🏗️Software Engineering: Responsibility (Just the Quotes)

"Poor management can increase software costs more rapidly than any other factor. Particularly on large projects, each of the following mismanagement actions has often been responsible for doubling software development costs." (Barry Boehm, "Software Engineering Economics", 1981)

"A subsystem is a set of classes" (and possibly other subsystems) collaborating to fulfill a set of responsibilities. Although subsystems do not exist as the software executes, they are useful conceptual entities." (Rebecca Wirfs-Brock, "Object-oriented Design: A. responsibility-driven approach", 1989)

"Architecture is defined as a clear representation of a conceptual framework of components and their relationships at a point in time [...] a discussion of architecture must take into account different levels of architecture. These levels can be illustrated by a pyramid, with the business unit at the top and the delivery system at the base. An enterprise is composed of one or more Business Units that are responsible for a specific business area. The five levels of architecture are Business Unit, Information, Information System, Data and Delivery System. The levels are separate yet interrelated. [...] The idea if an enterprise architecture reflects an awareness that the levels are logically connected and that a depiction at one level assumes or dictates that architectures at the higher level." (W Bradford Rigdon, "Architectures and Standards", 1989)

"The data-driven approach to object-oriented design focuses on the structure of the data in a system. This results in the incorporation of structural information in the definitions of classes. Doing so violates encapsulation. The responsibility-driven approach emphasizes the encapsulation of both the structure and behavior of objects. By focusing on the contractual responsibilities of a class, the designer is able to postpone implementation considerations until the implementation phase. While responsibility-driven design is not the only technique addressing this problem, most other techniques attempt to enforce encapsulation during the implementation phase. This is too late in the software life-cycle to achieve maximum benefits." (Rebecca Wirfs-Brock, "Object-oriented design: a responsibility-driven approach", 1989)

"Programmers are responsible for software quality - quality in their own work, quality in the products that incorporate their work, and quality at the interfaces between components. Quality has never been and will never be tested in. The responsibility is both moral and professional." (Boris Beizer, "Software Testing Techniques", 1990)

"[Object-oriented analysis is] the challenge of understanding the problem domain and then the system's responsibilities in that light." (Edward Yourdon, "Object-Oriented Design", 1991) 

"Extreme Programming is a discipline of software development with values of simplicity, communication, feedback and courage. We focus on the roles of customer, manager, and programmer and accord key rights and responsibilities to those in those roles." (Ron Jeffries, "Extreme Programming Installed", 2001)

"An object-oriented application is a set of interacting objects. Each object is an implementation of one or more roles. A role supports a set of related" (cohesive) responsibilities. A responsibility is an obligation to perform a task or know certain information. And objects don't work in isolation, they collaborate with others in a community to perform the overall responsibilities of the application. So a conceptual view, at least to start, is a distillation of the key object roles and their responsibilities" (stated at a fairly high level). More than likely" (unless you form classification hierarchies and use inheritance and composition techniques) many candidates you initially model will map directly to a single class in some inheritance hierarchy. But I like to open up possibilities by think first of roles and responsibilities, and then as a second step towards a specification-level view, mapping these candidates to classes and interfaces." (Rebecca Wirfs-Brock, [interview] 2003)

"The single most important trait of a professional programmer is personal responsibility. Professional programmers take responsibility for their career, their estimates, their schedule commitments, their mistakes, and their workmanship. A professional programmer does not pass that responsibility off on others." (Robert C Martin, [in Kevlin Henney’s "97 Things Every Programmer Should Know", 2024 )

"Conceptual models are best thought of as design-tools - a way for designers to straighten out and simplify the design and match it to the users' task-domain, thereby making it clearer to users how they should think about the application. The designers' responsibility is to devise a conceptual model that seems natural to users based on the users'' familiarity with the task domain. If designers do their job well, the conceptual model will be the basis for users' mental models of the application." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)

"The fact that bugs will certainly occur in your code does not mean you aren't responsible for them. The fact that the task to write perfect software is virtually impossible does not mean you aren't responsible for the imperfection." (Robert C Martin,"The Clean Coder: A code of conduct for professional programmers", 2011)

"Any software project must have a technical leader, who is responsible for all technical decisions made by the team and have enough authority to make them. Responsibility and authority are two mandatory components that must be present in order to make it possible to call such a person an architect." (Yegor Bugayenko, "Code Ahead", 2018)

"Attributing bugs to their authors doesn't make them more responsible, only more scared." (Yegor Bugayenko, "Code Ahead", 2018)

"A 'stream' is the continuous flow of work aligned to a business domain or organizational capability. Continuous flow requires clarity of purpose and responsibility so that multiple teams can coexist, each with their own flow of work. A stream-aligned team is a team aligned to a single, valuable stream of work; this might be a single product or service, a single set of features, a single user journey, or a single user persona." (Matthew Skelton & Manuel Pais, "Team Topologies: Organizing Business and Technology Teams for Fast Flow", 2019)

"Engineering managers have a responsibility to optimize their teams. They improve engineering workflows and reduce dependencies and repetitive tasks. Self-sustaining teams minimize dependencies that hinder them in their efforts to achieve their objectives. Scalable teams minimize software delivery steps and eliminate bottlenecks. The mechanisms to achieve this may include the use of tools, conventions, documentation, processes, or abstract things such as values and principles. Any action that produces a tangible improvement in the speed, reliability, or robustness of your team's work is worth your consideration." (Morgan Evans, "Engineering Manager's Handbook", 2023)

"In a workplace setting, accountability is the willingness to take responsibility for one's actions and their outcomes. Accountable team members take ownership of their work, admit their mistakes, and are willing to hold each other accountable as peers." (Morgan Evans, "Engineering Manager's Handbook", 2023)

"Systems architecture is the portion of any project over which the engineering team has the most control. This design is usually less of a collaboration between different functions and more clearly in the domain of engineers. As such, engineering managers have a high responsibility to own this process and its decisions. To produce the best decisions possible, you must have the right decision-building blocks: complete information to work from and a structured methodology to guide you." (Morgan Evans, "Engineering Manager's Handbook", 2023)

🧩IT: The Annotated Laws that Govern IT Professionals' Lives - Part I

"A bad idea executed to perfection is still a bad idea." (Norman R Augustine) [Augustine's Law]

"Bad code executed by powerful machines is still bad code." [sql-troubles]

