🪙Business Intelligence: Data Integration (Just the Quotes)

"The chaos without a data strategy is not as obvious, but the indicators abound: dirty data, redundant data, inconsistent data, the inability to integrate, poor performance, terrible availability, little accountability, users who are increasingly dissatisfied with the performance of IT, and the general feeling that things are out of control." (Sid Adelman et al, "Data Strategy", 2005)

"You cannot boil the ocean; you have to prioritize your data integration deliverables. An enterprise-wide data integration effort must be carved up into small iterative projects, starting with the most critical data and working down to the less significant data. The business people working with the data integration team must determine which data is most appropriate for integration. Some data might not be suitable for integration at all, such as department-specific data, highly secured data, and data that is too risky to integrate. The team also needs to look at historical data and decide how much of it to include in the data integration process." (Sid Adelman et al, "Data Strategy" 1st Ed., 2005)

"Many organizations need to create data warehouses - massive data stores of timeseries data for decision support. Data are imported from various external and internal resources and are cleansed and organized in a manner consistent with the organization’s needs. After the data are populated in the data warehouse, data marts can be loaded for a specific area or department. Alternatively, data marts can be created first, as needed, and then integrated into an EDW." (Ramesh Sharda et al, "Business Intelligence: A Managerial Perspective on Analytics" 3rd Ed., 2014)

"Unfortunately, some organizations are replicating the bad data warehouse practice by creating special-purpose data lakes - data lakes to address a specific business need. Resist that urge! Instead, source the data that is needed for that specific business need into an 'analytic sandbox' where the data scientists and the business users can collaborate to find those data variables and analytic models that are better predictors of the business performance. Within the 'analytic sandbox', the organization can bring together (ingest and integrate) the data that it wants to test, build the analytic models, test the model's goodness of fit, acquire new data, refine the analytic models, and retest the goodness of fit." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"Data warehousing, as we are aware, is the traditional approach of consolidating data from multiple source systems and combining into one store that would serve as the source for analytical and business intelligence reporting. The concept of data warehousing resolved the problems of data heterogeneity and low-level integration. In terms of objectives, a data lake is no different from a data warehouse. Both are primary advocates of terms like 'single source of truth' and 'central data repository'." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"The data warehouse approach is also referred to as data marts with the usual distinction that a data mart serves a single department in an organization, while a data warehouse serves the larger organization integrating across multiple departments. Regardless of their scope, from the architectural modeling perspective they both have similar characteristics." (Zhamak Dehghani,"Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Since data engineering is such a crucial field, you may be wondering who the main players are and what skill sets they possess. Building a data product involves several folks, all of whom need to come together with seamless handoffs to ensure a successful end product or service is created. It would be a mistake to create silos and increase both the number and complexity of integration points as each additional integration is a potential failure point." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"A data strategy must align with the business goals and overall framework of how data will be used and managed within an organization. It needs to include standards for how data will be discovered, integrated, accessed, shared, and protected. It needs to address how data will meet regulatory compliance policies, Master Data Management, and data democratization. There needs to be an assurance that both data and metadata have a quality control framework in place to achieve data trust. A data strategy needs to have a clear path on how an organization will accomplish data monetization." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"An enterprise data warehouse is a central repository of integrated and transformed, structured data from disparate sources and used for reporting and data analysis." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Consumability is a very important requirement because it will directly affect the experience domain consumers will have in a streaming data mesh. If other domains cannot easily consume streaming data products, then they may opt out of the streaming data mesh and decide to build their own integrations by hand, bypassing any issues they encounter with the data mesh. Some factors to consider when ingesting data derivatives that will affect the consumability of other domains are as follows: (*) Lack of scalability (*) Lack of interoperability" (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Data Mesh advocates for domain-driven ownership of data, enabling individual teams to manage and share their data effectively while aligning with the overall organizational objectives. By embracing this paradigm, enterprises can gradually dismantle the barriers of data silos, paving the way for a more integrated, agile, and data-centric organizational culture." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Observability [...] requires that systems be instrumented to expose rich telemetry, enabling ad hoc exploration and hypothesis testing regarding system health. Thus, observability demands design considerations at the architecture level, insisting on standardization of instrumentation, consistent metadata management, and tight integration across data processing, storage, and orchestration layers." (William Smith, "Soda Core for Modern Data Quality and Observability: The Complete Guide for Developers and Engineers", 2025)

🤖〽️Prompt Engineering: Copilot Unabridged (Part 177: How AI Can Detect Multi‑Step Relationships That Span Many Variables)

Prompt Engineering Series

Prompt: "write a post of 600 words on how AI can detect multi‑step relationships that span many variables"

Introduction

One of the most remarkable capabilities of modern Artificial Intelligence (AI) is its ability to uncover multi‑step relationships that span across many variables - patterns so complex, layered, and indirect that they remain invisible to human reasoning. Humans excel at intuitive leaps and conceptual understanding, but we struggle when relationships unfold across multiple dimensions, involve subtle dependencies, or require tracking interactions over long chains of influence. AI, by contrast, thrives in precisely these environments. Its computational architecture allows it to map relationships that are not linear, not obvious, and not easily captured by traditional analytical methods.

The first reason AI can detect multi‑step relationships is its ability to operate in high‑dimensional spaces. Humans can mentally juggle only a handful of variables at once before cognitive overload sets in. But many real‑world systems - biological networks, financial markets, climate dynamics - operate across hundreds or thousands of interacting factors. AI models, especially deep neural networks, are designed to navigate these high‑dimensional landscapes. They can track how small changes in one variable propagate through a chain of dependencies, eventually influencing outcomes far removed from the original cause. This ability to follow long causal threads is something human intuition simply cannot replicate at scale.

