🗄️Data Management: Data Culture (Part IV: Quo vadis? [Where are you going?])

Data Management Series

The people working for many years in the fields of BI/Data Analytics, Data and Process Management probably met many reactions that at the first sight seem funny, though they reflect bigger issues existing in organizations: people don’t always understand the data they work with, how data are brought together as part of the processes they support, respectively how data can be used to manage and optimize the respective processes. Moreover, occasionally people torture the data until it confesses something that doesn’t necessarily reflect the reality. It’s even more deplorable when the conclusions are used for decision-making, managing or optimizing the process. In extremis, the result is an iterative process that creates more and bigger issues than whose it was supposed to solve!

Behind each blunder there are probably bigger understanding issues that need to be addressed. Many of the issues revolve around understanding how data are created, how are brought together, how the processes work and what data they need, use and generate. Moreover, few business and IT people look at the full lifecycle of data and try to optimize it, or they optimize it in the wrong direction. Data Management is supposed to help, and it does this occasionally, though a methodology, its processes and practices are as good as people’s understanding about data and its use! No matter how good a data methodology is, it’s as weak as the weakest link in its use, and typically the issues revolving around data and data understanding are the weakest link. 

Besides technical people, few businesspeople understand the full extent of managing data and its lifecycle. Unfortunately, even if some of the topics are treated in the books, they are too dry, need hands on experience and some thought in corroborating practices with theories. Without this, people will do things mechanically, processes being as good as the people using them, their value becoming suboptimal and hinder the business. That’s why training on Data Management is not enough without some hands-on experience!

The most important impact is however in BI/Data Analytics areas - how the various artifacts are created and used as support in decision-making, process optimization and other activities rooted in data. Ideally, some KPIs and other metrics should be enough for managing and directing a business, however just basing the decisions on a set of KPIs without understanding the bigger picture, without having a feeling of the data and their quality, the whole architecture, no matter how splendid, can breakdown as sandcastle on a shore meeting the first powerful wave!

Sometimes it feels like organizations do things from inertia, driven by the forces of the moment, initiatives and business issues for which temporary and later permanent solutions are needed. The best chance for solving many of the issues would have been a long time ago, when the issues were still small to create any powerful waves within the organizations. Therefore, a lot of effort is sometimes spent in solving the consequences of decisions not made at the right time, and that can be painful and costly!

For building a good business one needs also a solid foundation. In the past it was enough to have a good set of products that are profitable. However, during the past decade(s) the rules of the game changed driven by the acerb competition across geographies, inefficiencies, especially in the data and process areas, costing organizations on the short and long term. Data Management in general and Data Quality in particular, even if they’re challenging to quantify, have the power to address by design many of the issues existing in organizations, if given the right chance!

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🧭Business Intelligence: Perspectives (Part XVI: On the Cusps of Complexity)

Business Intelligence Series

We live in a complex world, which makes it difficult to model and work with the complex models that attempt to represent it. Thus, we try to simplify it to the degree that it becomes processable and understandable for us, while further simplification is needed when we try to depict it by digital means that make it processable by machines, respectively by us. Whenever we simplify something, we lose some aspects, which might be acceptable in many cases, but create issues in a broader number of ways.

With each layer of simplification results a model that addresses some parts while ignoring some parts of it, which restricts models’ usability to the degree that makes them unusable. The more one moves toward the extremes of oversimplification or complexification, the higher the chances for models to become unusable.

This aspect is relevant also in what concerns the business processes we deal with. Many processes are oversimplified to the degree that we track the entry and exit points, respectively the quantitative aspects we are interested in. In theory this information should be enough when answering some business questions, though might be insufficient when one dives deeper into processes. One can try to approximate, however there are high chances that such approximations deviate too much from the value approximated, which can lead to strange outcomes.

Therefore, when a date or other values are important, organizations consider adding more fields to reflect the implemented process with higher accuracy. Unfortunately, unless we save a history of all the important changes in the data, it becomes challenging to derive the snapshots we need for our analyses. Moreover, it is more challenging to obtain consistent snapshots. There are systems which attempt to obtain such snapshots through the implementation of the processes, though also this approach involves some complexity and other challenges.

Looking at the way business processes are implemented (see ERP, CRM and other similar systems), the systems track the created, modified and a few other dates that allow only limited perspectives. The fields typically provide the perspectives we need for data analysis. For many processes, it would be interesting to track other events and maybe other values taken in between.

There is theoretical potential in tracking more detailed data, but also a complexity that’s difficult to transpose into useful information about the processes themselves. Despite tracking more data and the effort involved in such activities, processes can still behave like black boxes, especially when we have no or minimal information about the processes implemented in Information Systems.

There’s another important aspect - even if systems provide similar implementations of similar processes, the behavior of users can make an important difference. The best example is the behavior of people entering the relevant data only when a process closes and ignoring the steps happening in between (dates, price or quantity changes).

There is a lot of missing data/information not tracked by such a system, especially in what concerns users’ behavior. It’s true that such behavior can be tracked to some degree, though that happens only when data are modified physically. One can suppose that there are many activities happening outside of the system.

