🧮ERP: Implementations (Part XVIII: The Price of Partnership)

ERP Implementations Series

When one proceeds on a journey, it’s important to have a precise destination, a good map to show the road, the obstacles ahead, and help to plan the journey, good gear, enough resources to make it through the journey, but probably more important, good companions and ideally guides who can show the way ahead and offer advice when needed. This is in theory the role of a partner, and on such coordinates should be a partnership based upon. However, unless the partners pay for the journey as well, the partnership can come with important costs and occasionally more overhead than needed. 

The traveler’s metaphor is well suited to ERP implementations and probably many other projects in which the customer doesn’t have the knowledge about the various aspects of the project. The role of a partner is thus multifold, and it takes time for all the areas to be addressed. Typically, it takes years for such a relationship to mature to the degree that it all develops naturally, at least in theory. Conversely, few relationships can resist in time given the complex challenges resulting from different goals and objectives, business models, lack of successes or benefits.

Usually, a partnership means sharing the risks and successes, but more importantly, building a beneficial bidirectional relationship from which all parties can profit. This usually means that the partner provides a range of services not available in-house, allowing customers to pull resources and knowledge on a need-by basis, providing direction and other advice whenever is needed. A partner can help if it has the needed insight into the business, and this implies a minimum of communication in respect to business decisions, strategies, goals, objectives, requirements, implications, etc. 

During sales pitches and other meetings, many service providers assume themselves the role of partners, however between their behavior and the partner role is usually a considerable gap that often may seem impossible if not difficult to bridge. It’s helpful to define as part of the various contracts the role of partnership, respectively the further implications. It’s helpful to have a structure of bidirectional bonuses and other benefits that would help to strengthen the bond between organizations. A framework for supporting the partnership must be built, and this takes time to be implemented adequately. 

Even if some consultants are available from the early stages of the partnership, that’s typically the exception and not the norm. It’s typical for resources to be involved only for the whole duration of a project or less. Independently of their performance, the replacement of resources in projects is unavoidable and must be addressed adequately, with knowledge transfer and all that belongs to such situations. Moreover, it needs to be managed adequately by the serve provider, however resources can’t be replaced as the parts of an engine. The planning and other activities must consider and accommodate such changes.

Also the replacement of partners in mid of the project is possible and this option should be considered as exception in projects and planned accordingly. The costs of working with partners can be high and therefore organizations should consider the alternatives. Bringing individual resources in projects and even building long-term relationships with them can prove to be a cost-effective alternative. Even if such partnerships are more challenging to manage, the model can offer other advantages that compensate for the overhead of managing them.

Outsourcing resources across geographies or mixing models can work as well. Even if implementations usually don’t allow for experiments, they can still be a feasible alternative. The past successes and failures are usually a good measure of what works and doesn't for organizations. 

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🧮ERP: Implementations (Part XVI: It’s All About Politics)

ERP Implementations Series

An ERP implementation takes place within a political context and politics can make or break implementations. Politics occurs whenever individuals or organization groups interact to make decisions that affect parts or the whole organization. Besides decision-making there are further components that revolve around the various types of resources allocation and management, resulting in power dynamics that shape and pull organizations in politically charged directions.

Given the deep implications of ERP systems, probably in no other type of projects the political aspects are that visible and stringent to all employees to the degree that they pull decisions in one direction independently of the actual requirements. It may seem incredible, though there are cases in which ERP systems were selected just because the organization’s CEO played golf with the vendor’s CEO. In the end, the gaps between systems should be minimal nowadays, at least in theory, isn’t it?

Of course, just because one meets certain strange behaviors, it doesn’t mean that this is common practice! There are higher chances of selecting an inadequate system just because the sales representative did a good job and convinced the audience that the system can do anything they want. It probably does if coins are used for each missing feature, and in the long term it can be a lot of coins. Conversely, even if a system satisfies nowadays’ requirements, it doesn’t mean it will continue to do the same with future requirements. Only the future can tell whether the choice of a system over the others was a good one.

The bigger the gaps between the various interests, the more difficult it becomes to pull the project in the right direction. Probably the best way to demonstrate why one system is better than another is by bringing facts and focusing on the main requirements of the organization. This supposes the existence of an explicit list of requirements with a high-level description of how they can be addressed by the future system. This might not be enough, though it’s a good start, a good basis for discussion, for making people aware of the implications. However, doing this exercise for 2-3 or more systems is not cost effective, as such analysis can become time-consuming and expensive.

One way to address political resistance is by discussing openly with the stakeholders and addressing their concerns, arguing why the system is a good choice, what can be done to address the gaps, and so on. It will not always be enough, though it’s important to establish common ground for further discussions. Further on, it’s important to keep the same openness and disposition for communication given that the further the project progresses, the higher the likelihood of other concerns to appear. It’s a never-ending story if there are gaps between needs and what the system provides.

It's important to establish clear and honest communication with the stakeholders, informing them proactively about the challenges faced, independently in which area they are faced. Conversely, too much communication can be disruptive and can create other challenges. One way to cope with this is by identifying the communication needs of each stakeholder and trying to identify what’s the volume of information, respectively the communication needs of each of them. That’s project management 1:1.

The Project Manager and his team should ideally anticipate and address the potential conflicts timely, before they propagate and reach a broader audience. It’s questionable how much can be achieved proactively, especially when the project keeps everybody busy. The tendency is to answer politics with politics, though brainstorming sessions, open communication and a few other approaches can reach deeper where politics can’t.

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🧮ERP: Implementations (Part XIV: A Never-Ending Story)

ERP Implementations Series

An ERP implementation is occasionally considered as a one-time endeavor after which an organization will live happily ever after. In an ideal world that would be true, though the work never stops – things that were carved out from the implementation, optimizations, new features, new regulations, new requirements, integration with other systems, etc. An implementation is thus just the beginning from what it comes and it's essential to get the foundation right – and that’s the purpose of the ERP implementation – provide a foundation on which something bigger and solid can be erected. 

No matter how well an ERP implementation is managed and executed, respectively how well people work towards the same goals, there’s always something forgotten or carved out from the initial project. Usually, the casual suspects are the integrations with other systems, though there can be also minor or even bigger features that are planned to be addressed later, if the implementation hasn’t consumed already all the financial resources available, as it's usually the case. Some of the topics can be addressed as Change Requests or consolidated on projects of their own. 