A second advantage lies in AI’s capacity to model nonlinear interactions. Multi‑step relationships often involve nonlinearities: effects that amplify, dampen, or transform as they move through a system. Humans tend to assume linearity because it is cognitively simple. AI does not make this assumption. Neural networks, decision trees, and attention‑based architectures can capture nonlinear transformations at every layer. This allows AI to detect relationships where the influence of one variable depends on the state of several others - patterns that only emerge when multiple conditions align in specific ways.

Another key factor is AI’s ability to learn hierarchical representations. Deep learning models build understanding layer by layer. Early layers detect simple features; deeper layers combine these features into more abstract concepts. This hierarchical structure mirrors the multi‑step nature of complex relationships. For example, in medical diagnostics, an AI system might first detect subtle biomarkers, then combine them into intermediate patterns, and finally infer a higher‑level diagnosis. Each step builds on the previous one, allowing the model to trace relationships that unfold across multiple conceptual levels.

AI also excels at temporal reasoning, which is essential for detecting multi‑step relationships that evolve over time. Recurrent neural networks, transformers, and sequence models can track dependencies across long time horizons. They can identify how an event today influences outcomes weeks or months later, even when the connection is indirect. This is particularly valuable in fields like supply chain forecasting, epidemiology, and macroeconomic modeling, where delayed effects are the norm rather than the exception.

A further strength comes from AI’s ability to integrate heterogeneous data sources. Multi‑step relationships often span different types of information - numerical data, text, images, signals, or categorical variables. Humans struggle to synthesize such diverse inputs. AI systems, however, can fuse them into a unified representation. This multimodal integration allows AI to detect relationships that cross boundaries between data types, revealing patterns that would remain hidden if each source were analyzed in isolation.

Finally, AI’s ability to detect multi‑step relationships is amplified by continuous learning and iterative refinement. As new data arrives, AI systems can update their internal models, strengthening or revising the relationships they have inferred. This dynamic adaptation allows them to track evolving systems where relationships shift over time. Humans, by contrast, tend to cling to outdated mental models, even when the underlying reality has changed.

AI’s ability to detect multi‑step relationships across many variables is not a replacement for human judgment. Instead, it expands our analytical reach, revealing structures that lie beyond the limits of intuition. When humans and AI collaborate - combining human understanding with machine‑level pattern detection - we gain a deeper, more accurate view of the complex systems that shape our world.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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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 (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: A One-Man Show (Part V: Focus on the Foundation)

Business Intelligence Suite

I tend to agree that one person can't do anymore "everything in the data space", as Christopher Laubenthal put it his article on the topic [1]. He seems to catch the essence of some of the core data roles found in organizations. Summarizing these roles, data architecture is about designing and building a data infrastructure, data engineering is about moving data, database administration is mainly about managing databases, data analysis is about assisting the business with data and reports, information design is about telling stories, while data science can be about studying the impact of various components on the data. 

However, I find his analogy between a college's functional structure and the core data roles as poorly chosen from multiple perspectives, even if both are about building an infrastructure of some type. 

Firstly, the two constructions have different foundations. Data exists in a an organization also without data architects, data engineers or data administrators (DBAs)! It's enough to buy one or more information systems functioning as islands and reporting needs will arise. The need for a data architect might come when the systems need to be integrated or maybe when a data warehouse needs to be build, though many organizations are still in business without such constructs. While for the others, the more complex the integrations, the bigger the need for a Data Architect. Conversely, some systems can be integrated by design and such capabilities might drive their selection.

Data engineering is needed mainly in the context of the cloud, respectively of data lake-based architectures, where data needs to be moved, processed and prepared for consumption. Conversely, architectures like Microsoft Fabric minimize data movement, the focus being on data processing, the successive transformations it needs to suffer in moving from bronze to the gold layer, respectively in creating an organizational semantical data model. The complexity of the data processing is dependent on data' structuredness, quality and other data characteristics. 

As I mentioned before, modern databases, including the ones in the cloud, reduce the need for DBAs to a considerable degree. Unless the volume of work is big enough to consider a DBA role as an in-house resource, organizations will more likely consider involving a service provider and a contingent to cover the needs. 

Having in-house one or more people acting under the Data Analyst role, people who know and understand the business, respectively the data tools used in the process, can go a long way. Moreover, it's helpful to have an evangelist-like resource in house, a person who is able to raise awareness and knowhow, help diffuse knowledge about tools, techniques, data, results, best practices, respectively act as a mentor for the Data Analyst citizens. From my point of view, these are the people who form the data-related backbone (foundation) of an organization and this is the minimum of what an organization should have!

Once this established, one can build data warehouses, data integrations and other support architectures, respectively think about BI and Data strategy, Data Governance, etc. Of course, having a Chief Data Officer and a Data Strategy in place can bring more structure in handling the topics at the various levels - strategical, tactical, respectively operational. In constructions one starts with a blueprint and a data strategy can have the same effect, if one knows how to write it and implement it accordingly. However, the strategy is just a tool, while the data-knowledgeable workers are the foundation on which organizations should build upon!

"Build it and they will come" philosophy can work as well, though without knowledgeable and inquisitive people the philosophy has high chances to fail.

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Resources:
[1] Christopher Laubenthal (2024) "Why One Person Can’t Do Everything In Data" (link)

💫⚙️ERP Systems: Microsoft Dynamics 365's Invoice Capture (Setup Areas)

ERP Systems

When implementing the Invoice Capture process, there are several setup areas that need to be considered into the Power App (Channels & Mapping Rules), some that need to be done in Dynamics 365 for Finance and Operations (D365 F&O) and impact the integration with the Power App (Expense types/Procurement categories, Financial dimensions, Approval workflow), respectively setup that concerns only D365 F&O but still important. In addition, there are also overlapping features like Recurring Vendor Invoice Templates and E-Invoicing.

This post focuses on the first two types of setup areas, the rest following  in a second post. 

Channels

In the digitalization era, it's expected that most of the Vendor invoices are received by email or an integrated way (see e-Invoicing). However there can be also several exceptions (e.g. small companies operating mostly offline).