The data gathered represents only the projection of certain events, which might not represent accurately and completely the processes or users’ behavior. We have the illusion of transparency, though we work with black boxes. There can be a lot of effort happening outside of these borders.  

Fortunately, we can handle oversimplified processes and data maintenance, though one can but wonder how many important things can be found beyond the oversimplifications we work with, respectively what we miss in the process. 

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🧭Business Intelligence: Perspectives (Part XI: Ways of Thinking about Data)

Business Intelligence Series

One can observe sometimes the tendency of data professionals to move from a business problem directly to data and data modeling without trying to understand the processes behind the data. One could say that the behavior is driven by the eagerness of exploring the data, though even later there are seldom questions considered about the processes themselves. One can argue that maybe the processes are self-explanatory, though that’s seldom the case. 

Conversely, looking at the datasets available on the web, usually there’s a fact table and the associated dimensions, the data describing only one process. It’s natural to presume that there are data professionals who don’t think much about, or better said in terms of processes. A similar big jump can be observed in blog posts on dashboards and/or reports, bloggers moving from the data directly to the data model. 

In the world of complex systems like Enterprise Resource Planning (ERP) systems thinking in terms of processes is mandatory because a fact table can hold the data for different processes, while processes can span over multiple fact-like tables, and have thus multiple levels of detail. Moreover, processes are broken down into sub-processes and procedures that have a counterpart in the data as well. 

Moreover, within a process there can be multiple perspectives that are usually module or role dependent. A perspective is a role’s orientation to the word for which the data belongs to, and it’s slightly different from what the data professional considers as view, the perspective being a projection over a set of processes within the data, while a view is a projection of the perspectives into the data structure. 

For example, considering the order-to-cash process there are several sub-processes like order fulfillment, invoicing, and payment collection, though there can be several other processes involved like credit management or production and manufacturing. Creating, respectively updating, or canceling an order can be examples of procedures. 

The sales representative, the shop worker and the accountant will have different perspectives projected into the data, focusing on the projection of the data on the modules they work with. Thinking in terms of modules is probably the easiest way to identify the boundaries of the perspectives, though the rules are occasionally more complex than this.

When defining and/or attempting to understand a problem it’s important to understand which perspective needs to be considered. For example, the sales volume can be projected based on Sales orders or on invoiced Sales orders, respectively on the General ledger postings, and the three views can result in different numbers. Moreover, there are partitions within these perspectives based on business rules that determine what to include or exclude from the logic. 

One can define a business rule as a set of conditional logic that constraints some part of the data in the data structures by specifying what is allowed or not, though usually we refer to a special type called selection business rule that determines what data are selected (e.g. open Purchase orders, Products with Inventory, etc.). However, when building the data model we need to consider business rules as well, though we might need to check whether they are enforced as well. 

Moreover, it’s useful to think also in terms of (data) entities and sub-entities, in which the data entity is an abstraction from the physical implementation of database tables. A data entity encapsulates (hides internal details) a business concept and/or perspective into an abstraction (simplified representation) that makes development, integration, and data processing easier. In certain systems like Dynamics 365 is important to think at this level because data entities can simplify data modelling considerably.

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🧭Business Intelligence: Why Data Projects Fail to Deliver Real-Life Impact (Part I: First Thoughts)

Business Intelligence Series

A data project has a set of assumptions and requirements that must be met, otherwise the project has a high chance of failing. It starts with a clear idea of the goals and objectives, and they need to be achievable and feasible, with the involvement of key stakeholders and the executive without which it’s impossible to change the organization’s data culture. Ideally, there should also be a business strategy, respectively a data strategy available to understand the driving forces and the broader requirements. 

An organization’s readiness is important not only in what concerns the data but also the things revolving around the data - processes, systems, decision-making, requirements management, project management, etc. One of the challenges is that the systems and processes available can’t be used as they are for answering important business questions, and many of such questions are quite basic, though unavailability or poor quality of data makes this challenging if not impossible. 

Thus, when starting a data project an organization must be ready to change some of its processes to address a project’s needs, and thus the project can become more expensive as changes need to be made to the systems. For many organizations the best time to have done this was when they implemented the system, respectively the integration(s) between systems. Any changes made after that come in theory with higher costs derived from systems and processes’ redesign.

Many projects start big and data projects are no exception to this. Some of them build a costly infrastructure without first analyzing the feasibility of the investment, or at least whether the data can form a basis for answering the targeted questions. On one side one can torture any dataset and some knowledge will be obtained from it (aka data will confess), though few datasets can produce valuable insights, and this is where probably many data projects oversell their potential. Conversely, some initiatives are worth pursuing even only for the sake of the exposure and experience the employees get. However, trying to build something big only through the perspective of one project can easily become a disaster. 

When building a data infrastructure, the project needs to be an initiative given the transformative potential such an endeavor can have for the organization, and the different aspects must be managed accordingly. It starts with the management of stakeholders’ expectations, with building a data strategy, respectively with addressing the opportunities and risks associated with the broader context.

Organizations recognize that they aren’t capable of planning and executing such a project or initiative, and they search for a partner to lead the way. Becoming overnight such a partner is more than a challenge as a good understanding of the industry and the business is needed. Some service providers have such knowledge, at least in theory, though the leap from knowledge to results can prove to be a challenge even for experienced service providers. 

