to learn how to manage an ecosystem of several
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products and services. Join us in Barcelona for the first Live Edition. Mixing and integrating everything In this article, we wanted to explain why adopting both a team topology and a unit topology may be useful for organizations seeking the benefits these approaches promise. To understand why the
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two things work well in a mix, let’s quickly look at the organizational context. Let’s assume we are looking to adopt a platform-organization (3EO) model. Our organization will be interacting with a larger market and will be “made of” units. Such units will be mainly focused on providing a certain
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product/service (either internally or externally focused). The context of the single units will be where we’ll possibly apply Profit & Loss responsibility, for entrepreneurial motivation, cost control, or both. Such units may start small, especially if we’re incubating new ones instead of
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“unbundling” an existing large organization into smaller elements, but typically grow as they become more successful. As a consequence of growth, the number of teams inside a Micro-Enterprise will grow as the product grows its customer base and revenues. We can identify at least four layers in the
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organizational context and scopes of interactions: inside a single team between teams inside a product unit (and potentially between units) between the Micro-Enterprises (nodes, units) in the organization (and potentially between organizations) among different organizations These “layers” lead to a
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series of “interfaces,” like the one between teams (in a unit) and units (in an organization). Teams within units could be interchanged or collaborate, and units between organizations can partner to achieve ambitious plans through partnerships. Organizations are also of course keen to partner and
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trade services and products. Adopting a team topology inside our organizational units would create the benefits mentioned above (reduction of cognitive load, and flow…). So, an organizational developer should look to create a team topology, not limiting it to the team types that existing frameworks
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provide, but rather creating its own. This recognizes that each business model and product specificities call for a different team topology. In an interpretation of Conway’s Law, we could say that you ship your team topology and thus your team topology should be influenced by the product/service
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the unit is providing. In an organization with different products, each product-focused Micro-Enterprise should probably have a slightly different team topology. Most likely, product units (MEs) will have some sort of stream-aligned team type related to the type of business they’re running and a
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series of platform and enabling teams. As an example, while in a software development unit: the stream-aligned teams may be managing a particular feature group or macro-function of the software product; the platform teams may be providing cross-feature functions (authentication, data management…);
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and an enabling team may deal with adopting new tools and techniques. In a consulting Micro-Entrepreneurial unit: the stream-aligned team may be set to be a team capable of delivering a certain part of the consulting offer; a platform team may provide some ultra-specialized consulting capability or
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training services; and an enabling team may be providing travel arrangements. As micro-entrepreneurial units grow, Stream-Aligned teams will diversify and acquire certain recurring types that may be useful to track. Recognizing them can optimize hiring, skilling, and tooling for the teams. All MEs
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should also likely have one team type specialized to be in charge of go-to-market. We advise organizations to empower their MEs to reach the market autonomously in order to respect the principles of letting autonomous units create their own success. However, it’s also true — as we explained
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recently in Responding to Multiple Customer Needs with Products and Services — that creating shared, org-wide go-to-market processes can provide a potential coherence pattern for companies. These processes can complement, integrate, or in some cases substitute the embedded go-to-market capabilities
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in the units. This is important for companies concerned with exposing a coherent brand and sales motion, for example, to avoid proposing sub-optimal solutions to customers. In this case, the shared GTM process can be powered by product taxonomies that can help compose bundles and upsell, by
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promoting the whole set of organizational capabilities. This process is a delicate and important element to design and ponder in a multi-product organization model. Basic enabling/platform services, like legal support, finance, and administration, should be easy to embed into a Shared Services
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Platform from the start. As products diversify, the organizational developer must monitor recurring enabling and platform team types that may emerge inside MEs. These types cater to be extracted and embedded into new SSPs to better serve multiple MEs. Facilitating inter-team and inter-unit dynamics
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will be crucial. EMC contracts are handy solutions for units to collaborate to create new market outcomes, new products, and new strategies. The inter-team collaboration beyond the unit’s boundaries will require more careful facilitation. Conclusions and Further Works In this article, we’ve
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explained the differences between unit and team topology. We’ve also explained why modern platform organizations adopting the two can provide benefits in team experience — such as reduced cognitive load and flow — and organizational success — such as innovation, market success, reduced
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organizational brittleness, broader optionality, and more. Despite past confusion and overlap, we provided a clear approach to applying both concepts, rooted in our first-hand experience with Fortune 500s, fast-growing companies, and scaleups. Given the pioneering nature of such frameworks, we
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believe this overview can help you navigate this complexity. As organizations grow more complex, the need for coordination and strategy beyond operations emerges. The strategy process must consider the whole system and learn from the market environment. In addition to team and unit structure, a
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modern organization will need to create a capability to strategically and collectively look at product-units development. It will also needs coordination capabilities that help avoid resource overbooking and planning capability to be able to commit to customers and partners. In the coming weeks,
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we’ll release more articles in this series explaining how we use the approaches explained today in combination with: tools and processes such as Wardley Maps and Ecosystem Maps to develop a collective Portfolio strategy; frameworks such as the Viable System Model and Flight Levels to develop
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control, intelligence, and planning capabilities across the various levels of the organization. Furthermore, we’re passionate about thinking beyond the organizational boundaries at Boundaryless. Soon, we’ll publish further updates about how our work with organizations is leading to ecosystemic
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partnerships and inter-organization collaborations, extending teams’ reach beyond the single organization. Before You Go! As you may know, everything we do is released in Creative Commons for you to use. In case you’re getting value out of these reads and tools, we encourage you to click the
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Organizational Design, Team Topologies, Platform Ecosystem, Ecosystem, Micro Enterprise.