In Invoice Capture can be defined one or more channels for Outlook, SharePoint or OneDrive on which Invoices can be imported in the App. In a common scenario, depending on the number of mailboxes intended for use, one or more channels can be set up for Outlook. Power App listens on the respective channels for incoming emails in the Inbox, in a first step processes the emails by importing all the attachments (including signature images), while in a second step processes all the documents that look like an Invoice (including Receipts) and extracts the metadata to create the Invoice record. The Invoices received in hard-paper format can be scanned and sent to one of the internal mailboxes, respectively imported manually in Invoice Capture. 

More likely, there will be also a backlog that can be imported via SharePoint or OneDrive, which is more convenient than resending the backlog by email. In the end it should make no difference for the process which channels is used as long as the Invoices are processed in a timely manner. It should make also no difference if for example in UAT were used other channels than in Production. For testing purposes it might be advantageous to have more control over which Invoices are processed, while the UAT could follow the same setup as Prod, which is generally recommended.

Mapping Rules

The mapping rules allow on one side to set default values based on a matching string checked against several attributes (e.g. Company name, Address or Tax registration number for Legal entity, Item number for Item, Item description for Expense type). Secondly, they allow to also define a configuration for a rule, which defines what fields are mandatory, respectively which Invoice types are supported, etc.

The mapping rules will not cover all scenarios, though it's enough if they cover a good percentage from the most common cases. Therefore, over time they are also good candidates for further optimization. Moreover, because Invoice Capture remembers the values used before for an instance of the same Invoice, the mapping rules will be considered only for the first occurrence of the respective Invoice or whatever is new in its processing. 

If a channel was defined for each Legal entity, this seems to make obsolete the definition of mapping rules for it. Conversely, if the number of manual uploads is not neglectable, it still makes sense to define a mapping rule. 

Mapping rules for the Expense type seem to work well when Items' descriptions are general enough to include certain words (e.g. licenses, utilities). 

One can define mapping rules also for the Vendor accounts and Items, though it's questionable whether the effort makes sense as long as the internal Vendor names and Product numbers don't deviate from the ones used by the Vendor itself.

Expense Types

Invoice Capture requires that either the Item or the Expense type are provided on the line. For PO-based invoices, an Item should be available. Cost invoices can have Items as well and they can be used on the line, though from the point of view of the setup it might be easier to use Expense types. It's the question whether the information loss has any major impact on the business. There are also cases in which the lines don't bring any benefit and can be thus in Invoice capture deleted. 

At least for Cost invoices, the Expense types (aka Procurement categories in Dynamics 365) defined can considerably facilitate the automatic processing. D365 F&O can use the Procurement category to automatically populate the Main Account in the Invoice distributions. The value is used as default and can be overwritten, if needed. 

Having for example a 1:1 mapping between Procurement categories and Main accounts, respectively the same names can make easier the work of AP Clerks and facilitate the troubleshooting.

Conversely, one can define an additional level of detail (aka an additional segment) for reporting purposes. This implies that multiple categories will point to the same Main account, which can increase the overhead, though the complexity of the structure can be simplified by using maybe a good naming convention and a consolidated Excel list with the values. The overhead resumes mainly when dealing with the first instance of an Vendor invoice.

On Procurement categories can be defined also the default Item sales tax groups (a 1:1 mapping) which can be overwritten as well. For the categories with multiple Items sales tax groups, one should decide whether the benefit of providing a default value outweighs the effort for adding the value for each Invoice line. 

Defining upfront, before the Go Live, a good hierarchical structure for the Procurement categories and the mappings to the Main accounts, respectively to the Item sales tax groups can reduce the effort of maintaining the structure later and reduces the reporting overhead. 

Financial Dimensions & the Vendor Invoice Approval Workflow

Besides their general use, the Financial dimensions can be used to implement an approval process on D365 F&O side by configuring an expenditure reviewer (see [2]) and using it in the Vendor Invoice Approval workflow, respectively of setting up the financial owners for each dimension in scope. Different owners for the Financial dimensions can be defined for each Legal entity via Legal entity overrides. From what it seems, notifications are sent then to the override as well to the default owner. 

Starting with the 1.1.0.32 (07-Nov-2023) version of Invoice capture, respectively the 10.0.39 version of D365 F&O (planned for Apr-2024), 3 financial dimensions (Cost center, Department and Legal Entity) are supported directly in the App. This would allow us to cover the example covered in [2]). This reduces the need for maintaining the values in D365 F&O. 

Unfortunately, if the approval process needs to use further dimensions (e.g. Vendor, Location) or attributes (e.g. Invoice responsible person) in the approval, one needs either to compromise or find workarounds. If there's no Purchase order as in the case of Cost invoices, involving the actual Buyer is almost impossible. For such extreme cases one needs more flexibility in the approval process and hopefully Microsoft will extend the functionality behind it.

The approval processes needed by customers require occasionally a complexity that's not achievable with the functionality available in D365 for F&O. The customers are forced then either to compromise or use external tools (e.g. Power Automate) for building the respective functionality. 

One should consider defining default Financial dimensions on the Vendor, the respective values being used when generating the Invoice in D365 F&O. Defining Financial dimensions templates can help as well when the costs need to be split across different Financial dimensions based on percentages. 

In case the financial dimensions differ across a same Vendor's invoices, one can request from the Vendors to provide the respective information on the Invoice. 

Organization Administration

Cost invoices can work without providing a Unit of Measure (UoM) in Invoice capture, however one should consider using an UoM at least for aesthetic purposes in reporting. On the other side, this can complicate the setup if the same UoM is used for other purposes. 