Many projects follow the pattern: the service provider comes, analyzes the requirements, builds something wonderful, the solution is used for some time and then the business realizes that the result is not what was intended. The causes are multiple and usually form a complex network of causality, though probably the most important aspect is that customers don’t have the in-house technical resources to evaluate the feasibility of requirements, solutions, respectively of the results. Even if organizations involve the best key users, are needed also good data professionals or similar resources who can become the bond between the business and the services provider. Without such an intermediary the disconnect between the business and the service provider can grow with all the implications. 

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🗄️🗒️Data Management: Master Data Management [MDM] [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources. 
Last updated: 28-Mar-2024

Master Data Management (MDM)

• {definition} the technologies, processes, policies, standards and guiding principles that enable the management of master data values to enable consistent, shared, contextual use across systems, of the most accurate, timely, and relevant version of truth about essential business entities [2],[3]
• {goal} enable sharing of information assets across business domains and applications within an organization [4]
• {goal} provide authoritative source of reconciled and quality-assessed master (and reference) data [4]
• {goal} lower cost and complexity through use of standards, common data models, and integration patterns [4]
• {driver} meeting organizational data requirements
• {driver} improving data quality
• {driver} reducing the costs for data integration
• {driver} reducing risks 
• {type} operational MDM 
• involves solutions for managing transactional data in operational applications [1]
• rely heavily on data integration technologies
• {type} analytical MDM
• involves solutions for managing analytical master data
• centered on providing high quality dimensions with multiple hierarchies [1]
• cannot influence operational systems
• any data cleansing made within operational application isn’t recognized by transactional applications [1]
• ⇒ inconsistencies to the main operational data [1]
• transactional application knowledge isn’t available to the cleansing process
• {type} enterprise MDM
• involves solutions for managing both transactional and analytical master data 
• manages all master data entities
• deliver maximum business value
• operational data cleansing
• improves the operational efficiencies of the applications and the business processes that use the applications
• cross-application data need
• consolidation
• standardization
• cleansing
• distribution
• needs to support high volume of transactions
• ⇒ master data must be contained in data models designed for OLTP
• ⇐ ODS don’t fulfill this requirement 
• {enabler} high-quality data
• {enabler} data governance
• {benefit} single source of truth
• used to support both operational and analytical applications in a consistent manner [1]
• {benefit} consistent reporting
• reduces the inconsistencies experienced previously
• influenced by complex transformations
• {benefit} improved competitiveness
• MDM reduces the complexity of integrating new data and systems into the organization
• ⇒ increased flexibility and improves competitiveness
• ability to react to new business opportunities quickly with limited resources
• {benefit} improved risk management
• more reliable and consistent data improves the business’s ability to manage enterprise risk [1]
• {benefit} improved operational efficiency and reduced costs
• helps identify business’ pain point
• by developing a strategy for managing master data
• {benefit} improved decision making
• reducing data inconsistency diminishes organizational data mistrust and facilitates clearer (and faster) business decisions [1]
• {benefit} more reliable spend analysis and planning
• better data integration helps planners come up with better decisions
• improves the ability to 
• aggregate purchasing activities
• coordinate competitive sourcing
• be more predictable about future spending
• generally improve vendor and supplier management
• {benefit} regulatory compliance
• allows to reduce compliance risk
• helps satisfy governance, regulatory and compliance requirements
• simplifies compliance auditing
• enables more effective information controls that facilitate compliance with regulations
• {benefit} increased information quality
• enables organizations to monitor conformance more effectively
• via metadata collection
• it can track whether data meets information quality expectations across vertical applications, which reduces information scrap and rework
• {benefit} quicker results
• reduces the delays associated with extraction and transformation of data [1]
• ⇒ it speeds up the implementation of application migrations, modernization projects, and data warehouse/data mart construction [1]
• {benefit} improved business productivity
• gives enterprise architects the chance to explore how effective the organization is in automating its business processes by exploiting the information asset [1]
• ⇐ master data helps organizations realize how the same data entities are represented, manipulated, or exchanged across applications within the enterprise and how those objects relate to business process workflows [1]
• {benefit} simplified application development
• provides the opportunity to consolidate the application functionality associated with the data lifecycle [1]
• ⇐ consolidation in MDM is not limited to the data
• ⇒ provides a single functional to which different applications can subscribe
• ⇐ introducing a technical service layer for data lifecycle functionality provides the type of abstraction needed for deploying SOA or similar architectures
• factors to consider for implementing an MDM:
• effective technical infrastructure for collaboration [1]
• organizational preparedness
• for making a quick transition from a loosely combined confederation of vertical silos to a more tightly coupled collaborative framework
• {recommendation} evaluate the kinds of training sessions and individual incentives required to create a smooth transition [1]
• metadata management
• via a metadata registry 
• {recommendation} sets up a mechanism for unifying a master data view when possible [1]