Even simple integrations can become complex when the processes are poorly designed, and that typically happens more often than people think. It’s not necessarily about the lack of skillset or about the technologies used, but about the degree to which the processes can work in a loosely coupled interconnected manner. Even unidirectional integrations can raise challenges, though everything increases in complexity when the flow of data is bidirectional. Moreover, the complexity increases with each system added to the overall architecture. 

Like a sculpture’s manual creation, processes in an ERP implementation form a skeleton that needs chiseling and smoothing until the form reaches the desired optimized shape. However, optimization is not a one-time attempt but a continuous work of exploring what is achievable, what works, what is optimal. Sometimes optimization is an exact science, while other times it’s about (scientifical) experimentation in which theory, ideas and investments are put to good use. However, experimentation tends to be expensive at least in terms of time and effort, and probably these are the main reasons why some organizations don’t even attempt that – or maybe it’s just laziness, pure indifference or self-preservation. In fact, why change something that already works?

Typically, software manufacturers make available new releases on a periodic basis as part of their planning for growth and of attracting more businesses. Each release that touches used functionality typically needs proper evaluation, testing and whatever organizations consider as important as part of the release management process. Ideally, everything should go smoothly though life never ceases to surprise and even a minor release can have an important impact when earlier critical functionality stopped working. Test automation and other practices can make an important difference for organizations, though these require additional effort and investments that usually pay off when done right. 

Regulations and other similar requirements must be addressed as they can involve penalties or other risks that are usually worth avoiding. Ideally such requirements should be supported by design, though even then a certain volume of work is involved. Moreover, the business context can change unexpectedly, and further requirements need to be considered eventually. 

The work on an ERP system and the infrastructure built around it is a never-ending story. Therefore, organizations must have not only the resources for the initial project, but also what comes after that. Of course, some work can be performed manually, some requirements can be delayed, some risks can be assumed, though the value of an ERP system increases with its extended usage, at least in theory. 

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🧮ERP: Implementations (Part XIII: On Project Management)

ERP Implementations Series

Given its intrinsic complexity and extended implications, an ERP implementation can be considered as the real test of endurance for a Project Manager, respectively the team managed. Such projects typically deal with multiple internal and external parties with various interests in the outcomes of the project. Moreover, such projects involve multiple technologies, systems, and even methodologies. But, more importantly, such projects tend to have specific characteristics associated with their mass, being challenging to manage within the predefined constraints: time, scope, costs and quality.

From a Project Manager’s perspective what counts is only the current project. From a PMO perspective, one project, independent of its type, must be put within the broader perspective, while looking at the synergies and other important aspects that can help the organization. Unfortunately, for many organizations all begins and ends with the implementation, and this independently of the outcomes of the project. Often failure lurks in the background and usually there can be small differences that in the long term have a considerable impact. ERP implementations are more than other projects sensitive on the initial conditions – the premises under which the project starts and progresses. 

One way of coping with this inherent complexity is to split projects into several phases considered as projects or subprojects in their own boundaries. This allows organizations to narrow the focus and split the overall work into more manageable pieces, reducing to some degree the risks while learning in the process about organization’s capabilities in addressing the various aspects. Conversely, the phases are not necessarily sequential but often must overlap to better manage the resources and minimize waste. 

Given that an implementation project can take years, it’s normal for people to come and go, some taking over work from colleagues, with or without knowledge transfer. The knowledge is available further on, as long as the resources don’t leave the organization, though knowledge transfer can’t be taken for granted. It’s also normal for resources to suddenly not be available or disappear, increasing the burden that needs to be shifted on others’ shoulders. There’s seldom a project without such events and one needs to make the best of each situation, even if several tries and iterations are needed in the process.

Somebody needs to manage all this, and the weight of the whole project falls on a PM’s shoulders. Managing by exception and other management principles break under the weight of implementation projects and often it’s challenging to make progress without addressing this. Fortunately, PMs can shift the burden on Key Users and other parties involved in the project. Splitting a project in subprojects can help set boundaries even if more management could occasionally be involved. Also having clear responsibilities and resources who can take over the burdens when needed can be a sign of maturity of the teams, respectively the organization. 

Teams in Project Management are often compared with teams in sports, though the metaphor is partially right when each party has a ball to play with, while some of the players or even teams prefer to play alone at their own pace. It takes time to build effective teams that play well together, and the team spirit or other similar concepts can't fill all the gaps existing in organizations! Training in team sports has certain characteristics that must be mirrored in organizations to allow for teams to improve. Various parties expect from the PM to be the binder and troubleshooter of something that should have been part of an organization’s DNA! Bringing external players to do the heavy lifting may sometimes work, though who’ll do the lifting after the respective resources are gone? 

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🧮ERP: Implementations (Part XI: Tales from the Crypt)

ERP Implementations Series

One can seldom meet more frighteningly strange stories than the ones told by people who worked in ERP implementations. Such projects attempt to model an organization’s main functions and processes, independently on whether the focus is on production, finance, supply chain, services, projects or human resources. Because they tend to touch all important aspects of a business, such projects become so complex and political that they are often challenging to manage and occasionally are predestined to failure by design.

For the ones who never participated in an ERP implementation, imagine an average project and the number of challenges associated with it, and multiply it by 10 or a similar number that reflects the increase in complexity with the translation to broader scales. The jump in complexity can be compared with the jump from putting together a bed after a scheme to building a whole house using the same level of detail. The scale can further increase by moving from a house to a whole building or a complex of residential houses. Even if that’s technically achievable, a further challenge is how to build all this in a short amount of time, with minimal costs and acceptable quality levels.

With the increase of scale, imagine the amount of planning and coordination that needs to be achieved to avoid any delays. Even if many plan with the "first-time right" objective in mind, inherent issues are often unavoidable, and an organization’s agility can be measured on how robustly it can handle the foreseeable and unforeseeable challenges altogether. Of course, there are many approaches that allow one to minimize, defer or share the risks, or even opportunities, though there’s usually an important gap between one’s planning and reality!

This doesn’t mean that such projects are unmanageable! Everything can be managed to some level of detail and within some tolerance margins, however many organizations are tempted to answer complexity with complexity, and that’s seldom the right approach! Ideally, complexity should be broken down to manageable parts, though that’s challenging to do when one doesn’t know what is being done. That’s why many organizations search for partners with which to share the risks and success, though that works if the customer, and its partners can stir the same ship toward common destinations, at least for the main itinerary if not for the whole duration of the trip.  