Vendor Invoice Automation

To be able to process automatically the Vendor invoices once they arrived in D365 F&O, it would useful to maintain the attributes which are modified manually in D365 F&O directly in Invoice Capture. The usual fields are the following:

• Invoice description (header)
• Financial dimensions 
• Sales item tax group

Master Data Management

There are master data attributes which even if they are not directly involved in the process, they could make process actor's life a bit easier. It can be the case of the Responsible person and/or the Buyer group, which would allow the AP Clerks to identify the Cost Center and Department related to the Invoice. Maintaining the external Item descriptions for Vendors can help as well in certain scenarios. 

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Resources:
[1] Microsoft Learn (2023) Invoice capture overview (link)
[2] Microsoft Learn (2023) Configure expenditure reviewers (link)

🧭Business Intelligence: New Technologies, Old Challenges (Part II - ETL vs. ELT)

 

Data lakes and similar cloud-based repositories drove the requirement of loading the raw data before performing any transformations on the data. At least that’s the approach the new wave of ELT (Extract, Load, Transform) technologies use to handle analytical and data integration workloads, which is probably recommendable for the mentioned cloud-based contexts. However, ELT technologies are especially relevant when is needed to handle data with high velocity, variance, validity or different value of truth (aka big data). This because they allow processing the workloads over architectures that can be scaled with workloads’ demands.

This is probably the most important aspect, even if there can be further advantages, like using built-in connectors to a wide range of sources or implementing complex data flow controls. The ETL (Extract, Transform, Load) tools have the same capabilities, maybe reduced to certain data sources, though their newer versions seem to bridge the gap.

One of the most stressed advantages of ELT is the possibility of having all the (business) data in the repository, though these are not technological advantages. The same can be obtained via ETL tools, even if this might involve upon case a bigger effort, effort depending on the functionality existing in each tool. It’s true that ETL solutions have a narrower scope by loading a subset of the available data, or that transformations are made before loading the data, though this depends on the scope considered while building the data warehouse or data mart, respectively the design of ETL packages, and both are a matter of choice, choices that can be traced back to business requirements or technical best practices.

Some of the advantages seen are context-dependent – the context in which the technologies are put, respectively the problems are solved. It is often imputed to ETL solutions that the available data are already prepared (aggregated, converted) and new requirements will drive additional effort. On the other side, in ELT-based solutions all the data are made available and eventually further transformed, but also here the level of transformations made depends on specific requirements. Independently of the approach used, the data are still available if needed, respectively involve certain effort for further processing.

Building usable and reliable data models is dependent on good design, and in the design process reside the most important challenges. In theory, some think that in ETL scenarios the design is done beforehand though that’s not necessarily true. One can pull the raw data from the source and build the data models in the target repositories.

Data conversion and cleaning is needed under both approaches. In some scenarios is ideal to do this upfront, minimizing the effect these processes have on data’s usage, while in other scenarios it’s helpful to address them later in the process, with the risk that each project will address them differently. This can become an issue and should be ideally addressed by design (e.g. by building an intermediate layer) or at least organizationally (e.g. enforcing best practices).

Advancing that ELT is better just because the data are true (being in raw form) can be taken only as a marketing slogan. The degree of truth data has depends on the way data reflects business’ processes and the way data are maintained, while their quality is judged entirely on their intended use. Even if raw data allow more flexibility in handling the various requests, the challenges involved in processing can be neglected only under the consequences that follow from this.

Looking at the analytics and data integration cloud-based technologies, they seem to allow both approaches, thus building optimal solutions relying on professionals’ wisdom of making appropriate choices.

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🧮ERP: Panning (Part IV: It’s all about Planning - An Introduction)

Ideally the total volume of work can be uniformly distributed for all project’s duration though in praxis the curve representing the effort has the form of a wave or aggregation of waves that tend to reach the peak shortly before or during the Go-Live(s). More important, higher fluctuations occur in the various areas of the project on whole project’s duration, as there are dependencies between the various functional areas, as one needs to wait for decisions to be made, people are not available, etc. Typically, the time wasted on waiting, researching or other non-value-added activities have the potential of leading to such peaks. Therefore, the knowledge must be available, and decisions must be taken when required, which can be challenging but not impossible to achieve. 

To bridge the time wasted on waiting, the team members need to work on other topics. If on customer’s side the resources can handle maybe other activities, on vendor’s side the costs can be high and proportional with the volume of waiting. Therefore, vendor’s resources must be involved at least in two projects or do work in other areas in advance, which is not always possible. However, when vendor’s resources are involved in two or more projects, unless the planning is perfect or each resource can handle the work as it comes, there are further waiting times added. The customer is then forced either to book the resources exclusively, or to wait and carry the costs associated with it. 

On the other side ERP Implementations tend to become exploration projects, especially when the team has only partial knowledge about the system, or the requirements have a certain degree of specialization that deviates from the standard processes. The more unknowns an ERP implementation has, the more difficult is to plan. To be able to plan one must know the activities ahead, how long they take, and of course, one must adhere to the delivery dates, because each delay can have a cascading effect that can impact project’s schedule considerably. 

Probably the best approach to planning is to group the activities into packages and plan the packages, being in each subteam’s duty to handle the planning for each package, following to manage at upper level only the open issues, risks or opportunities. This shifts the burden from Project Manager’s shoulders within the project. Moreover, even if in theory the plan can consider each activity, it will become obsolete as soon it’s updated given the considerable volume of work requested to maintain it. Periodically, one can still revise the whole plan to identify opportunities and risks. What the team can do is to plan for a certain time interval (e.g. 4-6 weeks) and build from there. This allows focusing on the most important activities. 

To further shift the burden, activities like Data Migration, Data Cleaning or the integrations with third party systems should be treated when possible as subprojects. Despite having interdependencies with the main project (e.g. parameters, master data, decisions) and share same resources, they have their own schedule whose deadlines need to be aligned with main project’s milestones. 

Unless the team and business put all effort to respect the plan and, as long the plan is realistic, the initial plan can seldom be respected – it’s anyway just a sketch of the road ahead that can change as the project progresses – and this aspect needs to be understood by the business otherwise will lead to false expectations. On the other side, the team must try respecting the deadlines and communicate in time inability to do so. It’s an interplay in which communication is more important than ever.