• determines when that unification should be carried out [1]
• technology integration
• {recommendation} diagnose what technology needs to be integrated to support the process instead of developing the process around the technology [1]
• anticipating/managing change
• proper preparation and organization will subtly introduce change to the way people think and act as shown in any shift in pattern [1]
• changes in reporting structures and needs are unavoidable
• creating a partnership between Business and IT
• IT roles
• plays a major role in executing the MDM program[1]
• business roles
• identifying and standardizing master data [1]
• facilitating change management within the MDM program [1]
• establishing data ownership
• measurably high data quality
• overseeing processes via policies and procedures for data governance [1]
• {challenge} establishing enterprise-wide data governance
• {recommendation} define and distribute the policies and procedures governing the oversight of master data
• seeking feedback from across the different application teams provides a chance to develop the stewardship framework agreed upon by the majority while preparing the organization for the transition [1]
• {challenge} isolated islands of information
• caused by vertical alignment of IT
• makes it difficult to fix the dissimilarities in roles and responsibilities in relation to the isolated data sets because they are integrated into a master view [1]
• caused by data ownership
• the politics of information ownership and management have created artificial exclusive domains supervised by individuals who have no desire to centralize information [1]
• {challenge} consolidating master data into a centrally managed data asset [1]
• transfers the responsibility and accountability for information management from the lines of business to the organization [1]
• {challenge} managing MDM
• MDM should be considered a program and not a project or an application [1]
• {challenge} achieving timely and accurate synchronization across disparate systems [1]
• {challenge} different definitions of master metadata 
  • different coding schemes, data types, collations, and more
  • ⇐ data definitions must be unified
• {challenge} data conflicts 
• {recommendation} resolve data conflicts during the project [5]
• {recommendation} replicate the resolved data issues back to the source systems [5]
• {challenge} domain knowledge 
• {recommendation} involve domain experts in an MDM project [5]
• {challenge} documentation
• {recommendation} properly document your master data and metadata [5]
• approaches
• {architecture} no central MDM 
• isn’t a real MDM approach
• used when any kind of cross-system interaction is required [5]
• e.g. performing analysis on data from multiple systems, ad-hoc merging and cleansing
• {drawback} very inexpensive at the beginning; however, it turns out to be the most expensive over time [5]
• {architecture} central metadata storage 
• provides unified, centrally maintained definitions for master data [5]
• followed and implemented by all systems
• ad-hoc merging and cleansing becomes somewhat simpler [5]
• does not use a specialized solution for the central metadata storage [5]
• ⇐ the central storage of metadata is probably in an unstructured form 
• e.g. documents, worksheets, paper
• {architecture} central metadata storage with identity mapping 
• stores keys that map tables in the MDM solution
• only has keys from the systems in the MDM database; it does not have any other attributes [5]
• {benefit} data integration applications can be developed much more quickly and easily [5]
• {drawback} raises problems in regard to maintaining master data over time [5]
• there is no versioning or auditing in place to follow the changes [5]
• ⇒ viable for a limited time only
• e.g. during upgrading, testing, and the initial usage of a new ERP system to provide mapping back to the old ERP system
• {architecture} central metadata storage and central data that is continuously merged 
• stores metadata as well as master data in a dedicated MDM system
• master data is not inserted or updated in the MDM system [5]
• the merging (and cleansing) of master data from source systems occurs continuously, regularly [5]
• {drawback} continuous merging can become expensive [5]
• the only viable use for this approach is for finding out what has changed in source systems from the last merge [5]
• enables merging only the delta (new and updated data)
• frequently used for analytical systems
• {architecture} central MDM, single copy 
• involves a specialized MDM application
• master data, together with its metadata, is maintained in a central location [5]
• ⇒ all existing applications are consumers of the master data
• {drawback} upgrade all existing applications to consume master data from central storage instead of maintaining their own copies [5]
• ⇒ can be expensive
• ⇒ can be impossible (e.g. for older systems)
• {drawback} needs to consolidate all metadata from all source systems [5]
• {drawback} the process of creating and updating master data could simply be too slow [5]
• because of the processes in place
• {architecture} central MDM, multiple copies 
• uses central storage of master data and its metadata
• ⇐ the metadata here includes only an intersection of common metadata from source systems [5]
• each source system maintains its own copy of master data, with additional attributes that pertain to that system only [5]
• after master data is inserted into the central MDM system, it is replicated (preferably automatically) to source systems, where the source-specific attributes are updated [5]
• {benefit} good compromise between cost, data quality, and the effectiveness of the CRUD process [5]
• {drawback} update conflicts
• different systems can also update the common data [5]
• ⇒ involves continuous merges as well [5]
• {drawback} uses a special MDM application

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Acronyms:

MDM - Master Data Management

ODS - Operational Data Store

OLAP - online analytical processing

OLTP - online transactional processing

SOA - Service Oriented Architecture

References:
[1] The Art of Service (2017) Master Data Management Course 
[2] DAMA International (2009) "The DAMA Guide to the Data Management Body of Knowledge" 1st Ed.

[3] Tony Fisher 2009 "The Data Asset"

[4] DAMA International (2017) "The DAMA Guide to the Data Management Body of Knowledge" 2nd Ed.