"A great many problems do not have accurate answers, but do have approximate answers, from which sensible decisions can be made." (Berkeley's Law)

"It's easier to take/sell approximations as accurate answers than to find accurate answers. In time people will see no difference in between." [sql-troubles]

"About the time you finish doing something, you know enough to start." (James C Kinser) [Kinser's Law]

"By the time you finish something, the problem changed." [sql-troubles]

"Adding manpower to a late software project makes it later." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975) [Brook's Law]

"An object will fall so as to do the most damage." [Law of selective gravity]

"A bug will appear to do the most damage." [sql-troubles]

"Anything can be made to work if you fiddle with it long enough." (Wyszkowski's second law)

"Some problems do require infinite time." [sql-troubles]

"Build a system that even a fool can use, and only a fool will want to use it." [Shaw's principle]

"Doing it the hard way is always easier." (Murphy's paradox)

"Doing it the easy way is always harder." [sql-troubles]

"Don't force it - get a bigger hammer." [Anthony's law of force]

"Don't optimize it, get a more powerful machine." [sql-troubles]

"Every solution breeds new problems." [Murphy's laws]

"Every new problem multiplies the possible solutions." [sql-troubles]

"Everyone has a scheme that will not work." [Howe's law]

"Everything goes wrong all at once." (Quantized revision of Murphy's law)

"Small events converge toward bigger events." [sql-troubles]

"If a problem causes many meetings, the meetings eventually become more important than the problem." (Arthur Bloch, "Murphy's Law (Price/Stern/Sloan", 1977) (Hendrickson’s Law)

"More meetings tend to create more problems." [sql-troubles]

"If a project is not worth doing at all, it's not worth doing well." (Gordon's first law)

"The more a project is not worth doing, the more attention will attract."  [sql-troubles]

"If an experiment works, something has gone wrong." [Finagle's first law]

"If anything can go wrong, it will." [Murphy's laws]

"Things go wrong at a faster pace than one can find solutions." [sql-troubles]

"If there are two or more ways to do something, and one of those ways can result in a catastrophe, then someone will do it." [Murphy's Laws]

"It's enough one way, for things to result in catastrophes." [sql-troubles]

"If they know nothing of what you are doing, they suspect you are doing nothing." (Robert J Graham et al, "The Complete Idiot's Guide to Project Management", 2007)  [Graham's Law]

"People are good at ignoring the obvious." [sql-troubles]

"The more one explains, the more one is misunderstood." [sql-troubles] 

"If you mess with a thing long enough, it'll break." [Schmidt's law]

"Things break by design." [sql-troubles]

"One can learn to break things, by simply playing with them." [sql-troubles] 

"It's easier to break than design things. One can find thousands ways on how to break the same thing." [sql-troubles] 

"In any collection of data, the figure most obviously correct, beyond all need of checking, is the mistake." (Finagle's third law)

"In any collection of data there's at least a mistake." [sql-troubles]

"In any given set of circumstances, the proper course of action is determined by subsequent events." [McDonald's corollary to Murphy's laws]

"In crises that force people to choose among alternative courses of action, most people will choose the worst one possible." (Rudin's law)

"People go wrong with confidence." [sql-troubles]

"The more alternatives, the higher the chances to go wrong." [sql-troubles] 

"Information necessitating a change of design will be conveyed to the designer after - and only after - the plans are complete." [First law of revision:]

"In simple cases, presenting one obvious right way versus one obvious wrong way, it is often wiser to choose the wrong way so as to expedite subsequent revision." (First corollary

"The designer will get ahead of the design." [sql-troubles] 

"It is impossible to make anything foolproof because fools are so ingenious." (Murphy's second corollary)

"It works better if you plug it in." (Sattinger's law)

"It works longer if you don't plug it in." [sql-troubles]

"It's not a question of IF the car will break down, but WHEN it will break down." (Murphy's theory of automobiles)

"It's not a question of IF a program will break down, but when the code will break down." [sql-troubles]

"The longer a program runs smoothly, the higher the chances that will break down soon." [sql-troubles]

"Left to themselves, things tend to go from bad to worse." (Murphy's first corollary)

"The more on tries to fix things, the faster everything goes worse." [sql-troubles]

"Logic is a systematic method of coming to the wrong conclusion with confidence." (Manly's maxim)

 "One doesn't need logic to arrive at the right conclusion." [sql-troubles]

"Matter will be damaged in direct proportion to its value." (Murphy's constant)

"Most problems have either many answers or no answer. Only a few problems have a single answer." [Berkeley's Law]

"It's better to have a multitude of approximate solutions than one correct solution." [sql-troubles]

"Negative expectations yield negative results. Positive expectations yield negative results." (Non-reciprocal law of expectations)

"Negative results yield when there are no expectations." [sql-troubles]

"No matter how many things have gone wrong, there remains at least one more thing that will go wrong." (Murphy's law of the infinite)

"Things can go wrong in a multitude of ways." [sql-troubles]

"No matter how minor the job is, it's still over $50." (Murphy's rule of auto repair)

"No matter what the experiment's result, there will always be someone eager to: (i) misinterpret it, (ii) fake it, or (c) believe it supports his own pet theory." (Finagle's second law)

"It's easier to fake the experiment to get the right results." [sql-troubles]

"Nothing ever goes away." (Commoner's second law of ecology)

"Things do go away, but tend to come back." [sql-troubles]

"Nothing is as easy as it looks." (Murphy's first corollary)

"All things look simple until one dives deeper." [sql-troubles]

"Nothing is ever so bad that it can't get worse." (Gattuso's extension of Murphy's Law)

"Once a job is fouled up, anything done to improve it only makes it worse." (Finagle's fourth law)

"Once a mistake is corrected, a second mistake will become apparent." (Murphy's law of revision)

"Correcting mistakes introduces other mistakes." [sql-troubles]

"The chief cause of problems is solutions." [Sevareid's Law]

"The more time you spend in reporting on what you are doing, the less time you have to do anything. Stability is achieved when you spend all your time doing nothing but reporting on the nothing you are doing." [Cohn's Law]

"Reporting increases the needs for more information. The less one reports, the lower the need for further information." [sql-troubles]

"The more innocuous the modification appears to be, the further its influence will extend and the more plans will have to be redrawn." [H B Fyfe's second law of revision]