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🧊Data Warehousing: ETL (Part III: The Extract Subprocess)

 

Data Warehousing Series

As part of the ETL process with applicability to Data Warehousing, Data Migrations, Data Integrations or similar scenarios the extraction subprocess is responsible for preparing and implementing the logic required to extract the data from the various source systems at the required level of detail. The extraction is done typically based on SQL queries as long one deals with relational databases or any OLEDB or ODBC-based data repositories including flat or MS Office files.

One can consider the preparation of the extraction logic as separate design subprocess of the targeted solution. Even if high-level design decisions are considered at the respective level, the low-level design needs to be considered at ETL package level. As part of the process are identified the source of the data in terms of system, tables and attributes to be imported, as well the joins, business and transformation rules that need to be applied on the data. This can involve reengineering the logic from the source system(s) as well data profiling, discovery or exploration activities.

A common practice is to copy the source tables 1:1 into the solution, eventually by considering only the needed attributes to minimize the necessary space, loading time and content’s complexity, even if this would add more effort into the design phase to identify only the needed attributes. If further attributes are identified at a later stage, the packages need to be modified accordingly. If the data volume or the number of unnecessary attributes is neglectable, copying the table 1:1 could prove to be the best strategy.

A second approach is to model within the extraction the (business) entity as designed within the source system. For example, the entity could be split over multiple tables from design or other purposes. Thus, the extraction query will attempt modeling the entity. This approach reduces to some degree the number of tables from the targeted solution, as well the number of ETL packages involved, while providing a clear depiction of the entities involved.

A third approach is to extract the data as needed by the target system, eventually as a mix between master and transaction data, fact which could easily lead to data redundancy with different timeliness and all the consequences resulting from this. This approach is usually met in solutions which require fast data availability in the detriment of design.

Unfortunately, there can be design constraints or choice considerations that could lead to a mix between these approaches. If the impact caused by the mix between the first two approaches is minimal, the third approach can cause more challenges, though it might be a small price to pay as long the considered data are disconnected from other data.

To reduce the redundancy of data, it’s recommended to consider as goal creating a unique source of facts, which can be obtained by minimizing as much as possible the overlaps between tables, respectively entities. Ideally there should be no overlaps. On the other sides the overlaps can be acceptable when the same data are available in more systems and the solution requires all the data to be available.

If the above approaches consider the vertical partitioning of the data, there can be also horizontal partitioning needs especially when a subset of the data is needed or when is needed to partition the data based on a set of values. In addition, one might be forced to include also transformation rules directly into the extraction logic, for example to handle conversion issues or minimize certain design overhead early in the process. In practice it makes sense to link such choices to business rules and document them accordingly.

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🧊Data Warehousing: ETL (Part II: An Introduction)

 

ETL (Extract, Transform, Load) processes, technologies or tools are about extracting data from one or more data sources via a set of queries, performing changes on the data via conversions, aggregations, mappings or other types of transformations, respectively loading the data into target tables or other type of repositories. Thus, an ETL process allows moving and transforming data between predefined data structures on an ad-hoc basis or as part of stable repetitive processes, which makes ETL ideal for data warehousing, data integrations, data migrations or similar scenarios. 

Extract: The extraction of data is done typically based on SQL queries from relational databases or any OLEDB or ODBC-based data repositories including flat or MS Office files, though modern ETL tools can support other type of queries (CAML, XQuery, DAX) or even NoSQL architectures (Handoop). This allows addressing a wide range of requirements, the complexity of the logic depending on the functionality provided by the query languages, respectively the extraction functionality available.  

Transform: The transformation logic can be implemented based on the functionality provided by the ETL tool, and can involve after case any combination of aggregates, conditional splits, merges, lookups, multicasts, pivoting/unpivoting, cleansing, data conversions, sampling, mapping or any other transformations that can be performed on an in-transit dataset. On the other side, quite often the same can be achieved with the help of SQL-based manipulations directly in the extraction logic or later in the process. SQL can prove to be occasionally faster and more flexible than the transformations provided by the ETL tool, however despite the overlaps, the two approaches can complement each other when used adequately. 

Load: The load is usually just a dump of the data into one or more final or intermediary tables with predefined structures. Unless the data don’t match the data type, format or further defined constraints, the load seldom involve further challenges as long the solution was designed adequately. 

Within the logical model, extract, transform and load can be considered as process by themselves. Within the object model provided by the ETL tool, they are considered in the mentioned sequence within a data flow, which within a set of workflow constraints defines how the data move through the pipeline – the sequence of processing steps considered. The basic unit of work is the data flow and the workflow it belongs to, unit that can be encapsulated in one container for easier management or simply convenience. Several containers can be linked within a workflow to create more complex behavior. 

The data flows and workflow constraints, together with the supporting connections and containers form an ETL package, the main unit of work for encapsulating and running ETL logic. ETL packages are scheduled and run as fit for the purpose.

With the right design, these building blocks allow enough flexibility in handling ad-hoc requests or of building complex solutions. This involves decisions on how to partition the ETL packages, respectively the data flows, in which order they should be run, where and in which sequence the data should be transformed, how to handle exceptions, how to build eventually intermediary data repositories, how to handles audit requirements, and so on. Each of these choices can prove to be important. 

The knowledge of the ETL architecture and functionality is quintessential in providing the right solution for the problem considered, however once the basics were understood the challenges typically reside in understanding the source and/or target structures, the logical and physical entities available, identify the way the data can be partitioned horizontally or vertically, respectively what type of transformations are required for moving the data, as required by the solution. 