[5] Dejan Sarka et al (2012) Exam 70-463: Implementing a Data Warehouse with Microsoft SQL Server 2012 (Training Kit)

🗄️Data Management: Master Data Management (Part I: Understanding Integration Challenges) [Answer]

Data Management Series

Answering Piethein Strengholt’s post [1] on Master Data Management’s (MDM) integration challenges, the author of "Data Management at Scale".

Master data can be managed within individual domains though the boundaries must be clearly defined, and some coordination is needed. Attempting to partition the entities based on domains doesn’t always work. The partition needs to be performed at attribute level, though even then might be some exceptions involved (e.g. some Products are only for Finance to use). One can identify then attributes inside of the system to create the boundaries.

MDM is simple if you have the right systems, processes, procedures, roles, and data culture in place. Unfortunately, people make it too complicated – oh, we need a nice shiny system for managing the data before they are entered in ERP or other systems, we need a system for storing and maintaining the metadata, and another system for managing the policies, and the story goes on. The lack of systems is given as reason why people make no progress. Moreover, people will want to integrate the systems, increasing the overall complexity of the ecosystem.

The data should be cleaned in the source systems and assessed against the same. If that's not possible, then you have the wrong system! A set of well-built reports can make data assessment possible. 

The metadata and policies can be maintained in Excel (and stored in SharePoint), SharePoint or a similar system that supports versioning. Also, for other topics can be found pragmatic solutions.

ERP systems allow us to define workflows and enable a master data record to be published only when the information is complete, though there will always be exceptions (e.g., a Purchase Order must be sent today). Such exceptions make people circumvent the MDM systems with all the issues deriving from this.

Adding an MDM system within an architecture tends to increase the complexity of the overall infrastructure and create more bottlenecks. Occasionally, it just replicates the structures existing in the target system(s).

Integrations are supposed to reduce the effort, though in the past 20 years I never saw an integration to work without issues, even in what MDM concerns. One of the main issues is that the solutions just synchronized the data without considering the processual dependencies, and sometimes also the referential dependencies. The time needed for troubleshooting the integrations can easily exceed the time for importing the data manually over an upload mechanism.

To make the integration work the MDM will arrive to duplicate the all the validation available in the target system(s). This can make sense when daily or weekly a considerable volume of master data is created. Native connectors simplify the integrations, especially when it can handle the errors transparently and allow to modify the records manually, though the issues start as soon the target system is extended with more attributes or other structures.

If an organization has an MDM system, then all the master data should come from the MDM. As soon as a bidirectional synchronization is used (and other integrations might require this), Pandora’s box is open. One can define hard rules, though again, there are always exceptions in which manual interference is needed.

Attempting an integration of reference data is not recommended. ERP systems can have hundreds of such entities. Some organizations tend to have a golden system (a copy of production) with all the reference data. It works for some time, until people realize that the solution is expensive and time-consuming.

MDM systems do make sense in certain scenarios, though to get the integrations right can involve a considerable effort and certain assumptions and requirements must be met.

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References:
[1] Piethein Strengholt (2023) Understanding Master Data Management’s Integration Challenges (link)

🧭🏭Business Intelligence: Microsoft Fabric (Part II: Domains and the Data Mesh I -The Challenge of Structure Matching)

Business Intelligence Series

The holy grail of building a Data Analytics infrastructure seems to be nowadays the creation of a data mesh, a decentralized data architecture that organizes data by specific business domains. This endeavor proves to be difficult to achieve given the various challenges faced  – data integration, data ownership, data product creation and ownership, enablement of data citizens, respectively enforcing security and governance in a federated manner. 

Microsoft Fabric promises to facilitate the creation of data mashes with the help of domains and subdomain by providing built-in security, administration, and governance features associated with them. A domain is a way of logically grouping together all the data in an organization that is relevant to a particular area or field. A subdomain is a way for fine tuning the logical grouping of the data.

Business domains & their entities

At high level the challenge of building a data mesh is on how to match or aggregate structures. On one side is the high-level structure of the data mesh, while on the other side is the structure of the business data entities. The data entities can be grouped within a taxonomy with multiple levels that expands to the departments. That’s why it seems somehow natural to consider the departments as the top-most domains of the data mesh. The issue is that if the segmentation starts from a high level, iI becomes inflexible in modeling. Moreover, one has only domains and subdomains, and thus a 2-level structure to model the main aspects of the data mesh.

Some organizations allow unrestricted access to the data belonging to a given department, while others breakdown the access to a more granular level. There are also organizations that don’t restrict the access at all, though this may change later. Besides permissions and a way of grouping together the entities, what value brings to set the domains as departments? 

Therefore, I’m not convinced about using an organizations’ departmental structure as domains, especially when such a structure may change and this would imply a full range of further changes. Moreover, such a structure doesn’t reflect the span of processes or how permissions are assigned for the various roles, which are better reflected on how information systems are structured. Most probably the solution needs to accommodate both perspective and be somehow in the middle. 

Take for example the internal structure of the modules from Dynamics 365 (D365). The Finance area is broken down in Accounts Payable, Accounts Receivables, Fixed Assets, General Ledger, etc. In some organizations the departments reflect this delimitation to some degree, while in others are just associated with finance-related roles. Moreover, the permissions are more granular and, reflecting the data entities the users work with. 