Unfortunately, as happens in partnerships that diverge toward distinct goals, the misalignment and other similar factors resulting from this divergence can lead to further challenges that increase the complexity of ERP implementations even more. Ideally, a partner should behave like the mechanics at a pitstop, though that’s utopic especially when they must be always available and this for the whole duration of the project. So, all parties need to compromise somehow, and, even if there are general recipes that can be used, it’s challenging to make everybody happy!

Often in an ERP implementation is defined from the beginning whose needs are the most important, and from there one can build a whole hierarchy of scenarios, models and analyses that should show the right path(s). There’s a lot of knowledge that can be carried out between projects, respectively, between the different phases of a project, though there will always be surprises and one should be prepared for them! Same as the captain must occasionally change the course to avoid or fight storms or other hazards, so must the corresponding structure act when this is the case! Occasionally, each team member may be in the position to act like a captain and raise to expectations, though project designs must allow for this!

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🧭Business Intelligence: Why Data Projects Fail to Deliver Real-Life Impact (Part IV: Making It in the Statistics)

Business Intelligence Series

Various sources (e.g., [1], [2], [3]) advance the failure rates for data projects somewhere between 70% and 85%, rates which are a bit higher than the failure of standard projects estimated at 60-75% but not by much. This means that only 2-3 out of 10 projects will succeed and that’s another reason to plan for failure, respectively embrace the failure. 

Unfortunately, the statistics advanced on project failure have no solid fundament and should be regarded with circumspection as long the methodology and information about the population used for the estimates aren’t shared, though they do reflect an important point – many data projects do fail! It would be foolish to think that your project will not fail just because you’re a big company, and you have the best resources, and you have a proven rate of success, and you took all the precautions for the project not to fail.

Usually at the end of a project the team meets together to document the lessons learned in the hope that the next projects will benefit from them. The team did learn something, though as the practice shows even if the team managed to avoid some issues, other issues will impact the next similar project, leading to similar variances. One can summarize this as "on the average the impact of new issues and avoided known issues tends to zero out" or "on average, the plusses and minuses balance each other across projects". It’s probably a question of focus – if organizations focus too much on certain aspects, other aspects are ignored and/or unseen. 

So, your first data project will more likely fail. The question is: what do you do about it? It’s important to be aware of why projects and data projects fail, though starting to consider and monitor each possible issue can prove to be ineffective. One can, however, create a risk register from the list and estimate the rates for each of the potential failures, respectively focus on only the top 3-5 which have the highest risk. Of course, one should reevaluate the estimates on a regular basis though that’s Risk Management 101. 

Besides this, one should focus on how the team can make the project succeed. When adopting a technology, methodology or set of processes, it’s recommended to start with a proof-of-concept (PoC). To make the PoC a helpful experience it’s probably important to start with a topic that’s not too big to handle, but that also involves some complexity that would allow the organization to evaluate the targeted set of tools and technologies. It can also be a topic for which other organizations have made important progress, respectively succeed. The temptation is big to approach the most stringent issues in the organization, respectively to build something big that can have an enormous impact for the organization. Jumping too soon into such topics can just increase the chances of failure. 

One can also formulate the goals, objectives and further requirements in a form that allows the organization to build upon them even if the project fails. A PoC is about learning, building a foundation, doing the groundwork, exploring, mapping the unknown, and identifying what's still missing to make progress, respectively closing the full circle. A PoC is less about overachievement and a big impact, which can happen, though is a consequence of the good work done in the PoC. 

The bottom line, no matter whether you succeed or fail, once you start a project, you’ll still make it in the statistics! More important is what you’ve learnt after the first data project, respectively how you can use the respective knowledge in further projects to make a difference!

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References:
[1] Harvard Business Review (2023) Keep Your AI Projects on Track, by Iavor Bojinov (link)
[2] Cognilytica (2023) The Shocking Truth: 70-80% of AI Projects Fail! (link)
[3] VentureBeat (2019) Why do 87% of data science projects never make it into production? (link)

💫⚙️ERP Systems: Microsoft Dynamics 365's Invoice Capture (Some Thoughts to Start with)

Introduction

It's almost the year end and it's time for reviewing what went good and not that good during the year. On the "successful projects" list I can put the Invoice Capture implementation. I wrote on a previous post a short review on what the feature is about.

I had the chance of configuring Invoice Capture for Cost invoices (invoices without Purchase orders) while it was still in public preview, and we went live soon after the feature became generally available. The implementation had its challenges though in the end it was a positive experience, learning a lot from my colleagues, from Microsoft, other consultants and business users who embarked on the same journey. 

Where to Start?

Usually, it's a good idea to start with the documentation and the standard training material, which provides a good overview of what Invoice Capture is about, the steps needed for configuration, the processes involved, permissions, etc. 

You should check also the "Invoice Capture for Dynamics 365" group on Yammer (aka Viva) because besides the latest version of the Implementation Guide document are published in there also the Release notes and training videos associated with them, to which other users provide (lot of) feedback and questions. Some information is first available in the group and much later made available in the documentation. If you're facing an error or a challenge, more likely there's a conversation in there and the answer you're looking for. Otherwise, you can start a thread and the others will try to help. At least until now, Microsoft was quite active in helping.

Via FastTrack, Microsoft provided several sessions in Dec-2022 (preview) and Sep-2023 (GA) that can be used to get a good overview about the feature and its implementation. Frankly, I would start with the last session and then explore the other resources. In the process I found useful several other resources, mainly YouTube content - see Dan's Corner (link), DAFTD365 - and LinkedIn - see Hendrik M Larsen's posts, 

You might want to also check the Release planner for Finance, to see what features are in the pipeline, respectively the Ideas for Dynamics to get an idea what kind of improvements others wish for. 

In parallel, one can start sketching the "AS IS" and "TO BE" processes, and eventually put together a business case for using Invoice Capture to digitize the processing of Vendor invoices. This isn't a simple Change request, therefore it makes sense to start a project, though its scope is relatively small. 

Bridging the Gap

One can look at the "TO BE" process based on the functionality provided, respectively planned by Microsoft for Invoice capture, or look at the broader picture and sketch how an ideal digitized process should look like. If the gap between the two pictures is big, then might be a good idea to look at alternatives, which anyway should be done as part of the business case. There might be third-party tools out there (e,g, ExFlow) which provide similar functionality, however on the long term it makes sense to go with Microsoft, even if the full extent of the functionality might be not available. 

A review of other tools might be good - to understand how the ISV's approached the integration, what kind of features they provide, respectively whether the ideal digitized process makes sense. Conversely, this will imply more effort.