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𖣯🧮Strategic Management: Simplicity VI (ERP Implementations' Story II)

Besides the witty sayings and theories advanced in defining what simplicity is about, life shows that there’s a considerable gap between theory and praxis. In the attempt at a definition, one is forced to pull more concepts like harmony, robustness, variety, balance, economy, or proportion, which can be grouped under organic unity or similar concepts. However, intuitionally one can advance the idea that from a cybernetic perspective simplicity is achieved when the information flows are not disrupted and don’t meet unnecessary resistance. By information here are considered the various data aggregations – data, information, knowledge, and eventually wisdom (aka DIKW pyramid) – though it can be extended to encompass materials, cash and vital energy.

One can go further and say that an organization is healthy when the various flows mentioned above run smoothly through the organization nourishing it. The comparison with the human body can go further and say that a blockage in the flow can cause minor headaches or states that can take a period of convalescence to recover from them. Moreover, the sustained effort applied by an organization can result in fatigue or more complex ailments or even diseases if the state is prolonged. 

For example, big projects like ERP implementations tend to suck the vital energy of an organization to the degree that it will take months to recover from the effort, while the changes in the other types of flow can lead to disruptions, especially when the change is not properly managed. Even if ERP implementations provide standard solutions for the value-added processes, they represent vendors’ perspective into the respective processes, which don’t necessarily fit an organization’s needs. One is forced then to make compromises either by keeping close to the standard or by expanding the standard processes to close the gap. Either way processual changes are implied, which affect the information flow, especially for the steps where further coordination is needed, respectively the data flow in respect to implementation or integration with the further systems. A new integration as well as a missing integration have the potential of disrupting the data and information flows.

The processual changes can imply changes in the material flow as the handling of the materials can change, however the most important impact is caused maybe by the processual bottlenecks, which can cause serious disruptions (e.g. late deliveries, production is stopped), and upon case also in the cash-flow (e.g. penalties for late deliveries, higher inventory costs). The two flows can be impacted by the data and information flows independently of the processual changes (e.g. when they have poor quality, when not available, respectively when don’t reach the consumer in timely manner). 

With a new ERP solution, the organization needs to integrate the new data sources into the existing BI infrastructure, or when not possible, to design and implement a new one by taking advantage of the technological advancements. Failing to exploit this potential will impact the other flows, however the major disruptions appear when the needed knowledge about business processes is not available in-house, in explicit and/or implicit form, before, during and after the implementation. 

Independently on how they are organized – in center of excellence or ad-hoc form – is needed a group of people who can manage the various flows and ideally, they should have the appropriate level of empowerment. Typically, the responsibility resides with key users, IT and one or two people from the management. Without a form of ‘organization’ to manage the flows, the organization will reside only on individual effort, which seldom helps reaching the potential. Independently of the number of resources involved, simplicity is achieved when the activities flow naturally. 

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

𖣯Strategic Management: Strategy Design (Part I: Simple, but not that Simple)

Strategic Management Series

Simplicity of design has been for centuries the wholly grail of architects, while software designers seem to situate themselves in opposition with the trend, as they aim using a mix of technologies that usually increase architecture’s complexity (sometimes the many, the newer and fancier, the better). Unfortunately, despite the implied but not necessarily reachable potential, each component added to an information system or infrastructure has the potential of increasing the overall complexity by a factor proportional to the degree of interactions it creates, respectively by the number of issues it creates or allows to propagate through these interactions.

Conversely, one talks about simplicity in IT without stating what is intended by it, and it can mean many things. Quite often the aim is packed within the ‘keep it simple stupid’ (aka KISS) mantra, a modern and pejorative alternative of Occam’s razor. KISS became a principle in software architecture design, and it can mean that a simple solution works better than a complex one, or that pursuing something in the simplest manner possible is usually better. The nuances are wide enough to cover a wide spectrum of solutions, arriving at statements that the simplest choice to make is the most appropriate one to make, thing that’s not necessarily true in IT, where complexity finds itself home.

Starting with the important number of technologies coexisting in integrations and ending with the exceptions existing in processes or the quality of data, things are almost never as simple as one may wish. An IT infrastructure’s complexity is dependent on the number of existing components, on whether they come from different generations or come from different vendor, on whether are deployed on different operating systems or are supported by different service providers, on the number of customizations made, on the degree of overlapping of the data and integrations needed to keep the data in synch, respectively of the differences existing in data models, quality and use. In general, the more variance, randomness, and challenges one has, the higher the overall complexity.

Paraphrasing Saint Exupéry, in IT simplicity is reached when there is no longer anything to add or anything to take away, or in Hans Hofmann’s words, simplicity is reflected in ‘the ability to simplify means to eliminate the unnecessary so that the necessary may speak’. This refers to the features, what a piece of software can do, respectively the functionality, how a certain outcome is reached, which arrive to be packed in various logical aggregations (function point, functional requirement, story, epic, model, product, etc.) or physical aggregations (classes, components, packages, services, models, etc.). These are the levels at which one needs to address simplicity adequately.

To make something simple one must be able either to design a solution up to the detail that there’s nothing to add or remove, or to start with something and remove or things to reach simplicity. Both approaches involve a considerable effort, time, and multiple iterations, however the first approach can easily become utopian as some architectures are so complex that sooner or later the second approach comes into play. Therefore, one needs in general to focus on what seems an optimal solution and optimize it continuously in further iterations. Aiming for perfection from the beginning or also later in the improvement process is a foolhardy wish.

Even if simplicity is hard to achieve, one can still talk about the elegance of a solution, scenarios in which the various components fit together like the pieces of a puzzle, or about robustness, reliability, correctness, maintainability, (re)usability, or learnability. These latter characteristics are known in Software Engineering as (software) quality attributes.

🧊🎡☯Data Warehousing: SQL Server Integration Services (The Good, the Bad and the Ugly)

Microsoft SQL Server Integration Services (SSIS) is a platform for building (enterprise-level) data integrations and data transformations solutions by using a rich set of built-in tasks and transformations, graphical tools for building packages, respectively a catalog for storing the packages. Formally called Data Transformation Services (DTS), it was introduced with SQL Server 2000 and with SQL Server 2005 it was rebranded as SSIS.