Conversely, SCM extends into Finance as Purchase orders, Sales orders and other business documents are the starting or intermediary points of processes that span modules. Similarly, there are processes that start in CRM or other systems. The span of processes seem to be more appropriate for structuring the data mesh, though the system overlapping with the roles involved in the processes and the free definition of process boundaries can overcomplicate the whole design.

It makes sense to define the domains at a level that resembles the structure of the modules available in D365, while the macro data-entities represent the subdomain. The subdomain would represent then master as well as transactional data entities from the perspective of the domains, with there will be entities that need to be shared between multiple domains. Such a structure has less chances to change over time, allowing more flexibility and smaller areas of focus and thus easier to design, develop, test, deploy and maintain.

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💫ERP Systems: Microsoft Dynamics 365's Vendor Invoices Processing (Setup Areas)

ERP Systems

Invoices can be set up to be imported automatically in Dynamics 365 for Finance and Operation (D365 F&O) via Invoice Capture (see process). Besides the Invoice Capture-related set up, there are several areas related to Vendor invoice's processing:

General Ledger

Accounts for automatic transactions need to be defined at least for the 'Vendor invoice rounding off' and 'Rounding variance', respectively for the 'Exchange rate gain' and 'Exchange rate loss', if this wasn't done already.

To facilitate accounts' reconciliation and reporting, one can enable a Batch transfer rule for the Source document type 'Vendor invoice' and thus one journal is used for each Vendor invoice. This also makes sure that the Description from Invoice will be taken over in the Journal, which facilitates accounts' reconciliation.

Subscription Billing

In case one needs to defer the amounts over a time interval (e.g. x months), as it's the case for prepayments, the integration with Subscription Billing on the Vendor side in F&O seems to work with minimal configuration. A Billing schedule is created for each Invoice distribution and can be modified or cancelled after the Invoice is posted, if needed.

If your organization uses Subscription Billing also for Accounts Receivables (AR), one might need to compromise on the setup because the same parameters are used for both modules. 

In what concerns accounts' reconciliation, there seems to be a 1:1 mapping between the Schedule line and the General Ledger (GL) posting, a table with the mapping between the records being available. One can build thus a Paginated report to display the mapping between AP and GL, if the infrastructure is in place (e.g. by building a data lakehouse/warehouse based on F&O, see post). 

Fixed Assets

One can enable the creation of Fixed assets by checking the "Create asset during product receipt or invoice posting" radio button in Fixed assets parameters.

The feature "create the fixed asset automatically during the time of invoice import" for PO-based Invoices (with "Create Fixed Asset" flag set at line level) doesn't seem to be supported yet.

Vendor Invoice Journals

Before Invoice Capture, Vendor invoice journals were helpful for posting summarized cost invoices that are not associated with POs (e.g. expenses for supplies or services). One can still use this approach, however if the line-based details are important, then it makes sense to use Pending vendor invoice with Service items or Procurement categories.  

A single Vendor invoice is created as one Vendor invoice journal. 

Organization Administration

When using workflows, setting the same language for all LEs can reduce the amount of redundant information maintained in the workflow(s), otherwise the texts need to be provided for each language. 

Default descriptions can be enabled for Purchase orders' invoice ledger and vendor to carry the same description entered Pending Vendor Invoices in Invoices and Journals. The functionality works also for Cost invoices.

Ideally, Invoice's description should have been maintained in Invoice capture and/or Microsoft should have provided a default description also for it. 

System

Unless there's a requirement to post manually the journals from subledger to GL, a batch transfer for subledger journals must be created for each LE.

One can enable email notifications for the users participating in workflows and use workflow delegations for the intervals the respective users are on leave. 

All users participating in the workflow must be available also as active employees. It makes sense to do latest when the integration goes Live. Moreover, the Users need to have the appropriate permissions for the roles they have in the process.

Vendor Invoice Automation

There's further functionality available under the 'Vendor Invoice Automation' label (see [1], [2]), though the following are the most important ones: 

• Automatically apply prepayments to vendor invoices
• Automatically submit imported invoices to the workflow system.
• Match product receipts to pending vendor invoice lines.

Recurring Vendor Invoice Templates

Microsoft introduced with 10.0.38 PU a new feature called 'Vendor invoice templates', which allows creating recurring vendor invoices without the need to enter all the vendor invoice information for each separate invoice (see [3]). There seems to be no information available whether any integration between this feature and Invoice Capture will be supported.

E-Invoicing

EU countries need to enforce the Directive 2014/55/EU for Vendor and Customer invoices. The directive requires that the electronic exchange of invoice documents between suppliers and buyers to occur over government-held third-party solutions. Each country has its own system(s) and regulations with different scope and timelines. Some countries have already requirements for 2024, respectively 2025 and there are similar projects in US and other countries. 

Even if Microsoft started this year (2023) to provide country-specific integrations for e-Invoicing, for the moment there seem no information available on how e-Invoicing will integrate with Invoice Capture.