"The only thing more costly than stretching the schedule of an established development program is accelerating it, which is itself the most costly action known to man." (Norman R Augustine, "Augustine's Laws", 1983) [Law of economic unipolarity]

"The other line moves faster." (Etorre's observation)

"The other team moves faster." [sql-troubles]

"If you change lines, the one you just left will start to move faster than the one you are now in." (O'Brien's variation

"If you change a line, the whole codes breaks." [sql-trpoubles]

"The longer you wait in line, the greater the likelihood that you are in the wrong line." (The Queue Principal)

"The longer you wait for a deliverable, the greater the likelihood that it contains bugs." [sql-troubles]

"The perceived usefulness of an article is inversely proportional to its actual usefulness once bought and paid for." (Glatum's law of materialistic acquisitiveness)

"The probability of anything happening is in inverse ratio to its desirability." (Gumperson's law)

"The solution to a problem changes the problem." [Peers's Law]

"The tasks to do immediately are the minor ones; otherwise, you’ll forget them. The major ones are often better to defer. They usually need more time for reflection. Besides, if you forget them, they’ll remind you." [Wolf ’s Law of Management]

"There are two states to any large project: Too early to tell and too late to stop." (Ernest Fitzgerald) [Fitzgerald's Law]

"There is a solution to every problem; the only difficulty is finding it." [Evvie Nef's Law]

"There is no mechanical problem so difficult that it cannot be solved by brute strength and ignorance. [William's Law]

"There's no software problem so difficult that can't be solved by brute force and ignorance." [sql-troubles]

"There's always one more bug." (Lubarsky's law of cybernetic entomology)

"Software solutions diverge to a set of bugs." [sql-troubles

"Things get worse under pressure." [Murphy's law of thermodynamics]

"Things get worse also without pressure." [sql-troubles]

"Things go right gradually, but things go wrong all at once." (Murphy's asymmetry principle)

"Tolerances will accumulate unidirectionally toward maximum difficulty of assembly. (Klipstein's law)

"Two wrongs are only the beginning." (Kohn's corollary to Murphy's law)

"When all else fails, read the instructions." [Cahn's axiom]

"Even if you read the instructions, things fall." [sql-troubles]

"When an error has been detected and corrected, it will be found to have been correct in the first place." [Scott's second law]

"When in doubt, use a bigger hammer." [Dobbins’ Law]

"When taking something apart to fix a minor malfunction, you will cause a major malfunction." (Murphy's second law of construction)

"Whenever you set out to do something, something else must be done first." (Murphy's sixth corollary)

"While the difficulties and dangers of problems tend to increase at a geometric rate, the knowledge and manpower qualified to deal with these problems tend to increase linearly." [Dror's First Law]

"Beyond a point, the problems are so complex that people can't differentiate between geometric and linear rates." [sql-troubles]

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🧭Business Intelligence: Perspectives (Part XXIV: Building Castles in the Air)

Business Intelligence Series

Business users have mainly three means of visualizing data – reports, dashboards and more recently notebooks, the latter being a mix between reports and dashboards. Given that all three types of display can be a mix of tabular representations and visuals/visualizations, the difference between them is often neglectable to the degree that the terms are used interchangeably. 

For example, in Power BI a report is a "multi-perspective view into a single semantic model, with visualizations that represent different findings and insights from that semantic model" [1], while a dashboard is "a single page, often called a canvas, that uses visualizations to tell a story" [1], a dashboards’ visuals coming from one or more reports [2]. Despite this clear delimitation, the two concepts continue to be mixed and misused in conversations even by data-related professionals. This happens also because in other tools the vendors designate as dashboard what is called report in Power BI. 

Given the limited terminology, it’s easy to generalize that dashboards are useless, poorly designed, bad for business users, and so on. As Stephen Few recognized almost two decades ago, "most dashboards fail to communicate efficiently and effectively, not because of inadequate technology (at least not primarily), but because of poorly designed implementations" [3]. Therefore, when people say that "dashboards are bad" refer to the result of poorly implementations, of what some of them were part of, which frankly is a different topic! Unfortunately, BI implementations reflect probably more than any other areas how easy is to fail!

Frankly, here it is not necessarily the poor implementation of a project management methodology at fault, which quite often happens, but the way requirements are defined, understood, documented and implemented. Even if these last aspects are part of the methodologies, they are merely a reflection of how people understand the business. The outcomes of BI implementations are rooted in other areas, and it starts with how the strategic goals and objectives are defined, how the elements that need oversight are considered in the broader perspectives. The dashboards become thus the end-result of a chain of failures, failing to build the business-related fundament on which the reporting infrastructure should be based upon. It’s so easy to shift the blame on what’s perceptible than on what’s missing!

Many dashboards are built because people need a sense of what’s happening in the business. It starts with some ideas based on the problems identified in organizations, one or more dashboards are built, and sometimes a lot of time is invested in the process. Then, some important progress is made, and all comes to a stale if the numbers don’t reveal something new, important, or whatever users’ perception is. Some might regard this as failure, though as long as the initial objectives were met, something was learned in the process and a difference was made, one can’t equate this with failure!

It’s more important to recognize the temporary character of dashboards, respectively of the requirements that lead to them and build around them. Of course, this requires occasionally a different approach to the whole topic. It starts with how KPIs and other business are defined and organized, respectively on how data repositories are built, and it ends with how data are visualized and reported.

As the practice often revealed, it’s possible to build castles in the air, without a solid foundation, though the expectation for such edifices to sustain the weight of businesses is unrealistic. Such edifices break with the first strong storm and unfortunately it's easier to blame a set of tools, some people or a whole department instead at looking critically at the whole organization!

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References:
[1] Microsoft Learn (2024) Power BI: Glossary [link]

[2] Microsoft Learn (2024) Power BI: Dashboards for business users of the Power BI service [link] 

[3] Stephen Few, "Information Dashboard Design", 2006

🧊🗒️Data Warehousing: Extract, Transform, Load (ETL) [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes.