The Good: Since its introduction it was adopted by DBAs and (database) programmers because it allowed the import and export of data on the fly from and to SQL Server, flat files, other relational data sources, in fact any resource exposing a driver for ODBC or OLEDB libraries. The extract/load functionality was extended by a basic set of transformations, making from DTS the ideal ETL tool for data warehousing and integrations. The data from multiple sources and targets could be processed in parallel or sequentially, the ETL logic being encapsulated in one or more packages that could be run manually or scheduled via the SQL Server agent flexibly.

With SQL Server 2005 and further versions the SSIS framework was extended to support further data sources including XML, CAML-based SharePoint lists, OData, Hadoop or Azure Bloob. It allowed the storage of packages on the local storage or within the built-in catalog.

One could thus develop rich ETL functionality without writing a single line of code. In theory the packages could be run and modified also by non-IT users, which can be a plus in certain scenarios. On the other side one could build custom packages programmatically from the beginning, and thus extend the available data processing logic as seemed fit, being able to using existing code and whole libraries embedded into the packages or run via dlls calls .

The Bad: Despite the rich functionality, a data pipeline usually has a lower performance and is more difficult to troubleshoot compared with the built-in RDBMS functionality for data processing. Most, if not all transformations can be handled over SQL-based queries more efficiently as long the data are available on the same SQL Server instance. In addition, SQL provides better code reuse, maintainability, chances for refactoring, scalability and the solutions are easier to deploy. Therefore, one practice resumes in using SSIS only for import/export, the further logic being encapsulated into stored procedures and further database objects. This isn’t necessarily bad, on contrary, though specific expertise is needed then to modify the code.

The Ugly: SSIS is in general suitable for data warehousing and integrations solutions whose logic is ideally stable and well-defined. Therefore, SSIS is less suitable for ERP data migrations or similar task which at least at the beginning have an exploratory nature and an overwhelming complexity, multiple iterations being needed before the requirements were fully identified and understood. In extremis each iteration can involve a redesign, which can prove to be time-consuming. One could in theory attempt first understanding all the data, though this could mean starting the development late in the process, while the data for testing are required much earlier. One can still use SSIS for specific tasks, though implementing a whole solution could imply certain challenges that otherwise could have been avoided.

SSIS is not suitable for real-time complex data integrations which require the processing of a considerable amount of data, when specific architectures like SOA, Restful calls or other solution could be more efficient. When not adequately implemented a data integration can lead to more problems than it can solve. Best example is the increase in execution time with the volume of data, fact that can easily lead to time-outs and locking of data.

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🧊Data Warehousing: Architecture (Part I: Push vs. Pull)

In data integrations, data migrations and data warehousing there is the need to move data between two or more systems. In the simplest scenario there are only two systems involved, a source and a target system, though there can be complex scenarios in which data from multiple sources need to be available in a common target system (as in the case of data warehouses/marts or data migrations), or data from one source (e.g. ERP systems) need to be available in other systems (e.g. Web shops, planning systems), or there can be complex cases in which there is a many-to-many relationship (e.g. data from two ERP systems are consolidated in other systems).  

The data can flow in one direction from the source systems to the target systems (aka unidirectional flow), though there can be situations in which once the data are modified in the target system they need to flow back to the source system (aka bidirectional flow), as in the case of planning or product development systems. In complex scenarios the communication may occur multiple times within same process until a final state is reached.

Independently of the number of systems and the type of communication involved, data need to flow between the systems as smooth as possible, assuring that the data are consistent between the various systems and available when needed. The architectures responsible for moving data between the sources are based on two simple mechanisms - push vs pull – or combinations of them.

A push mechanism makes data to be pushed from the source system into the target system(s), the source system being responsible for the operation. Typically the push can happen as soon as an event occurs in the source system, event that leads to or follows a change in the data. There can be also cases when is preferred to push the data at regular points in time (e.g. hourly, daily), especially when the changes aren’t needed immediately. This later scenario allows to still make changes to the data in the source until they are sent to other system(s). When the ability to make changes is critical this can be controlled over specific business rules.

A pull mechanism makes the data to be pulled from the source system into the target system, the target systems being responsible for the operation. This usually happens at regular points in time or on demand, however the target system has to check whether the data have been changed.

Hybrid scenarios may involve a middleware that sits between the systems, being responsible for pulling the data from the source systems and pushing them into the targets system. Another hybrid scenario is when the source system pushes the data to an intermediary repository, the target system(s) pulling the data on a need basis. The repository can reside on the source, target on in-between. A variation of it is when the source informs the target that a change happened at it’s up to the target to decide whether it needs the data or not.

The main differentiators between the various methods is the timeliness, completeness and consistency of the data. Timeliness refers to the urgency with which data need to be available in the target system(s), completeness refers to the degree to which the data are ready to be sent, while consistency refers to the degree the data from the source are consistent with the data from the target systems.

Based on their characteristics integrations seem to favor push methods while data migrations and data warehousing the pull methods, though which method suits the best depends entirely on the business needs under consideration.

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🗄️Data Management: Data Integration (Part I: From Disintegration to Integration)

Data Management Series

No matter how tight the integration between the various systems or processes there will be always gaps that need to be addressed in one way or another. The problems are in general caused by design errors rooted in the complexity of the logic from the integration layer or from the systems integrated. The errors can range from missing or incorrect validation rules, mappings and parameters to data quality issues.

A unidirectional integration involves distributing data from one system (aka publisher) to one or more systems (aka subscribers), while in bidirectional integrations systems can act as publishers and subscribers, resulting thus complex data flows with multiple endpoints. In simplest integrations the records flow one-to-one between systems, though more complex scenarios can involve logic based on business rules, mappings and other type of transformations. The challenge is to reflect the states as needed by the system with minimal involvement from the users.