The current version of Invoice capture provides a good basis to build upon. One can use Power Apps or Power Automate to address some of the gaps, some gaps can be discussed with Microsoft and stress their importance, while other gaps are maybe not that important and can be dismissed. One way or another one must be ready to compromise as long as this doesn't have an important impact on the business. 

The Project

The scope of the project might be relatively small, though one should follow the best practices of Project Management and make sure that all important stakeholders are involved, that the right resources are available when needed, manage the requirements adequately, assure that the changes are adequately tested, that the users are trained, the process documented, etc.

It's important to understand that the simple configuration of Invoice capture will not be the end of the effort. As Microsoft will release further features directly and indirectly related to Invoice Capture, additional effort might be involved after the implementation went live to address the gaps, opportunities, as well as the risks. Moreover, Invoice Capture requires a learning curve; addressing the lessons learned might involve further changes in the system's setup as well in data's management. Therefore, further effort must be planned accordingly. 

Even if we talk about a full implementation or the implementation of a feature, the overall success tends to be more dependent on how the implementation is approached than on the technology involved. 

Closing Thoughts

Some of the points made here can be applied to similar feature implementations. Overall, it's important to gather enough information to start the project and in time to reach the level of depth required by it. Don't expect for things to be perfect, start small and evolve, prioritize, cover the gaps, optimize!

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Notes: Team Data Science Process (TDSP)

Team Data Science Process (TDSP)
• an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently [1]
• {goal} help customers fully realize the benefits of their analytics program [1]
• {component} data science lifecycle definition
  • {description} a framework to structure the development of data science projects [1]
  • {goal} designed for data science projects that ship as part of intelligent applications that deploy ML & AI models for predictive analytics [1]
  • {benefit} can be used in the context of other DM methodologies as they have common ground [1]
    • e.g. CRISP-DM, KDD
  • {benefit} exploratory data science projects or improvised analytics projects can also benefit from using this process [1]
• {component} standardized project structure
  • {description} a directory structure that includes templates for project documents
    • ⇒makes it easy for team members to find information [1]
    • ⇐templates for the folder structure and required documents are provided in standard locations [1]
    • all code and documents are stored in an agile VCS tracking repository [1]
      • {recommendation} create a separate repository for each project on the VCS for versioning, information security, and collaboration [1]
  • {benefit} organizes the code for the various activities [1]
  • {benefit} allows tracking the progress [1]
  • {benefit} provides checklist with key questions for each project to guarantee process and deliverables’ quality [1]
  • {benefit} enables team collaboration [1]
  • {benefit} allows closer tracking of the code for individual features [1]
  • {benefit} enables teams to obtain better cost estimates [1]
  • {benefit} helps build institutional knowledge across the organization [1]
• {component} recommended infrastructure
  • {description} a set of recommendations for the infrastructure and resources needed for analytics and storage [1]
  • {benefit} addresses cloud and/or on-premises requirements [1]
  • {benefit} enables reproducible analysis [1]
  • {benefit} avoids infrastructure duplication [1]
    • ⇒minimizes inconsistencies and unnecessary infrastructure costs [1]
  • {tools} tools are provided to provision the shared resources, track them, and allow each team member to connect to those resources securely [1]
  • {good practice} create a consistent compute environment [1]
    • ⇐allows team members replicate and validate experiments [1]
• {component} recommended tools and utilities
  • {description} a set of recommendations for the tools and utilities needed for project’s execution [1]
  • {benefit} help lower the barriers and increase the consistency of their adoption [1]
  • {benefit} provides an initial set of tools and scripts to jump-start methodology’s adoption [1]
  • {benefit} helps automate some of the common tasks in the data science lifecycle [1]
    • e.g. data exploration and baseline modeling [1]
  • {benefit} well-defined structure provided for individuals to contribute shared tools and utilities into their team's shared code repository [1]
    • ⇐ resources can then be leveraged by other projects [1]
• {phase} 1: business understanding
  • {goal} define and document the business problem, its objectives, the needed attributes, and the metric(s) used to determine project’s success
  • {goal} identify and document the relevant data sources
  • {step} 1.1: define project’s objectives
    • elicit together with the stakeholders the requirements, define and document the problem and its objectives, respectively the metric(s) used to determine project’s success
      • requires a good understanding of the business processes, data and further characteristics
  • {step} 1.2: identify data sources
    • identify the attributes and the data sources relevant to the problem under study
  • {step} 1.3: define project plan and team*
    • develop a high-level milestone plan and identify the resources needed for executing it
  • {tool} project charter
    • standard template that documents the business problem, the scope of the project, the business objectives and metric(s) used to determine project’s success
• {phase} 2: data acquisition & understanding
  • {goal} prepare the base dataset(s) as needed by the modeling phase into the target repository
  • {goal} build the data ETL/ELT architecture and processes needed for provisioning the basis data
  • {step} 2.1: ingest data
    • make the required data available for the team in the repository where the analytics operations take place
  • {step} 2.2: explore data
    • understand data’s characteristics by leveraging specific tools (visualization, analysis)
    • prepare the data as needed for further processing
  • {step} 2.3: set up pipelines
    • build the pipelines needed for data actualization and qualitative assessment [3]
    • set up a process to score new data or refresh the data regularly [3]
  • {step} 2.4: feasibility analysis*
    • reevaluate the project to determine whether the value expected is sufficient to continue pursuing it
  • {tool} data quality report
    • report that includes data summaries, data mappings, variable ranking, data qualitative assessment(s) and further information [3]
  • {tool} solution architecture
    • diagram and/or textual-based description of the data pipeline(s), technical assumptions and further aspects
  • {tool} data reports
    • document the structure and statistics of the raw data
  • {tool} checkpoint decision
    • decision template document that
      • summarizes the findings of the feasibility analysis step
      • includes a set of choices and recommendations for the next steps
      • serves as basis for the decision on whether to continue or not the project, respectively what the next steps are
• {phase} 3: modeling
  • {goal} create a machine-learning model that addresses the prediction requirements and that's suitable for production
  • {step} 3.1: feature engineering
    • the inclusion, aggregation, and transformation of raw variables to create the features used in the analysis [4]
      • ⇐requires a good understanding of how the features relate to each other and how the ML algorithms use those features [4]
  • {step} 3.2: model selection*
    • choose one or more modeling algorithms that address problem’s characteristics the best
  • {step} 3.3: model training
    • involves the following steps:
      • split the input data into training and test datasets
      • build the models by using the training dataset
      • evaluate the training and the test data set
      • determine the optimal setup and methods
  • {step} 3.4: model evaluation
    • evaluate the performance of the model(s)
  • {step} 3.5: feasibility analysis*
    • evaluate the readiness of the models for use into production, respectively on whether they fulfill project’s objectives
  • {tool} feature sets
    • describe the features developed for the modeling and how they were generated
    • contains pointers to the code used to generate the features
  • {tool} model report
    • a standard, template-based report that provides details on each experiment’s outcomes
    • created for each model tried
  • {tool} checkpoint decision
  • {tool} model performance metrics
    • e.g. ROC curves or MSE
• {phase} 4: deployment
  • {goal} deploy the models and the data pipelines to the environment used for final user acceptance
  • {step} 4.1: operationalize architecture
    • prepare the models and data pipelines for use into production
    • {best practice} expose the models over an open API interface
      • enables models’ consumption from various applications
    • {best practice} build telemetry and monitoring into the models and the data pipelines [5]
      • helps in monitoring and troubleshooting [5]
  • {step} 4.2: deploy solution*
    • deploy the architecture into production
  • {tool} status dashboard
    • displays data on system’s health and key metrics
  • {tool} model report
    • the report in its final form with deployment information
  • {tool} solution architecture
    • the document in its final form
• {phase} 5: customer acceptance
  • {goal} confirm that project’s objectives were fulfilled and get customer’s acceptance
  • {step} 5.1: system validation
    • validate system’s performance and outcomes and confirm that it fulfills customer’s needs
  • {step} 5.2: project signoff*
    • finalize and review documentation
    • handover the solution and afferent documentation to customer
    • evaluate the project against the defined objectives and get customer’ signoff
  • {tool} exit report
  • {tool} technical report
    • contains all the details of the project that are useful for learning about how to operate the system [6]