👏button and hold down to 20–50 claps as this will help us to get more exposure, and hopefully, work more on developing these tools. Subscribe to our newsletter here Subscribe to our podcast on YouTube or on any other platform you use Join our telegram broadcast channel where we publish all our
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medium
| 5,833 |
Responsible Spending, Money Emotions, Personal Finance.
The other day I was singing to myself in my microapartment — everyone who lives alone continually sings to themselves like life was their own badly-written musical, right? — and the lyrics went something like “Why do they let me write about personal finance when I break all of the personal finance
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medium
| 5,835 |
Responsible Spending, Money Emotions, Personal Finance.
rules?” So let’s look at some of the terrible financial decisions I’ve made, say, in the past month. 1. Bought a $923.15 round-trip ticket to fly and visit my parents in Iowa for Christmas I feel like there’s nobody in the entire country who is paying more for a single ticket to a location within
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medium
| 5,836 |
Responsible Spending, Money Emotions, Personal Finance.
the contiguous United States than I am. And $923 is a bargain compared to last year, when the ticket cost $1,179. The heartless and prudent thing to do would have been to say “Sorry, parents, Christmas is cancelled. Let’s all sit in our respective homes and spend the holiday staring at the wall.”
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medium
| 5,837 |
Responsible Spending, Money Emotions, Personal Finance.
You see all the advice columnists handing around this “get out of plane free” card this time of year: If you can’t afford to travel on the holidays, don’t do it. But what is the definition of can’t afford? I had enough in my checking account to buy the ticket, so I could afford it. That’s what
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medium
| 5,838 |
Responsible Spending, Money Emotions, Personal Finance.
“afford” means, right? The rest will all sort itself out eventually. You spend some, you earn some, you get to see your folks on Christmas, your “emergency fund” gets a little smaller, you save more money a few months down the line, everything works out. (All together now: “Why do they let me write
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medium
| 5,839 |
Responsible Spending, Money Emotions, Personal Finance.
about personal finance…”) 2. Put a $2,524 cruise payment on my Ann Taylor Loft Mastercard Ha ha ha ha ha ha ha ha ha look at my emotions getting in the way of good common sense. I’ll tell you honestly that I didn’t make this decision lightly. I wasn’t going to go on this cruise at all; I was going
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medium
| 5,840 |
Responsible Spending, Money Emotions, Personal Finance.
to focus on paying off my debt in 2015. I was going to do the Right Thing. And then I had one of those Life Circumstance Changes that meant I needed to get my butt on that boat, and then it was all “okay, I have to book my cabin tonight or I won’t be able to go at all.” So I put it on my
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medium
| 5,841 |
Responsible Spending, Money Emotions, Personal Finance.
high-interest Ann Taylor Loft Mastercard oh why why why do they let me write about personal finance (this is the part of the song where it goes into the super-emotional bridge). I have every reason to believe that I can pay this off in three months. I did the math. I’m going to be making more money
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medium
| 5,842 |
Responsible Spending, Money Emotions, Personal Finance.
in 2015 than I am in 2014, because I had the Rates Conversation with most of my clients, and my rates are going up without me having to find new gigs. (This is the optimal way to freelance.) But I know, deep in my heart, that I could also accidentally get hit by a car (again). Something could
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medium
| 5,843 |
Responsible Spending, Money Emotions, Personal Finance.
happen to prevent me from paying the credit card off, and then the interest could spiral up like an ever-ascending Escher staircase. 3. Procrastinated on taking action on a credit card offer from my bank that gives me 0% APR for a year I have been telling myself that 2015 will be the year I pay off
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medium
| 5,844 |
Responsible Spending, Money Emotions, Personal Finance.
all my debt, even though I ended 2014 by adding more debt to my debt, which I then justified by comparing myself to Pomplamoose. And then, a week ago, I got this credit card offer — I mean, good gracious, I get gobs of these, like everyone else — but this one was different. It was from my bank
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medium
| 5,845 |
Responsible Spending, Money Emotions, Personal Finance.
(Capital One) and it wants to give me 12 months of 0% APR with a 3% balance transfer fee and a 12.9–22.9% variable APR after the year is out. (Also, 1.5% cash back.) Look, I know that 12.9–22.9% APR is terrible. That’s higher interest than I’m paying on any of my debt except, of course, the
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medium
| 5,846 |
Responsible Spending, Money Emotions, Personal Finance.
high-interest Ann Taylor Loft Mastercard. But I feel the challenge beckoning. I could pay it all off IN A YEAR with 0% interest. Why am I procrastinating on this? Ideally, I’d want to apply and get approved before that cruise payment shows up on the Mastercard I was working on paying off. I think
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medium
| 5,847 |
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