Last updated: 21-Jan-2025

[Data Warehousing] Extract, Transform, Load (ETL)  

• {def} automated process which takes raw data, extracts the data required for further processing, transforms it into a format that addresses business' needs, and loads it into the destination repository (e.g. data warehouse)
• includes 
• the transportation of data
• overlaps between stages
• changes in flow 
• due to 
• new technologies
• changing requirements
• changes in scope
• troubleshooting
• due to data mismatches
• {step} extraction
• data is extracted directly from the source systems or intermediate repositories
• data may be made available in intermediate repositories, when the direct access to the source system is not possible
• ⇐ this approach can add a complexity layer
•  {substep} data validation
• an automated process that validates whether data pulled from sources has the expected values
• relies on a validation engine
• rejects data if it falls outside the validation rules
• analyzes rejected records on an ongoing basis to
• identifies what went wrong
• corrects the source data
• modifies extraction to resolve the problem in the next batches
• {step} transform
• transforms the data, removing extraneous or erroneous data
• applies business rules 
• checks data integrity
• ensures that the data is not corrupted in the source or corrupted by ETL
• may ensure no data was dropped in previous stages
• aggregates the data if necessary
• {step} load
• {substep} store the data into a staging layer
• transformed data are not loaded directly into the target but staged into an intermediate layer (e.g. database)
• {advantage} makes it easier to roll back, if something went wrong
• {advantage} allows to develop the logic iteratively and publish the data only when needed 
• {advantage} can be used to generate audit reports for 
• regulatory compliance 
• diagnose and repair of data problems
• modern ETL process perform transformations in place, instead of in staging areas
• {substep} publish the data to the target
• loads the data into the target table(s)
• {scenario} the existing data are overridden every time the ETL pipeline loads a new batch
• this might happen daily, weekly, or monthly
• {scenario} add new data without overriding
• the timestamp can indicate the data is new
• {recommendation} prevent the loading process to error out due to disk space and performance limitations
• {approach} building an ETL infrastructure
• involves integrating data from one or more data sources and testing the overall processes to ensure the data is processed correctly
• recurring process
• e.g. data used for reporting
• one-time process
• e.g. data migration
• may involve 
• multiple source or destination systems 
• different types of data
• e.g. reference, master and transactional data
• ⇐ may have complex dependencies
• different level of structuredness
• e.g. structured, semistructured, nonstructured 
• different data formats
• data of different quality
• different ownership 
• {recommendation} consider ETL best practices
• {best practice} define requirements upfront in a consolidated and consistent manner
• allows to set clear expectations, consolidate the requirements, estimate the effort and costs, respectively get the sign-off
• the requirements may involve all the aspects of the process
• e.g. data extraction, data transformation, standard formatting, etc.
• {best practice} define a high level strategy
• allows to define the road ahead, risks and other aspects 
• allows to provide transparency
• this may be part of a broader strategy that can be referenced 
• {best practice} align the requirements and various aspects to the existing strategies existing in the organization
• allows to consolidate the various efforts and make sure that the objectives, goals and requirements are aligned
• e.g. IT, business, Information Security, Data Management strategies
• {best practice} define the scope upfront
• allows to better estimate the effort and validate the outcomes
• even if the scope may change in time, this allows to provide transparence and used as basis for the time and costs estimations
• {best practice} manage the effort as a project and use a suitable Project Management methodology
• allows to apply structured well-established PM practices 
• it might be suited to adapt the methodology to project's particularities 
• {best practice} convert data to standard formats to standardize data processing
• allows to reduce the volume of issues resulted from data type mismatches
• applies mainly to dates, numeric or other values for which can be defined standard formats
• {best practice} clean the data in the source systems, when  cost-effective
• allows to reduces the overall effort, especially when this is done in advance
• this should be based ideally on the scope
• {best practice} define and enforce data ownership
• allows to enforce clear responsibilities across the various processes
• allows to reduce the overall effort
• {best practice} document data dependencies
• document the dependencies existing in the data at the various levels
• {best practice} protocol data movement from source(s) to destination(s) in term of data volume
• allows to provide transparence into the data movement process
• allows to identify gaps in the data or broader issues
• can be used for troubleshooting and understanding the overall data growth
• {recommendation} consider proven systems, architectures and methodologies
• allows to minimize the overall effort and costs associated with the process

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🏭🗒️Microsoft Fabric: Data Mesh [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 23-May-2024

[Microsoft Fabric] Data Mesh

• {definition} a type of decentralized data architecture that organizes data based on different business domains [2]
•  ⇐ a centrally managed network of decentralized data products
• {concept} landing zone
• typically a subscription that needs to be governed by a common policy [7]
• {downside} creating one landing zone for every project can lead to too many landing zones to manage
• {alternative} landing zones based on a business domain [7] 
•  resources must be managed efficiently in a way that each team is given access to only their resources [7]
• ⇐  shared resources might be need with separate management and common access to all [7]
• need to be linked together into a mesh
• via peer-to-peer networks
• {concept} connectivity hub
• {feature} resource group
• {definition} a container that holds related resources for an Azure solution 
• can be associated with a data product
• when the data product becomes obsolete, the resource group can be deleted [7]
• {feature} subscription
• {definition} a logical unit of Azure services that are linked to an Azure account
• can be associated as a landing zone governed by a policy [7]
• {feature} tenant (aka Microsoft Fabric tenant, MF tenant)
• a single instance of Fabric for an organization that is aligned with a Microsoft Entra ID
• can contain any number of workspaces
• {feature} workspaces
• {definition} a collection of items that brings together different functionality in a single environment designed for collaboration
• associated with a domain [3]
• {feature} domains
• {definition} a way of logically grouping together data in an organization that is relevant to a particular area or field [1]
• some tenant-level settings for managing and governing data can be delegated to the domain level [2]
• {feature} subdomains
  • a way for fine tuning the logical grouping data under a domain [1]
  • subdivisions of a domain
• {concept} deployment template
• 
  

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References

[1] Microsoft Learn: Fabric (2023) Fabric domains (link)

[2] Establishing Data Mesh architectural pattern with Domains and OneLake on Microsoft Fabric, by Maheswaran Arunachalam (link) 

[3] Data mesh: A perspective on using Azure Synapse Analytics to build data products, by Amanjeet Singh (link)

[4] Zhamak Dehghani (2021) Data Mesh: Delivering Data-Driven Value at Scale

[5] Marthe Mengen (2024) How do you set up your Data Mesh in Microsoft Fabric? (link)

[6] Administering Microsoft Fabric - Considering Data Products vs Domains vs Workspaces, by Paul Andrew (link)

[7] Aniruddha Deswandikar (2024) Engineering Data Mesh in Azure Cloud

#️⃣Software Engineering: Mea Culpa (Part V: All-Knowing Developers are Back in Demand?)