Acronyms:

Artificial Intelligence (AI)

Cross-Industry Standard Process for Data Mining (CRISP-DM)

Data Mining (DM)

Knowledge Discovery in Databases (KDD)

Team Data Science Process (TDSP) 

Version Control System (VCS)

Visual Studio Team Services (VSTS)

Resources:

[1] Microsoft Azure (2020) What is the Team Data Science Process? [source]

[2] Microsoft Azure (2020) The business understanding stage of the Team Data Science Process lifecycle [source]

[3] Microsoft Azure (2020) Data acquisition and understanding stage of the Team Data Science Process [source]

[4] Microsoft Azure (2020) Modeling stage of the Team Data Science Process lifecycle [source] 

[5] Microsoft Azure (2020) Deployment stage of the Team Data Science Process lifecycle [source]

[6] Microsoft Azure (2020) Customer acceptance stage of the Team Data Science Process lifecycle [source]

🧭Business Intelligence: New Technologies, Old Challenges (Part I: An Introduction)

Each important technology has the potential of creating divides between the specialists from a given field. This aspect is more suggestive in the data-driven fields like BI/Analytics or Data Warehousing. The data professionals (engineers, scientists, analysts, developers) skilled only in the new wave of technologies tend to disregard the role played by the former technologies and their role in the data landscape. The argumentation for such behavior is rooted in the belief that a new technology is better and can solve any problem better than previous technologies did. It’s a kind of mirage professionals and customers can easily fall under.

Being bigger, faster, having new functionality, doesn’t make a tool the best choice by default. The choice must be rooted in the problem to be solved and the set of requirements it comes with. Just because a vibratory rammer is a new technology, is faster and has more power in applying pressure, this doesn’t mean that it will replace a hammer. Where a certain type of power is needed the vibratory rammer might be the best tool, while for situations in which a minimum of power and probably more precision is needed, like driving in a nail, then an adequately sized hammer will prove to be a better choice.

A technology is to be used in certain (business/technological) contexts, and even if contexts often overlap, the further details (aka requirements) should lead to the proper use of tools. It’s in a professional’s duties to be able to differentiate between contexts, requirements and the capabilities of the tools appropriate for each context. In this resides partially a professional’s mastery over its field of work and of providing adequate solutions for customers’ needs. Especially in IT, it’s not enough to master the new tools but also have an understanding about preceding tools, usage contexts, capabilities and challenges.

From an historical perspective each tool appeared to fill a demand, and even if maybe it didn’t manage to fill it adequately, the experience obtained can prove to be valuable in one way or another. Otherwise, one risks reinventing the wheel, or more dangerously, repeating the failures of the past. Each new technology seems to provide a deja-vu from this perspective.

Moreover, a new technology provides new opportunities and requires maybe to change our way of thinking in respect to how the technology is used and the processes or techniques associated with it. Knowledge of the past technologies help identifying such opportunities easier. How a tool is used is also a matter of skills, while its appropriate use and adoption implies an inherent learning curve. Having previous experience with similar tools tends to reduce the learning curve considerably, though hands-on learning is still necessary, and appropriate learning materials or tutoring is upon case needed for a smoother transition.

In what concerns the implementation of mature technologies, most of the challenges were seldom the technologies themselves but of non-technical nature, ranging from the poor understanding/knowledge about the tools, their role and the implications they have for an organization, to an organization’s maturity in leading projects. Even the most-advanced technology can fail in the hands of non-experts. Experience can’t be judged based only on the years spent in the field or the number of projects one worked on, but on the understanding acquired about implementation and usage’s challenges. These latter aspects seem to be widely ignored, even if it can make the difference between success and failure in a technology’s implementation.

Ultimately, each technology is appropriate in certain contexts and a new technology doesn’t necessarily make another obsolete, at least not until the old contexts become obsolete.

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💼Project Management: Methodologies (Part II: Agile Manifesto Reloaded II - Requirements Management)

Independently of its scope and the methodology used, each software development project is made of the same blocks/phases arranged eventually differently. It starts with Requirements Managements (RM) subprocesses in which the functional and non-functional requirements are gathered, consolidated, prioritized and brought to a form which facilitates their understanding and estimation. It’s an iterative process as there can be overlapping in functionality, requirements that don’t bring any significant benefit when compared with the investment, respectively new aspects are discovered during the internal discussions or with the implementer.

As output of this phase, it’s important having a list of requirements that reflect customer’s needs in respect to the product(s) to be implemented. Once frozen, the list defines project’s scope and is used for estimating the costs, sketching a draft of the final solution, respectively of reaching a contractual agreement with the implementer. Ideally the set of requirements should be completed and be coherent while reflecting customer’s needs. It allows thus in theory to agree upon costs as well about an architecture and other important aspects (responsibilities/accountability).