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Resources:
[1] Microsoft Learn (2023) Vendor Invoice Automation (link)
[2]Microsoft Dynamics 365 (2023) The Future of Finance: Unlocking the Benefits of Accounts Payable Automation (link)
[3] Hylke Britstra (2023) Recurring vendor invoice templates (link)
[4] Dan Edwards (2023) AP Automation at DynamicsCon (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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🔦Process Management: "Manage Problem" Process Diagram in ITIL

Process diagrams for IT methodologies like ITIL can be approached in general at a lower level of detail than business processes (see 'Create Product' process diagram) and thus the text blocks can be left out unless further high-level instructions need to be given. Because they are highly standardized, one can find many examples on the internet as inspiration. On the other hand, the processes need to be adapted to an organization's needs. 

Compared with other similar process diagrams, the diagram attempts (1) to highlight also the interfaces with other processes (e.g. Manage Change, Manage Knowledge, etc.), (2) to assure that the User can cycle through the steps, respectively that there's no infinite loop via the solvability question. 

The following definitions apply:

Change: the addition, modification or removal of anything that could have an effect on a servicel

Incident: unplanned interruption or reduction in quality.

Known Error: problem that has a documented root cause and/or a workaround.

Problem: a cause of one or more incidents.

Resolution: action taken to repair the root cause of an incident/problem or to implement a workaround.

Workaround: reducing/eliminating the impact of an incident/problem for which a full resolution is not yet available.

🌡Performance Management: Mastery (Part II: First Time Right - The Aim toward Operational Excellence)

 

Performance Management Series

Rooted in Six Sigma methodology as a step toward operational excellence, First Time Right (FTR) implies that any procedure is performed in the right manner the first time and every time. It equates to minimizing the waste in its various forms (inventory, motion, overprocessing, overproduction, waiting, transportation, defects). Like many quality concepts from the manufacturing industry, the concept was transported in the software development process as principle, process, goal and/or metric. Thus, it became part of Software Engineering, Project Management, Data Science, and any other similar endeavors whose outcome results in software products. 

Besides the quality aspect, FTR is rooted also in the economic imperative – the need to achieve something in the minimum amount of time with the minimum of effort. It’s about being efficient in delivering a product or achieving a given target. It can be associated with continuous improvement, learning and mastery, the aim being to encompass FTR as part of organization’s culture. 

Even if not explicitly declared, FTR lurks in each task planned. It seems that it became common practice to plan with the FTR in mind, however between this theoretical aim and practice there’s as usual an important gap. Unfortunately, planners, managers and even tasks' performers often forget that mistakes are made, that several iterations are needed to get the job done. It starts with the communication between people in clarifying the requirements and ends with the formal sign off. All the deviations from the FTR add up in the deviations between expected and actual effort, though probably more important are the deviations from the plan and all the consequences deriving from it. Especially in complex projects this adds up into a spiral of issues that can easily reinforce themselves. 

Many of the jobs that imply creativity, innovation, research or exploration require at least several iterations to get the job done and this is independent of participants’ professionalism and experience. Moreover, the more quality one needs, the higher the effort, the 80/20 being sometimes a good approximation of the effort needed. In extremis, aiming for perfection instead of excellence can make certain tasks a never-ending story. 

Achieving FTR requires practice - the more novelty, the higher the complexity, the communication or the synchronization needs, the more practice is needed. It starts with the individual to master the individual tasks and ends with the team, where communication, synchronization and other aspects need to be considered. The practice is usually achieved on hands-on work as part of the daily duties, project work, and so on. Unfortunately, it’s based primarily on individual experience, and seldom groomed in advance, as preparation for future tasks. That’s why sometimes when efficiency is needed in performing critical complex tasks, one also needs to consider the learning curve in achieving the required quality. 

Of course, many organizations demand from job applicants experience and, when possible, they hire people with experience, however the diversity, complexity and changing nature of tasks require further practice. This aspect is somehow recognized in the implementation in organizations of the various forms of DevOps, though how many organizations adopt it and enforce it on a regular basis? Moreover, a major requirement of nowadays businesses is to be agile, and besides the mere application of methodologies, being agile means to have also a FTR mindset. 

FTR starts with the wish for mastery at individual and team level and, with the right management attention, by allocating time for learning, self-development in the important areas, providing relevant feedback and building an infrastructure for knowledge sharing and harnessing, FTR can become part of organization’s culture. It’s up to each of us to do it!

🧭🪄Business Intelligence: Power BI (Part I: Power BI's Learning Curve I)

A learning curve attempts depicting the (average) time it takes a person to learn how to use a method, tool, or technique, tracing the path from newbie to mastery. A common definition of the learning curve is based on the correlation between a learner’s performance on a task or activity and the number of attempts or amount of time required to complete it.

There are several diagrams in circulation which depict the correlation between the difficulty of Power BI concepts and probably their implementation as functionality. Even if they reflect to some degree the rate of learning, their simplicity and fuzziness can easily make one question their accuracy in reflecting the reality.

Researchers tend to categorize the curves associated with the learning process in simple idealized patterns like S-curve (aka sigmoid), exponential growth, exponential rise and fall to limit, or power law, however the learning process in IT-based endeavors is seldom characterized by a linear or exponential curve, given that the tasks seldom allow a steady path. The jumps of knowledge between tasks can be wide enough to appear insurmountable, and they can prove to be quite of a challenge without some help.