Software Engineering Series

I’ve been reading many job descriptions lately related to my experience and curiously or not I observed that many organizations look for developers with Microsoft Dynamics experience in the CRM, respectively Finance and Operations (F&O) and Business Central (BC) areas. It’s a good sign that the adoption of Microsoft solutions for CRM and ERP increases, especially when one considers the progress made in the BI and AI areas with the introduction of Microsoft Fabric, which gives Microsoft a considerable boost. Conversely, it seems that the "developers are good for everything" syntagma is back, at least from what one reads in job descriptions. 

Of course, it’s useful to have an inhouse developer who can address all the aspects of an implementation, though that’s a lot to ask considering the different non-programming areas that need to be addressed. It’s true that a developer with experience can handle Requirements, Data and Process Management, respectively Data Migrations and Business Intelligence topics, though if one considers that each of the topics can easily become a full-time job before, during and post-project implementations. I’ve been there and I (hopefully) know that the jobs imply. Even if an experienced programmer can easily handle the different aspects, there will be also times when all the topics combined will be too much for a person!

It's not a novelty that job descriptions are treated like Christmas lists, but it’s difficult to differentiate between essential and nonessential skillset. I read many jobs descriptions lately in which among a huge list of demands, one of the requirements is to program in the F&O framework, sign that D365 programmers are in high demand. I worked for many years as programmer and Software Engineer, respectively in the BI area, where SQL and non-SQL code is needed. Even if I can understand the code in F&O, does it make sense to learn now to program in X++ and the whole framework? 

It's never too late to learn new tricks, respectively another programming language and/or framework. It even helps to provide better solutions in usual areas, though frankly I would invest my time in other areas, and AI-related topics like AI prompting or Data Science seem to be more interesting on the long run, especially when they are already in demand!

There seems to be a tendency for Data Science professionals to do everything, building their own solutions, ignoring the experience accumulated respectively the data models built in BI and Data Analytics areas, as if the topics and data models are unrelated! It’s also true that AI-modeling comes with its own requirements in what concerns data modeling (e.g. translating non-numeric to numeric values), though I believe that common ground can be found!

Similarly, the notebook-based programming seems to replicate logic in each solution, which occasionally makes sense, though personally I wouldn’t recommend it as practice! The other day, I was looking at code developed in Python to mimic the joining of tables, when a view with the same could be easier (re)used, maintained, read and probably more efficient, even if different engines will be used. It will be interesting to see how the mix of spaghetti solutions will evolve over time. There are developers already complaining of the number of objects used in the process by building logic for each layer from the medallion architecture! Even if it makes sense from architectural considerations, it will become a nightmare in time.

One can wonder also about nomenclature used – Data Engineer or Prompt Engineering for the simple manipulation of data between structures in data transformations, respectively for structuring the prompts for AI. I believe that engineering involves more than this, no matter the context! 

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🧭Business Intelligence: Mea Culpa (Part IV: Generalist or Specialist in an AI Era?)

Business Intelligence Series

Except the early professional years when I did mainly programming for web or desktop applications in the context of n-tier architectures, over the past 20 years my professional life was a mix between BI, Data Analytics, Data Warehousing, Data Migrations and other topics (ERP implementations and support, Project Management, IT Service Management, IT, Data and Applications Management), though the BI topics covered probably on average at least 60% of my time, either as internal or external consultant. 

I can consider myself thus a generalist who had the chance to cover most of the important aspects of a business from an IT perspective, and it was thus a great experience, at least until now! It’s a great opportunity to have the chance to look at problems, solutions, processes and the various challenges and opportunities from different perspectives. Technical people should have this opportunity directly in their jobs through the communication occurring in projects or IT services, though that’s more of a wish! Unfortunately, the dialogue between IT and business occurs almost only over the tickets and documents, which might be transparent but isn’t necessarily effective or efficient! 

Does working only part time in an area make one person less experienced or knowledgeable than other people? In theory, a full-time employee should get more exposure in depth and/or breadth, but that’s relative! It depends on the challenges one faces, the variation of the tasks, the implemented solutions, their depth and other technical and nontechnical factors like training, one’s experience in working with the various tools, the variety of the tasks and problem faced, professionalism, etc. A richer exposure can but not necessarily involve more technical and nontechnical knowledge, and this shouldn’t be taken as given! There’s no right or wrong answer even if people tend to take sides and argue over details.

Independently of job's effective time, one is forced to use his/her time to keep current with technologies or extend one’s horizon. In IT, a professional seldom can rely on what is learned on the job. Fortunately, nowadays one has more and more ways of learning, while the challenge shifts toward what to ignore, respectively better management of one’s time while learning. The topics increase in complexity and with this blogging becomes even more difficult, especially when one competes with AI content!

Talking about IT, it will be interesting to see how much AI can help or replace some of the professions or professionals. Anyway, some jobs will become obsolete or shift the focus to prompt engineering and technical reviews. AI still needs explicit descriptions of how to address tasks, at least until it learns to create and use better recipes for problem definition and solving. The bottom line, AI and its use can’t be ignored, and it can and should be used also in learning new things. It’s amazing what one can do nowadays with prompt engineering! 

Another aspect on which AI can help is to tailor the content to one’s needs. A high percentage in the learning process is spent on fishing in a sea of information for content that is worth knowing, respectively for a solution to one’s needs. AI must be able to address also some of the context without prompters being forced to give information explicitly!

AI opens many doors but can close many others. How much of one’s experience will remain relevant over the next years? Will AI have more success in addressing some of the challenges existing in people’s understanding or people will just trust AI blindly? Anyway, somebody must be smarter than AI, and here people’s collective intelligence probably can prove to be a real match. 

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🧭Business Intelligence: Mea Culpa (Part III: Problem Solving)

Business Intelligence Series

I've been working for more than 20 years in BI and Data Analytics area, in combination with Software Engineering, ERP implementations, Project Management, IT services and several other areas, which allowed me to look at many recurring problems from different perspectives. One of the things I learnt is that problems are more complex and more dynamic than they seem, respectively that they may require tailored dynamic solutions. Unfortunately, people usually focus on one or two immediate perspectives, ignoring the dynamics and the multilayered character of the problems!

