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: A Software Engineer's Perspective II (Major Knowledge Gaps)

Business Intelligence Series

Solving a problem requires a certain degree of knowledge in the areas affected by the problem, degree that varies exponentially with problem's complexity. This requirement applies to scientific fields with low allowance for errors, as well as to business scenarios where the allowance for errors is in theory more relaxed. Building a report or any other data artifact is closely connected with problem solving as the data artifacts are supposed to model the whole or parts of what is needed for solving the problem(s) in scope.

In general, creating data artifacts requires: (1) domain knowledge - knowledge of the concepts, processes, systems, data, data structures and data flows as available in the organization; (2) technical knowledge - knowledge about the tools, techniques, processes and methodologies used to produce the artifacts; (3) data literacy - critical thinking, the ability to understand and explore the implications of data, respectively communicating data in context; (4) activity management - managing the activities involved. 

At minimum, creating a report may require only narrower subsets from the areas mentioned above, depending on the complexity of the problem and the tasks involved. Ideally, a single person should be knowledgeable enough to handle all this alone, though that's seldom the case. Commonly, two or more parties are involved, though let's consider the two-parties scenario: on one side is the customer who has (in theory) a deep understanding of the domain, respectively on the other side is the data professional who has (in theory) a deep understanding of the technical aspects. Ideally, both parties should be data literates and have some basic knowledge of the other party's domain. 

To attack a business problem that requires one or more data artifacts both parties need to have a common understanding of the problem to be solved, of the requirements, constraints, assumptions, expectations, risks, and other important aspects associated with it. It's critical for the data professional to acquire the domain knowledge required by the problem, otherwise the solution has high chances to deviate from the expectations. The general issue is that there are multiple interactions that are iterative. Firstly, the interactions for building the needed common ground. Secondly, the interaction between the problem and reality. Thirdly, the interaction between the problem and parties’ mental models und understanding about the problem. 

The outcome of these interactions is that the problem and its requirements go through several iterations in which knowledge from the previous iterations are incorporated successively. With each important piece of knowledge gained, it's important to revise and refine the question(s), respectively the problem. If in each iteration there are also programming and further technical activities involved, the effort and costs resulted in the process can explode, while the timeline expands accordingly. 

There are several heuristics that could be devised to address these challenges: (1) build all the required knowledge in one person, either on the business or the technical side; (2) make sure that the parties have the required knowledge for approaching the problems in scope; (3) make sure that the gaps between reality and parties' mental models is minimal; (4) make sure that the requirements are complete and understood before starting the development; (5) adhere to methodologies that accommodate the necessary iterations and endeavor's particularities; (6) make sure that there's a halt condition for regularly reviewing the progress, respectively halting the work; (7) build an organizational culture to support all this. 

The list is open, and the heuristics aren't exclusive, so in theory any combination of them can be considered. Ideally, an organization should reflect all these heuristics in one form or another. The higher the coverage, the more mature the organization is. The question is how organizations with a suboptimal setup can change the status quo?

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Process Management: (Capability) Maturity Model (Definitions)

[capability maturity model:] "A model that contains the essential elements of effective processes for one or more disciplines and describes an evolutionary improvement path from ad hoc, immature processes to disciplined, mature processes with improved quality and effectiveness." (Sandy Shrum et al, "CMMI®: Guidelines for Process Integration and Product Improvement", 2003)

[capability maturity model (CMM):] "A formal document describing the requirements for a 'good' process, using some structure or taxonomy. Process maturity models define how you “ought to” produce a product, and typically require that the process be defined, documented, taught, practiced, measured, improved, and enforced." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"A model to categorize the maturity of an organization by different levels. Most famous are the Capability Maturity Model (CMM) and its successor, the Capability Maturity Model Integration (CMMI). Following this approach, many organizations have developed SOA maturity models." (Nicolai M Josuttis, "SOA in Practice", 2007)

"A Capability Maturity Model (CMM) is an evolutionary roadmap for implementing the vital practices from one or more domains of organizational process. It contains the essential elements of effective processes for one or more disciplines. It describes an evolutionary improvement path from an ad hoc, immature process to a disciplined, mature process with improved quality and effectiveness." (Sally A Miller et al, "People CMM: A Framework for Human Capital Management 2nd Ed.", 2009)

"A structured collection of characteristics of effective processes at progressive levels of quality and effectiveness. A maturity model provides a common language and a shared vision for process improvement, a standard for benchmarking, and a framework for prioritizing actions. A maturity model assumes a natural evolutionary path for organizational process improvement." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A framework that describes, for a specific area of interest, a number of levels of sophistication at which activities in this area can be carried out." (Jim Davis & Aiman Zeid, "Business Transformation: A Roadmap for Maximizing Organizational Insights", 2014)

"First introduced by the Carnegie Mellon Software Engineering Institute in 1991 to improve the process of software development. However, their broader applicability was recognized, and the model was expanded in 2000 to apply to enterprise-wide process improvement." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

[Capability Maturity Model Integration (CMMI):] "A process improvement approach that provides organizations with the essential elements of effective processes, which will improve their performance." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed.", 2015)