Typically, each new requirement considered after this stage needs to go through a Change Management (CM) process in which it gets formulated to the needed level of detail, a cost, effort and impact analysis is performed, respectively the budget for it is approved or the change gets rejected. Ideally small changes can be considered as part of a buffer budget upfront, however in the end each change comes with a cost and project delays.

Some changes can come late in the project and can have an important impact on the whole architecture when important aspects were missed upfront. Moreover, when the number of changes goes beyond a certain limit it can lead to what is known as scope creep, with important consequences on project’s costs, timeline and quality. Therefore, to minimize the impact on the project, the number of changes needs to be kept to a minimum, typically considering only the critical changes, while the others can be still implemented after project’s end.

The agile manifesto’s principles impose an important constraint on the requirements - changing requirements is a good practice even late in the process – an assumption - best requirements emerge from self-organizing teams, and probably one implication – the requirements need to be defined together with the implementer.

The way changing requirements are handled seem to provide more flexibility though it’s actually a constraint imposed on the CM process which interfaces with the RM processes. Without a proper CM in place, any requirement might arrive to be implemented, independently on whether is feasible or not. This can easily make project’s costs explode, sometimes unnecessarily, while accommodating extreme behavior like changing the same functionality frequently, handling exceptions extensively, etc.

It’s usually helpful to define the requirements together with the implementer, as this can bring more quality in the process, even if more time needs to be invested. However, starting from a solid set of requirements is a critical factor for project’s success. The manifesto makes no direct statement about this. Just iterates that good requirements emerge from self-organizing teams which is not necessarily the case. 

The users who in theory can define the requirements best are the ones who have the deepest knowledge about an organization’s processes and IT architecture, typically the key users and/or IT experts. Self-organization revolves around how a team organizes itself and handles the various activities, though there’s no guarantee that it will address the important aspects, no matter how motivated the team is, how constant the pace, how excellent the technical details were handled or how good the final product works.

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💼Project Management: Methodologies (Part I: Agile Manifesto Reloaded I - An Introduction)

 

There are so many books written on agile methodologies, each attempting to depict the realities of software development projects. There are many truths considered in them, though they seem to blend in a complex texture in which the writer takes usually the position of a preacher in which the sins of the traditional technologies are contrasted with the agile principles. In extremis everything done in the past seems to be wrong, while the agile methods seem to be a panacea, which is seldom the case.

There are already 20 years since the agile manifesto was published and the methodologies adhering to the respective principles don’t seem to provide the expected success, suffering from the same chronical symptoms of their predecessors - they are poorly understood and implemented, tend to function after hammer’s principle, respectively the software development projects still deliver poor results. Moreover, there are more and more professionals who raise their voice against agile practices.

Frankly, the principles behind the agile manifesto make sense. A project should by definition satisfy stakeholders’ requirements, ideally through regular deliveries that incorporate the needed functionality while gradually seeking to get early feedback from customers, respectively involve the customer through all project’s duration, working together to deliver a feasible product. Moreover, self-organizing teams, face-to-face meetings, constant pace, technical excellence should allow minimizing the waste, respectively maximizing the efficiency in the project. Further aspects like simplicity, good design and architecture should establish a basis for success.

Re-reading the agile manifesto, even if each read pulls from experience more and more pro and cons, the manifesto continues to look like a Christmas wish-list. Even if the represented ideas make sense and satisfy a specific need, they are difficult to achieve in a project’s context and setup. Each wish introduces a constraint that brings with it its own limitations. Unfortunately, each policy introduced by a methodology follows the same pattern, no matter of the methodology considered. Moreover, the wishes cover only a small subset from a project’s texture, are general and let lot of space for interpretation and implementation, though the same can be said about any principles that don’t provide a coherent worldview or a conceptual model.

The software development industry needs a coherent worldview that reflects its assumptions, models, characteristics, laws and challenges. Software Engineering (SE) attempts providing such a worldview though unfortunately is too complex for many and there seem to be a big divide when considered in respect to the worldviews introduced by the various Project Management (PM) methodologies. Studying one or two PM methodologies, learning a few programming languages and even the hand on experience on a few projects won’t fill the gaps in knowledge associated with the SE worldview.

Organizations don’t seem to see the need for professionals of having a formal education in SE. On the other side is expected from employees to have by default some of the skillset required, which is not the case. Besides understanding and implementing a technology there are a set of knowledge areas in which the IT professional must have at least a high-level knowledge if it’s expected from him/her to think critically about the respective areas. Unfortunately, the lack of such knowledge leads sometimes to situations which can impact negatively projects.

Almost each important word from the agile manifesto pulls with it a set of concepts from a SE’ worldview – customer satisfaction, software delivery, working software, requirements management, change management, cooperation, teamwork, trust, motivation, communication, metrics, stakeholders’ management, good design, good architecture, lessons learned, performance management, etc. The manifesto needs to be regarded from a SE’s eyeglasses if one expects value from it.

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💼Project Management: Project Execution (Part IV: Projects' Dynamics II - Motion)

Motion is the action or process of moving or being moved between an initial and a final or intermediate point. From the tinniest endeavors to the movement of the planets and beyond, everything is governed by motion. If the laws of nature seem to reveal an inner structural perfection, the activities people perform are quite often far from perfect, which is acceptable if we consider that (almost) everything is a learning process. What is probably less acceptable is the volume of inefficient motion we can easily categorize sometimes as waste.

The waste associated with motion can take many forms: sorting through a pile of tools to find the right one, searching for information, moving back and forth to reach a destination or achieve a goal, etc. Suboptimal motion can have important effects for an organization resulting in reduced productivity, respectively higher costs.

If for repetitive activities that involve a certain degree of similarity can be found typically a way to optimize the motion, the higher the uncertainty of the steps involved, the more difficult it becomes to optimize it. It’s the case of discovery endeavors in which the path between start and destination can’t be traced beforehand, respectively when the destination or path in between can’t be depicted to the needed level of detail. A strategy’s implementation, ERP implementations and other complex projects, especially the ones dealing with new technologies and/or incomplete knowledge, tend to be exploratory in nature and thus fall under this latter type a motion.

In other words, one must know at minimum the starting point, the destination, how to reach it and what it takes to reach it – resources, knowledge, skillset. When one has all this information one can go on and estimate how long it will take to reach the destination, though the estimate reflects the information available as well estimator’s skills in translating the information into a realistic roadmap. Each new information has the potential of impacting considerably the whole process, in extremis to the degree that one must start the journey anew. The complexity of such projects and the volume of uncertainty can make estimation difficult if not impossible, no matter how good estimators' skills are. At best an estimator can come with a best- and worst-case estimation, both however dependent on the assumptions made.