Sometimes, a quick fix and limited perspective is what we need to get started and fix the symptoms, and problem-solvers usually stop there. When left unsupervised, the problems tend to kick back, build up momentum and appear under more complex forms in various places. Moreover, the symptoms can remain hidden until is too late. To this also adds the political agendas and the further limitations existing in organizations (people, money, know-how, etc.).

It seems much easier to involve external people (individual experts, consultancy companies) to solve the problem(s), though unless they get a deep understanding of the business and the issues existing in it, the chances are high that they solve the wrong problems and/or implement the wrong solutions. Therefore, it's more advisable to have internal experts, when feasible, and that's the point where business people with technical expertise and/or IT people with business expertise can help. Ideally, one should have a good mix and the so called competency centers can do a great job in handling the challenges of organizations. 

Between business and IT people there's a gap that can be higher or lower depending on resources know-how or the effort made by organizations to reduce it. To this adds the nature of the issues existing in organizations, which can vary considerable across departments, organizations or any other form of establishment. Conversely, the specific skillset can be transmuted where needed, which might happen naturally, though upon case also considerable effort needs to be involved in the process.

Being involved in similar tasks, one may get the impression that one can do whatever the others can do. This can happen in IT as well on the business side. There can be activities that can be done by parties from the other group, though there are also many exceptions in both directions, especially when one considers that one can’t generalize the applicability and/or transmutation of skillset. 

A more concrete example is the know-how needed by a businessperson to use the BI infrastructure for answering business questions, and ideally for doing all or at least most of the activities a BI professional can do. Ideally, as part of the learning path, it would be helpful to have a pursuable path in between the two points. The mastery of tools helps in the process though there are different mindsets involved.

Unfortunately, the data-related fields are full of overconfident people who get the problem-solving process wrong. Data-based problem-solving resumes in gathering the right facts and data, building the right conceptual model, identifying the right questions to ask, collecting more data, refining methods and solutions, etc. There’s aways an easy wrong way to solve a problem!

The mastery of tools doesn’t imply the mastery of business domains! What people from the business side can bring is deeper insight in the business problems, though getting from there to implementing solutions can prove a long way, especially when problems require different approaches, different levels of approximations, etc. No tool alone can bridge such gaps yet! Frankly, this is the most difficult to learn and unfortunately many data professionals seem to get this wrong!

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🧭🏭Business Intelligence: Microsoft Fabric (Part V: One Person Can’t Learn or Do Everything)

Business Intelligence Series

Today’s Explicit Measures webcast [1] considered an article written by Kurt Buhler (The Data Goblins): [Microsoft] "Fabric is a Team Sport: One Person Can’t Learn or Do Everything" [2]. It’s a well-written article that deserves some thought as there are several important points made. I can’t say I agree with the full extent of some statements, even if some disagreements are probably just a matter of semantics.

My main disagreement starts with the title “One Person Can’t Learn or Do Everything”. As clarified in webcast's chat, the author defines “everything" as an umbrella for “all the capabilities and experiences that comprise Fabric including both technical (like Power BI) or non-technical (like adoption data literacy) and everything in between” [1].

For me “everything” is relative and considers a domain's core set of knowledge, while "expertise" (≠ "mastery") refers to the degree to which a person can use the respective knowledge to build back-to-back solutions for a given area. I’d say that it becomes more and more challenging for beginners or average data professionals to cover the core features. Moreover, I’d separate the non-technical skills because then one will also need to consider topics like Data, Project, Information or Knowledge Management.

There are different levels of expertise, and they can vary in depth (specialization) or breadth (covering multiple areas), respectively depend on previous experience (whether one worked with similar technologies). Usually, there’s a minimum of requirements that need to be covered for being considered as expert (e.g. certification, building a solution from beginning to the end, troubleshooting, performance optimization, etc.). It’s also challenging to roughly define when one’s expertise starts (or ends), as there are different perspectives on the topics. 

Conversely, the term expert is in general misused extensively, sometimes even with a mischievous intent. As “expert” is usually considered an external consultant or a person who got certified in an area, even if the person may not be able to build solutions that address a customer’s needs. 

Even data professionals with many years of experience can be overwhelmed by the volume of knowledge, especially when one considers the different experiences available in MF, respectively the volume of new features released monthly. Conversely, expertise can be considered in respect to only one or more MF experiences or for one area within a certain layer. Lot of the knowledge can be transported from other areas – writing SQL and complex database objects, modelling (enterprise) semantic layers, programming in Python, R or Power Query, building data pipelines, managing SQL databases, etc. 

Besides the standard documentation, training sessions, and some reference architectures, Microsoft made available also some labs and other material, which helps discovering the features available, though it doesn’t teach people how to build complete solutions. I find more important than declaring explicitly the role-based audience, the creation of learning paths for the various roles.

During the past 6-7 months I've spent on average 2 days per week learning MF topics. My problem is not the documentation but the lack of maturity of some features, the gaps in functionality, identifying the respective gaps, knowing what and when new features will be made available. The fact that features are made available or changed while learning makes the process more challenging. 

My goal is to be able to provide back-to-back solutions and I believe that’s possible, even if I might not consider all the experiences available. During the past 22 years, at least until MF, I could build complete BI solutions starting from requirements elicitation, data extraction, modeling and processing for data consumption, respectively data consumption for the various purposes. At least this was the journey of a Software Engineer into the world of data. 

References:
[1] Explicit Measures (2024) Power BI tips Ep.328: Microsoft Fabric is a Team Sport (link)
[2] Data Goblins (2024) Fabric is a Team Sport: One Person Can’t Learn or Do Everything (link)

🧭🏭Business Intelligence: Microsoft Fabric (Part III: The Metrics Layer [new feature])

Introduction

One of the announcements of this year's Microsoft Fabric Community first conference was the introduction of a metrics layer in Fabric which "allows organizations to create standardized business metrics, that are rooted in measures and are discoverable and intended for reuse" [1]. As it seems, the information content provided at the conference was kept to a minimum given that the feature is still in private preview, though several webcasts start to catch up on the topic (see [2], [4]). Moreover, as part of their show, the Explicit Measures (@PowerBITips) hosts had Carly Newsome as invitee, the manager of the project, who unveiled more details about the project and the feature, details which became the main source for the information below. 