[capability maturity model integration (CMMI):] "A process model that captures the organization’s maturity and fosters continuous improvement." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"A set of structured levels that describe how well an organization can reliably and sustainably produce required outcomes." (Yassine Maleh et al, 'Strategic IT Governance and Performance Frameworks in Large Organizations", 2019)

[Capability Maturity Model (CMM):] "A five level staged framework that describes the key elements of an effective software process. The Capability Maturity Model covers best practices for planning, engineering and managing software development and maintenance ." (IQBBA)

[Capability Maturity Model Integration (CMMI):] "A framework that describes the key elements of an effective product development and maintenance process. The Capability Maturity Model Integration covers best-practices for planning, engineering and managing product development and maintenance. (CMMI)

"A structured collection of elements that describe certain aspects of maturity in an organization, and aid in the definition and understanding of an organization's processes. A maturity model often provides a common language, shared vision and framework for prioritizing improvement actions." (SQA)

"A Maturity Model is a framework that is used as a benchmark for comparison when looking at an organisation's processes." (Experian) [source]

"A means of identifying and/or measuring the maturity of something of interest, such as a Service, Capability, Function, Skill, or Competency." (IF4IT)

ERP Implementation: The right ERP software

ERP Implementations Series

Reading an interesting post in Panorama Consulting Group’s 360° Blog on Top Ten ERP Software Predictions for 2010, I was struck by the syntagm “choose the right software for their organizations”. I know from my own experience that this is a quest for Pandora’s Box, no matter how much we like to be confident and connoisseur about it, in the end is just philosophy.

Putting myself in the position of IT Manager or whoever implied in taking decisions related to software adoption I would ask myself: “what’s the right ERP software for my company?”. In theory things are not complicated, I evaluate my requirements and the functionality provided in the various ERP solutions, the costs involved with them, the amount of time and resources I can afford, and in the end I choose whatever may seem appropriate for my business model. It sounds simple, isn’t it? Of course, this supposing that I understand my business as a whole, its infrastructure and its culture, the issues it faces, the short term needs vs. the long term needs, that the requirements are defined upfront, etc.

In order to be sure that I’m doing the right thing, I even discuss with the sales representatives of the ERP vendors, have maybe one or two presentation sessions supplemented by a Q&A session in which my most experienced workers from each department express their concerns, ask for details, etc. Now jumping over the fact that the presentational skills and convincing tone of the sales representative might be a decisive factor in choosing a solution, I realize that the most important ERP solutions offer relatively similar functionality, most probably the differences rely in details. Now, there are a few questions that might occur to me… 

How much the people in the room, who maybe never worked with an ERP system, would understand what an ERP system is about? How much can they articulate their needs, identify which are the details that makes the most important impact on the business? How much the sales representative has understood my business and the overall context? In definitive he’s just trying to sell a product, how much he’s willing to dive into my requirements, analyze them and identify feasible solutions? Actually for that a few presentation sessions are not enough, it might take weeks, a whole team of resources, multiple iterations until you’ll come up with a feasible solution. Even then, once the ERP system is in place you observe that it doesn’t look exactly with what you wanted, with what you team intended, but that’s normal for IT solutions, unfortunately.

Sometime after Go Live, most probably the employees will understand what an ERP system is about – sticking to the processes, data ownership, more time spent on data entry and data management, unified implementation starting with the strategic planning and ending with the booking of revenue, dealing with issues not considered during implementation, functionality that is not so easy to use as expected so Excel or MS Access seems to be a more flexible solution, difficulty of changing the system and processes when needed by the business, more systems need to be integrated with it, that the 360 ° overview of the business is just a myth, and so on. From my experience I observed that the users have great expectations before and during implementing an ERP system, though the reality brings them down to earth, almost no magic behind the software the whole company was talking about, just a different way of approaching things!

The fact is that the decision of going with one ERP solution is validated only by the final outcome considered on long term, the impact it has on the business, the overall adoption and the degree to which it will fit the business needs, the flexibility of modifying it when needed. On the other side, if the ERP system doesn’t impact the business in a negative way, then the system can be considered successful, even if creepy things come to the surface from time to time. Was it the right decision? That I will not know for sure unless I’m moving to another ERP solution and I can compare the outcome with what I had in place, otherwise we just consider hypothetical situations. Actually the comparison might not be well founded because in such situation I benefit from the experience of already implementing an ERP system, I better understand the issues I was confronted with and eventually better address them in the new implementation.

The important point I would like to highlight is that a company needs to have a certain maturity when going with an ERP solution, situation that needs to be addressed by vendors or/and organizations themselves in order to increase the chances of success, otherwise the mixture of acronyms like ROI, CIOs, SMBs, SaaS, ERP, CRM in vision philosophies are just nice stories to read before going to bed. Of course the learning by doings syntagma can be applied to ERP implementations too, though the costs are too high for such a scenario. 

The state of art in ERP world – the vendor wants to sell you a product and profit on customer’s expense also after doing that, often not being interested whether the product fits the purpose as long more issues lead to be more income, while on the other side the customer wants an affordable flexible solution that allows bringing the business to higher level of performance. Most probable something must be changed in how the two parties work, and this might be, at least from my point of view, the most important challenge for the next years.