Moreover, complex projects are sensitive to the initial conditions or auspices under which they start. This sensitivity can turn a project in a totally different direction or pace, that can be reinforced positively or negatively as the project progresses. It’s a continuous interplay between internal and external factors and components that can create synergies or have adverse effects with the potential of reaching tipping points.

Related to the initial conditions, as the praxis sometimes shows, for entities found in continuous movement (like organizations) it’s also important to know from where one’s coming (and at what speed), as the previous impulse (driving force) can be further used or stirred as needed. Metaphorically, a project will need a certain time to find the right pace if it lacks the proper impulse.

Unless the team is trained to play and plays like an orchestra, the impact of deviations from expectations can be hardly quantified. To minimize the waste, ideally a project’s journey should minimally deviate from the optimal path, which can be challenging to achieve as a project’s mass can pull the project in one direction or the other. The more the project advances the bigger the mass, fact which can make a project unstoppable. When such high-mass projects are stopped, their impulse can continue to haunt the organization years after.

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💼Project Management: Project Execution (Part III: Projects' Dynamics - An Introduction)

Despite the considerable collection of books on Project Management (PM) and related methodologies, and the fact that projects are inherent endeavors in professional as well personal life (setups that would give in theory people the environment and exposure to different project types), people’s understanding on what it takes to plan and execute a project seems to be narrow and questionable sometimes. Moreover, their understanding diverges considerably from common sense. It’s also true that knowledge and common sense are relative when considering any human endeavor in which there are multiple roads to the same destination, or when learning requires time, effort, skills, and implies certain prerequisites, however the lack of such knowledge can hurt when endeavor’s success is a must and a team effort. 

Even if the lack of understanding about PM can be considered as minor when compared with other challenges/problems faced by a project, when one’s running fast to finish a race, even a small pebble in one’s running shoes can hurt a lot, especially when one doesn’t have the luxury to stop and remove the stone, as it would make sense to do.

It resides in the human nature to resist change, to seek for information that only confirm own opinions, to follow the same approach in handling challenges, even if the attempts are far from optimal, even if people who walked the same path tell you that there’s a better way and even sketch the path and provide information about what it takes to reach there. As it seems, there’s the predisposition to learn on the hard way, if there’s significant learning involved at all. Unfortunately, such situations occur in projects and the solutions often overrun the boundaries of PM, where social and communication skills must be brought into play. 

On the other side, there’s still hope that change can be managed optimally once the facts are explained to a certain level that facilitates understanding. However, such an attempt can prove to be quite a challenge, given the various setups in which PM takes place. The intersection between technologies and organizational setups lead to complex scenarios which make such work more difficult, even if projects’ challenges are of organizational rather than technological nature. 

When the knowledge we have about the world doesn’t fit our expectation, a simple heuristic is to return to the basics. A solid edifice can be built only on a solid foundation and the best foundation in coping with reality is to establish common ground with other people. One can achieve this by identifying their suppositions and expectations, by closing the gap in perception and understanding, by establishing a basis for communication, in which feedback is a must if one wants to make significant progress.

Despite of being explorative and time-consuming, establishing common ground can be challenging when addressing to an imaginary audience, which is quite often the situation. The practice shows however that progress can be made by starting with a set of well-formulated definitions, simple models, principles, and heuristics that have the potential of helping in sense-making.

The goal is thus to identify first the definitions that reflect the basic concepts that need to be considered. Once the concepts defined, they can be related to each other with the help of a few models. Even if fictitious, as simplifications of the reality, the models should allow playing with the concepts, facilitating concepts’ understanding. Principles (set of rules for reasoning) can be used together with heuristics (rules of thumb methods or techniques) for explaining the ‘known’ and approaching the ‘unknown’. Even maybe not perfect, these tools can help building theories or explanatory constructs.

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📦Data Migrations (DM): Conceptualization (Part II: Plan vs. Concept vs. Strategy)

Data Migrations Series

A concept is a document that describes at high level the set of necessary steps and their implications to achieve a desired result, typically making the object of a project. A concept is usually needed to provide more technical and nontechnical information about the desired solution, the context in which a set of steps are conducted, respectively the changes considered, how the changes will be implemented and the further aspects that need to be considered. It can include a high-level plan and sometimes also information that typically belong in a Business Case – goals,objectives, required resources, estimated effort and costs, risks and opportunities.

A concept is used primarily as basis for sign-off as well for establishing common ground and understanding. When approved, it’s used for the actual implementation and solution’s validation. The concept should be updated as the project progresses, respectively as new information are discovered.

Creating a concept for a DM can be considered as best practice because it allows documenting the context, the technical and organizational requirements and dependencies existing between the DM and other projects, how they will be addressed. The concept can include also a high-level plan of the main activities (following to be detailed in a separate document).

Especially when the concept has an exploratory nature (due to incomplete knowledge or other considerations), it can be validated with the help of a proof-of-concept (PoC), the realization of a high-level-design prototype that focuses on the main characteristics of the solution and allows thus identifying the challenges. Once the PoC implemented, the feedback can be used to round out the concept.

Building a PoC for a DM should be considered as objective even when the project doesn’t seem to meet any major challenges. The PoC should resume in addressing the most important DM requirements, ideally by implementing the whole or most important aspects of functionality (e.g. data extraction, data transformations, integrity validation, respectively the import into the target system) for one or two data entities. Once the PoC built, the team can use it as basis for the evolutive development of the solution during the iterations considered.

A strategy is a set of coordinated and sustainable actions following a set of well-defined goals, actions devised into a plan and designed to create value and overcome further challenges. A strategy has the character of a concept though it has a broader scope being usually considered when multiple projects or initiatives compete for the same resources to provide a broader context and handle the challenges, risks and opportunities. Moreover, the strategy takes an inventory of the current issues and architecture – the 'AS-IS' perspective and sketches the to 'TO-BE' perspective by devising a roadmap that bridges the gap between the two.

In the case of a DM a strategy might be required when multiple DM projects need to be performed in parallel or sequentially, as it can help the organization to better manage the migrations.

A plan is a high-level document that describes the tasks, schedule and resources required to carry on an activity. Even if it typically refers to the work or product breakdown structure, it can cover other information usually available in a Business Case. A project plan is used to guide both project execution and project control, while in the context of Strategic Management the (strategic) plan provides a high-level roadmap on how the defined goals and objectives will be achieved during the period covered by the strategy.