The idea of a metric layer or metric store is not new, data professionals occasionally refer to their structure(s) of metrics as such. The terms gained weight in their modern conception relatively recently in 2021-2022 (see [5], [6], [7], [8], [10]). Within the modern data stack, a metrics layer or metric store is an abstraction layer available between the data store(s) and end users. It allows to centrally define, store, and manage business metrics. Thus, it allows us to standardize and enforce a single source of truth (SSoT), respectively solve several issues existing in the data stacks. As Benn Stancil earlier remarked, the metrics layer is one of the missing pieces from the modern data stack (see [10]).

Microsoft's Solution

Microsoft's business case for metrics layer's implementation is based on three main ideas (1) duplicate measures contribute to poor data quality, (2) complex data models hinder self-service, (3) reduce data silos in Power BI. In Microsoft's conception the metric layer provides several benefits: consistent definitions and descriptions, easy management via management views, searchable and discoverable metrics, respectively assure trust through indicators. 

For this feature's implementation Microsoft introduces a new Fabric Item called a metric set that allows to group several (business) metrics together as part of a mini-model that can be tailored to the needs of a subset of end-users and accessed by them via the standard tools already available. The metric set becomes thus a mini-model. Such mini-models allow to break down and reduce the overall complexity of semantic models, while being easy to evolve and consume. The challenge will become then on how to break down existing and future semantic models into nonoverlapping mini-models, creating in extremis a partition (see the Lego metaphor for data products). The idea of mini-models is not new, [12] advocating the idea of using a Master Model, a technique for creating derivative tabular models based on a single tabular solution.

A (business) metric is a way to elevate the measures from the various semantic models existing in the organization within the mini-model defined by the metric set. A metric can be reused in other fabric artifacts - currently in new reports on the Power BI service, respectively in notebooks by copying the code. Reusing metrics in other measures can mean that one can chain metrics and the changes made will be further propagated downstream. 

The Metrics Layer in Microsoft Fabric (adapted diagram)

Every metric is tied to the original semantic model which allows thus to track how a metric is used across the solutions and, looking forward to Purview, to identify data's lineage. A measure is related to a "table", the source from which the measure came from.

Users' Perspective

The Metrics Layer feature is available in Microsoft Fabric service for Power BI within the Metrics menu element next to Scorecards. One starts by creating a metric set in an existing workspace, an operation which creates the actual artifact, to which the individual metrics are added. To create a metric, a user with build permissions can navigate through the semantic models across different workspaces he/she has access to, pick a measure from one of them and elevate it to a metric, copying in the process its measure's definition and description. In this way the metric will always point back to the measure from the semantic model, while the metrics thus created are considered as a related collection and can be shared around accordingly. 

Once a metric is added to the metric set, one can add in edit mode dimensions to it (e.g. Date, Category, Product Id, etc.). One can then further explore a metric's output and add filters (e.g. concentrate on only one product or category) point from which one can slice-and-dice the data as needed.

There is a panel where one can see where the metric has been used (e.g. in reports, scorecards, and other integrations), when was last time refreshed, respectively how many times was used. Thus, one has the most important information in one place, which is great for developers as well as for the users. Probably, other metadata will be added, such as whether an increase in the metric would be favorable or unfavorable (like in Tableau Pulse, see [13]) or maybe levels of criticality, an unit of measure, or maybe its type - simple metric, performance indicator (PI), result indicator (RI), KPI, KRI etc.

Metrics can be persisted to the OneLake by saving their output to a delta table into the lakehouse. As demonstrated in the presentation(s), with just a copy-paste and a small piece of code one can materialize the data into a lakehouse delta table, from where the data can be reused as needed. Hopefully, the process will be further automated. 

One can consume metrics and metrics sets also in Power BI Desktop, where a new menu element called Metric sets was added under the OneLake data hub, which can be used to connect to a metric set from a Semantic model and select the metrics needed for the project. 

Tapping into the available Power BI solutions is done via an integration feature based on Sempy fabric package, a dataframe for storage and propagation of Power BI metadata which is part of the python-based semantic Link in Fabric [11].

Further Thoughts

When dealing with a new feature, a natural idea comes to mind: what challenges does the feature involve, respectively how can it be misused? Given that the metrics layer can be built within a workspace and that it can tap into the existing measures, this means that one can built on the existing infrastructure. However, this can imply restructuring, refactoring, moving, and testing a lot of code in the process, hopefully with minimal implications for the solutions already available. Whether the process is as simple as imagined is another story. As misusage, in extremis, data professionals might start building everything as metrics, though the danger might come when the data is persisted unnecessarily. 

From a data mesh's perspective, a metric set is associated with a domain, though there will be metrics and data common to multiple domains. Moreover, a mini-model has the potential of becoming a data product. Distributing the logic across multiple workspaces and domains can add further challenges, especially in what concerns the synchronization and implemented of requirements in a way that doesn't lead to bottlenecks. But this is a general challenge for the development team(s). 

The feature will probably suffer further changes until is released in public review (probably by September or the end of the year). I subscribe to other data professionals' opinion that the feature was for long needed and that can have an important impact on the solutions built. 

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Resources:
[1] Microsoft Fabric Blog (2024) Announcements from the Microsoft Fabric Community Conference (link)
[2] Power BI Tips (2024) Explicit Measures Ep. 236: Metrics Hub, Hot New Feature with Carly Newsome (link)
[3] Power BI Tips (2024) Introducing Fabric Metrics Layer / Power Metrics Hub [with Carly Newsome] (link)
[4] KratosBI (2024) Fabric Fridays: Metrics Layer Conspiracy Theories #40 (link)
[5] Chris Webb's BI Blog (2022) Is Power BI A Semantic Layer? (link)
[6] The Data Stack Show (2022) TDSS 95: How the Metrics Layer Bridges the Gap Between Data & Business with Nick Handel of Transform (link)
[7] Sundeep Teki (2022) The Metric Layer & how it fits into the Modern Data Stack (link)
[8] Nick Handel (2021) A brief history of the metrics store (link)
[9] Aurimas (2022) The Jungle of Metrics Layers and its Invisible Elephant (link)
[10] Benn Stancil (2021) The missing piece of the modern data stack (link)
[11] Microsoft Learn (2024) Sempy fabric Package (link)
[12] Michael Kovalsky (2019) Master Model: Creating Derivative Tabular Models (link)
[13] Christina Obry (2023) The Power of a Metrics Layer - and How Your Organization Can Benefit From It (link) 
[14] KratosBI (2024) Introducing the Metrics Layer in #MicrosoftFabric with Carly Newsome [link]

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

Business Intelligence Series

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

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

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

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

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

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

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

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

🧭Business Intelligence: Why Data Projects Fail to Deliver Real-Life Impact (Part III: Failure through the Looking Glass)

Business Intelligence Series

There’s a huge volume of material available on project failure – resources that document why individual projects failed, why in general projects fail, why project members, managers and/or executives think projects fail, and there seems to be no other more rewarding activity at the end of a project than to theorize why a project failed, the topic culminating occasionally with the blaming game. Success may generate applause, though it's failure that attracts and stirs the most waves (irony, disapproval, and other similar behavior) and everybody seems to be an expert after the consumed endeavor. 