Like a baby’s first steps, we, as learners, must learn first to crawl, before making some unsteady steps, and it can take long time until visible progress is made. It’s a slow progress until we suddenly hit a (tipping) point from which everything seems easy, fact that increases our confidence in us. On the other side, when we find that we make no visible progress for a long period, it’s easy to arrive to the opposite, a critical zone, which in extremis could make one lose interest.

As beginners, after the first tipping point on the learning journey, it’s easy to arrive at a plateau in which there seem no need to learn new things, the current knowledge allowing to handle a range of tasks of small to average complexity. This can last for a long time, and then, a big thing comes our way – a hard problem to solve or a concept hard to understand. It’s the point where we stagnate, and the deeper we go, and the more such challenges are thrown in our way, the more difficult the learning seems to be. However, with new understanding, small steps are made, one step after the other, the pace makes us to evolve faster until we reach again a critical point from which the process increases smoothly until we seem to stagnate again. We meet again a hard limit to growth, which seems to be more solid than the previous one.

Both limits to growth can appear to be hard, however, considering that the knowledge in the field expands, more opportunity for growth appear, thus, the limits are apparent. Even if knowledge tends to increase ‘indefinitely’, the limits are there in terms of complexity, time available, knowledge quality (incl. availability) or any other dimension of the learning process. Moreover, these successions of tipping points, growth limits, plateaus, critical, steady and fast progress zones can occur in several iterations in the learning path. Thus, the path seems to resemble a snakelike curve with many ups and downs.

For the learner is important to be aware of this last aspect, there are always ups and downs, taking effort, patience and maybe an expert’s help to bridge the gaps in between. The chances are high that the gap between what we think we know and what we know is considerable, therefore a reality check is useful from time to time. A new problem to tackle will provide that occasion!

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💼Project Management: Lean Management (Part I: Between Value and Waste I - An Introduction)

 

Independently on whether Lean Management is considered in the context of Manufacturing, Software Development (SD), Project Management (PM) or any other business-related areas, there are three fundamental business concepts on which the whole scaffolding of the Lean philosophies is built upon, namely the ones of value, value stream and waste. 

From an economic standpoint, value refers to the monetary worth of a product, asset or service (further referred as product) to an organization, while from a qualitative perspective, it refers to the perceived benefit associated with its usage. The value is thus reflected in the costs associated with a product’s delivery (producer’s perspective), respectively the price paid on acquiring it and the degree to which the product can fulfill a demand (customer’s perspective).

Without diving too deep into theory of product valuation, the challenges revolve around reducing the costs associated with a product’s delivery, respectively selling it to a price the customer is willing to pay for, typically to address a given set of needs. Moreover, the customer is willing to pay only for the functions that satisfy the needs a product is thought to cover. From this friction of opposing driving forces, a product is designed and valued.

The value stream is the sequence of activities (also steps or processes) needed to deliver a product to customers. This formulation includes value-added and non-value-added activities, internal and external customers, respectively covers the full lifecycle of products and/or services in whatever form it occurs, either if is or not perceived by the customers.  

Waste is any activity that consumes resources but creates no value for the customers or, generally, for the stakeholders, be it internal or external. The waste is typically associated with the non-added value activities, activities that don’t produce value for stakeholders, and can increase directly or indirectly the costs of products especially when no attention is given to it and/or not recognized as such. Therefore, eliminating the waste can have an important impact on products’ costs and become one of the goals of Lean Management. Moreover, eliminating the waste is an incremental process that, when put in the context of continuous improvement, can lead to processes redesign and re-engineering.

Taiichi Ohno, the ‘father’ of the Toyota Production System (TPS), originally identified seven forms of waste (Japanese: muda): overproduction, waiting, transporting, inappropriate processing, unnecessary inventory, unnecessary/excess motion, and defects. Within the context of SD and PM, Tom and Marry Poppendieck [1] translated the types of wastes in concepts closer to the language of software developers: partially done work, extra processes, extra features, task switching, waiting, motion and, of course, defects. To this list were added later further types of waste associated with resources, confusion and work conditions.

Defects in form of errors and bugs, ineffective communication, rework and overwork, waiting, repetitive activities like handoffs or even unnecessary meetings are usually the visible part of products and projects and important from the perspective of stakeholders, which in extremis can become sensitive when their volume increases out of proportion.

Unfortunately, lurking in the deep waters of projects and wrecking everything that stands in their way are the other forms of waste less perceivable from stakeholders’ side: unclear requirements/goals, code not released or not tested, specifications not implemented, scrapped code, overutilized/underutilized resources, bureaucracy, suboptimal processes, unnecessary optimization, searching for information, mismanagement, task switching, improper work condition, confusion, to mention just the important activities associated to waste.

Through their elusive nature, independently on whether they are or not visible to stakeholders, they all impact the costs of projects and products when the proper attention is not given to them and not handled accordingly.

References:
[1] Mary Poppendieck & Tom Poppendieck (2003) Lean Software Development: An Agile Toolkit, Addison Wesley, ISBN: 0-321-15078-3

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]