For small DM projects a plan can be in theory enough. As both a strategy and a concept can include a high-level plan, the names are in praxis interchangeable.

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🧮ERP: Implementations (Part V: It’s all about Partnership - An Introduction)

ERP Implementations Series

Unless the organization (customer) implementing an ERP system has a strong IT team and the knowledge required for the implementation is available already in-house, the resources need to be acquired from the market, and probably the right thing to do is to identify a certified implementer (partner) which can fill the knowledge and skillset gaps, respectively which can help splitting the risks associated with such an implementation.

In theory, the customer provides knowledge about its processes, while the partner comes with expertise about the system to be implemented and further technologies, industry best practices, project methodologies, etc. Further on, the mix is leveraged to harness the knowledge and reach project’s objectives. 

In praxis however finding an implementer which can act as partner might be more challenging than expected. This because the implementer needs to understand customer’s business and where it’s heading, bridge the gap between functional requirements and system’s functionality, advise on areas of improvement, prepare the customer for the project and lead the customer through the changes, respectively establish a basis for the future. Some of the implications are seldom made explicit even if they are implied by what is needed by the project. 

Technology is seldom the issue in an ERP implementation, the challenges residing in handing the change and the logistics required. There are so many aspects to be considered and handled, and this can be challenging for any implementer no matter how long has been on the market or how experienced the resources are. Somebody needs to lead the change and the customer seldom has the knowledge to handle the change. In some cases, the implementer must make the customer aware of the implications, while in others needs to take the initiative and lead the change, though the customer needs to play along, which can be challenging also. 

Many aspects need to be handled at management level from a strategical point of view on customer’s side. It starts with assuring that the most important aspects of the business where considered, that the goals and objectives are clear, that the proper environment is created, and ends with the timely decision-making, with assuring that the resources are available when needed, that the needed organization structures and roles are in place, that the required knowledge is available before, during and after implementation, that the potential brought by the ERP system is harnessed for the years to come. 

A partnership allows in theory splitting the implementation risks as ERP implementations have a high rate of failure. Quite often the outcomes of such projects don’t meet the expectations, the systems being in extremis unusable or a bottleneck for the organization. Ideally one should work with the partner(s) and attempt solving the issues, split eventually the incurred cost overruns, find a middle way. Most of the times it’s recommended to find a solution together rather than coming to a litigation. 

Given the complex dependencies existing between the various parts of the project, the causes that lead to poor implementations are difficult to prove, as there are almost always grey areas. Moreover, the litigations can require a considerable time and resources to settle. These can be just extreme situations, and as long one has a good partner, there’s no need to think that far. On the other side, even if undesirable, one must be prepared also for such outcomes, even if the countermeasures may involve an additional effort. Therefore, one must address such issues in contracts by establishing the areas of accountability/responsibilities for each party, document adequately the requirements and further (important) communication, make sure that the deliverables have the expected quality, etc.

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𖣯Strategic Management: Strategy Design (Part IV: Designing for Simplicity)

More than two centuries ago, in his course on the importance of Style in Literature, George Lewes wisely remarked that 'the first obligation of Simplicity is that of using the simplest means to secure the fullest effect' [1]. This is probably the most important aspect the adopters of the KISS mantra seem to ignore – solutions need to be simple while covering all or most important aspects to assure the maximum benefit. The challenge for many resides in defining what the maximum benefit is about. This state of art is typically poorly understood, especially when people don’t understand what’s possible, respectively of what’s necessary to make things work smoothly. 

To make the simplicity principle work, one must envision the desired state of a product or solution and trace back what’s needed to achieve that vision. One can aim for the maximum or for the minimum possible, respectively for anything in between. That’s at least true in theory, in praxis there are constraints that limit the range of achievement, constraints ranging from the availability of resources, their maturity or the available time, respectively to the limits for growth - the learning capacity of individuals and organization as a whole. 

On the other side following the 80/20 principle, one could achieve in theory 80% of a working solution with 20% of the effort needed in achieving the full 100%. This principle comes with a trick too because one needs to focus on the important components or aspects of the solution for this to work. Otherwise, one is forced to do exploratory work in which the learning is gradually assimilated into the solution. This implies continuous feedback, respectively changing the targets as one progresses in multiple iterations. The approach is typically common to ERP implementations, BI and Data Management initiatives, or similar transformative projects which attempt changing an organization’s data, information, or knowledge flows - the backbones organizations are built upon.     

These two principles can be used together to shape an organization. While simplicity sets a target or compass for quality, the 80/20 principle provides the means of splitting the roadmap and effort into manageable targets while allowing to identify and prioritize the critical components, and they seldom resume only to technology. While technologies provide a potential for transformation, in the end is an organization’s setup that has the transformative role. 

For transformational synergies to happen, each person involved in the process must have a minimum of necessary skillset, knowledge and awareness of what’s required and how a solution can be harnessed. This minimum can be initially addressed through training and self-learning, however without certain mechanisms in place, the magic will not happen by itself. Change needs to be managed from within as part of an organization’s culture, by the people close to the flow, and when necessary, also from the outside, by the ones who can provide guiding direction. Ideally, a strategic approach is needed the vision, mission, goals, objectives, and roadmap are sketched, where intermediary targets are adequately mapped and pursued, and the progress is adequately tracked.

Thus, besides the technological components is needed to consider the required organizational components to support and manage change. These components form a structure which needs to adhere by design to the same principle of simplicity. According to Lewes, the 'simplicity of structure means organic unity' [1], which can imply harmony, robustness, variety, balance, economy or proportion. Without these qualities the structure of the resulting edifice can break under its own weight. Moreover, paraphrasing Eric Hoffer, simplicity marks the end of a continuous process of designing, building, and refining, while complexity marks a primitive stage.

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Written: Sep-2020, Last Reviewed: Mar-2024

References:
[1] George H Lewes (1865) "The Principles of Success in Literature"

Considered quotes:
"Simplicity of structure means organic unity, whether the organism be simple or complex; and hence in all times the emphasis which critics have laid upon Simplicity, though they have not unfrequently confounded it with narrowness of range." (George H Lewes, "The Principles of Success in Literature", 1865)
"The first obligation of Simplicity is that of using the simplest means to secure the fullest effect. But although the mind instinctively rejects all needless complexity, we shall greatly err if we fail to recognise the fact, that what the mind recoils from is not the complexity, but the needlessness." (George H Lewes, "The Principles of Success in Literature", 1865)
"In products of the human mind, simplicity marks the end of a process of refining, while complexity marks a primitive stage." (Eric Hoffer, 1954)