The mere definition of a project failure – not fulfilling project’s objectives within the set budget and timeframe - is a misnomer because budgets and timelines are estimated based on the information available at the beginning of the project, the amount of uncertainty for many projects being considerable, and data projects are no exceptions from it. The higher the uncertainty the less probable are the two estimates. Even simple projects can reveal uncertainty especially when the broader context of the projects is considered. 

Even if it’s not a common practice, one way to cope with uncertainty is to add a tolerance for the estimates, though even this practice probably will not always accommodate the full extent of the unknown as the tolerances are usually small. The general expectation is to have an accurate and precise landing, which for big or exploratory projects is seldom possible!

Moreover, the assumptions under which the estimates hold are easily invalidated in praxis – resources’ availability, first time right, executive’s support to set priorities, requirements’ quality, technologies’ maturity, etc. If one looks beyond the reasons why projects fail in general, quite often the issues are more organizational than technological, the lack of knowledge and experience being some of the factors. 

Conversely, many projects will not get approved if the estimates don’t look positive, and therefore people are pressured in one way or another to make the numbers fit the expectations. Some projects, given their importance, need to be done even if the numbers don’t look good or can’t be quantified correctly. Other projects represent people’s subsistence on the job, respectively people's self-occupation to create motion, though they can occasionally have also a positive impact for the organizations. These kinds of aspects almost never make it in statistics or surveys. Neither do the big issues people are afraid to talk about. Where to consider that in the light of politics and office’s grapevine the facts get distorted!

Data projects reflect all the symptoms of failure projects have in general, though when words like AI, Statistics or Machine Learning are used, the chances for failure are even higher given that the respective fields require a higher level of expertise, the appropriate use of technologies and adherence to the scientific process for the results to be valid. If projects can benefit from general recipes, respectively established procedures and methods, their range of applicability decreases when the mentioned areas are involved. 

Many data projects have an exploratory nature – seeing what’s possible - and therefore a considerable percentage will not reach production. Moreover, even those that reach that far might arrive to be stopped or discarded sooner or later if they don’t deliver the expected value, and probably many of the models created in the process are biased, irrelevant, or incorrectly apply the theory. Where to add that the mere use of tools and algorithms is not Data Science or Data Analysis. 

The challenge for many data projects is to identify which Project Management (PM) best practices to consider. Following all or no practices at all just increases the risks of failure!

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🧭Business Intelligence: Why Data Projects Fail to Deliver Real-Life Impact (Part II: There's Value in Failure)

Business Intelligence Series

"Results are nothing; the energies which produce them

and which again spring from them are everything."

(Wilhelm von Humboldt,  "On Language", 1836)

When the data is not available and is needed on a continuous basis then usually the solution is to redesign the processes and make sure the data becomes available at the needed quality level. Redesign involves additional costs for the business; therefore, it might be tempting to cancel or postpone data projects, at least until they become feasible, though they’re seldom feasible. 

Just because there’s a set of data, this doesn’t mean that there is important knowledge to be extracted from it, respectively that the investment is feasible. There’s however value in building experience in the internal resources, in identifying the challenges and the opportunities, in identifying what needs to be changed for harnessing the data. Unfortunately, organizations expect that somebody else will do the work for them instead of doing the jump by themselves, and this approach more likely will fail. It’s like expecting to get enlightened after a few theoretical sessions with a guru than walking the path by oneself. 

This is reflected also in organizations’ readiness to do the required endeavors for making the jump on the maturity scale. If organizations can’t approach such topics systematically and address the assumptions, opportunities, and risks adequately, respectively to manage the various aspects, it’s hard to believe that their data journey will be positive. 

A data journey shouldn’t be about politics even if some minds need to be changed in the process, at management as well as at lower level. If the leadership doesn’t recognize the importance of becoming an enabler for such initiatives, then the organization probably deserves to keep the status quo. The drive for change should come from the leadership even if we talk about data culture, data strategy, decision-making, or any critical aspect.

An organization will always need to find the balance between time, scope, cost, and quality, and this applies to operations, tactics, and strategies as well as to projects.  There are hard limits and lot of uncertainty associated with data projects and the tasks involved, limits reflected in cost and time estimations (which frankly are just expert’s rough guesses that can change for the worst in the light of new information). Therefore, especially in data projects one needs to be able to compromise, to change scope and timelines as seems fit, and why not, to cancel the projects if the objectives aren’t feasible anymore, respectively if compromises can’t be reached.

An organization must be able to take the risks and invest in failure, otherwise the opportunities for growth don’t change. Being able to split a roadmap into small iterative steps that allow besides breaking down the complexity and making progress to evaluate the progress and the knowledge resulted, respectively incorporate the feedback and knowledge in the next steps, can prove to be what organizations lack in coping with the high uncertainty. Instead, organizations seem to be fascinated by the big bang, thinking that technology can automatically fill the organizational gaps.

Doing the same thing repeatedly and expecting different results is called insanity. Unfortunately, this is what organizations and service providers do in what concerns Project Management in general and data projects in particular. Building something without a foundation, without making sure that the employees have the skillset, maturity and culture to manage the data-related tasks, challenges and opportunities is pure insanity!

Bottom line, harnessing the data requires a certain maturity and it starts with recognizing and pursuing opportunities, setting goals, following roadmaps, learning to fail and getting value from failure, respectively controlling the failure. Growth or instant enlightenment without a fair amount of sweat is possible, though that’s an exception for few in sight!

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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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