Typically, it falls in application/process owners or key users’ responsibilities to make sure that the integration works smoothly. When the integration makes use of interface or staging tables they can be used as starting point for the troubleshooting, however even then the troubleshooting can be troublesome and involve a considerable manual effort. When possible the data can be exported manually from the various systems and matched in Excel or similar solutions. This leads often to personal or departmental solutions hard to maintain, control and support.

A better approach is to automatize the process by importing the data from the integrated systems at regular points in time into the same database (much like in a data warehouse), model the entities and the needed logic in there, and report the differences. Even if this approach involves a small investment in the beginning and some optimization in logic or performance over time, it can become a useful tool for troubleshooting the differences. Such solutions can be used successfully in multiple integration scenarios (e.g. web shop or ERP integrations).

A set of reports for each entity can help identify the differences between the various entities. Starting from the reported differences the users can identify, categorize and devise specific countermeasures for the various issues. The best time to have such a solution is shortly before or during UAT. This would allow to make sure that the integration layer really works, and helps correcting the issues as long they still have a small impact on the systems. Some integration issues might even lead to a postponement of the Go-Live. The second best time is during the time the first important issues were found, as the issues can be used as support for a Business Case for implementing this type of solutions.

In general, it’s recommended to fix the problems in the integration layer and use the reports only for troubleshooting and for assuring that the integration runs smoothly. There are however situations in which the integration problems can’t be fixed without creating more issues. It’s the case in which multiple systems are involved and integrated over an integration bus.

One extreme approach, not advisable though, is to build a second integration to correct the issues of the first. This solution might work in theory however there’s the risk of multiplying the issues is really high and the complexity of troubleshooting increases with the degree of dependency between the two integrations. It would be more advisable to rebuild the integration anew, however also this approach has its advantages and disadvantages.

Bottom line is that integration issues should be addressed while they are small and that an automated solution for comparing the data can help in the process

💎SQL Reloaded: Cursor and Linked Server for Data Import

There are times when is needed to pull some data (repeatedly) from one or more databases for analysis and SSIS is not available or there’s not much time to create individual packages via data imports. In such scenarios is needed to rely on the use of SQL Server Engine’s built-in support. In this case the data can be easily imported via a linked server into ad-hoc created tables in a local database. In fact, the process can be partially automated with the use of a cursor that iterates through a predefined set of tables.For exemplification I will use a SELECT instead of an EXEC just to render the results:

-- cleaning up
-- DROP TABLE dbo.LoadTables 

-- defining the scope
SELECT *
INTO dbo.LoadTables
FROM (VALUES ('dbo.InventTable')
    , ('dbo.InventDimCombination')
    , ('dbo.InventDim')
    , ('dbo.InventItemLocation')) DAT ([Table])

-- creating the stored procedure 
CREATE PROCEDURE dbo.pLoadData(
    @Table as nvarchar(50))
AS
/* loads the set of tables defiend in dbo.LoadTables */
BEGIN
   DECLARE @cTable varchar(50)

   -- creating the cursor
   DECLARE TableList CURSOR FOR
   SELECT [Table]
   FROM dbo.LoadTables
   WHERE [Table] = IsNull(@Table, [Table])
   ORDER BY [Table]

   -- opening the cursor
   OPEN TableList 

   -- fetching next record 
   FETCH NEXT FROM TableList
   INTO @cTable

   -- looping through each record 
   WHILE @@FETCH_STATUS = 0 
   BEGIN
 --- preparing the DROP TABLE statement 
        SELECT(' DROP TABLE IF EXISTS ' + @cTable + '')

        -- preparing the SELECT INTO STATEMENT
        SELECT( ' SELECT *' +
         ' INTO ' + @cTable +
                ' FROM [server].[database].[' + @cTable + ']')

 -- fetching next record 
 FETCH NEXT FROM TableList
 INTO @cTable
   END

   --closing the cursor
   CLOSE TableList 
   -- deallocating the cursor
   DEALLOCATE TableList 
END

Running the stored procedure for all the tables:

 -- Testing the procedure 
 EXEC dbo.pLoadData NULL -- loading all tables 

-- output 
 DROP TABLE IF EXISTS dbo.InventDim
 SELECT * INTO dbo.InventDim FROM [server].[database].[dbo.InventDim]

 DROP TABLE IF EXISTS dbo.InventDimCombination
 SELECT * INTO dbo.InventDimCombination FROM [server].[database].[dbo.InventDimCombination]

 DROP TABLE IF EXISTS dbo.InventItemLocation
 SELECT * INTO dbo.InventItemLocation FROM [server].[database].[dbo.InventItemLocation]

 DROP TABLE IF EXISTS dbo.InventTable
 SELECT * INTO dbo.InventTable FROM [server].[database].[dbo.InventTable]

Running the stored procedure for a specific table:

-- Testing the procedure 

EXEC dbo.pLoadData 'dbo.InventTable' -- loading a specific table

-- output 
DROP TABLE IF EXISTS dbo.InventTable
SELECT * INTO dbo.InventTable FROM [server].[database].[dbo.InventTable]

Notes:
1) Having an old example of using a cursor (see Cursor and Lists)  the whole mechanism for loading the data was available in 30 Minutes or so.
2) Tables can be added or removed after need, and the loading can be made more flexible by adding other parameters to the logic.
3) The solution is really easy to use and the performance is as well acceptable in comparison to SSIS packages.
4) Probably you already observed the use of DROP TABLE IF EXSISTS introduced with SQL Server 2016 (see also post)

Advantages:
The stored procedure can be extended to any database for which can be created a linked server.
Structural changes of the source tables are reflected in each load.
Tables can be quickly updated when needed just by executing the stored procedure.

Disadvantages:
Such solutions are more for personal use and their use should be avoided in a production environment.
The metadata will be temporarily unavailable during the time the procedure is run. Indexes need to be created after each load.

Happy Coding!