ERP Systems: Microsoft Dynamics 365 (The Good, the Bad and the Ugly)

The Good: The shift made by Microsoft by porting their Dynamics AX ERP solution to a web-based application (aka D365) hosted in the Microsoft cloud, offered them a boost on the ERP market. The integration with the Office and BI stack, as well Microsoft’s One Version strategy of pushing new features almost on a monthly basis, and of having customers at a maximum 2 releases from the current version, makes from D365 a solution to consider for small to big organizations that span over business sectors and geographies.

The Bad: Currently the monthly release cycle seems to be a challenge for the customers and service providers altogether. Even if the changes in existing functionality are minor, while the functionality is thoroughly tested before releases, the customers still need to test the releases in several systems, especially to assure that the customizations and integrations still work. This can prove to be quite a challenge in which automatic or semiautomatic tools can help when adequately used. Even then, a considerable effort needs to be addressed by the parties involved.

The burden is bigger for the service providers that build their own solutions for D365 as they need to assure in advance that after each release the applications still work. From customers’ perspective, the more such applications they use, the higher the risks of delays in adopting a release or, in extremis, to look for similar solutions. In theory, with good planning and by following best practices the risks are small, though that’s just the theory speaking.

If in the past 2-3 instances were enough to support the ERP during and post implementation, currently the requirements for the cloud-based solution more than doubled, an organization arriving to rent 5-7 D365 instances for the same. Moreover, even if the split between the main blocks (Finance, Supply Chain, Retail and Talent), plus the various Customer Engagement packages, provides some flexibility when thy are combined, this leads to a considerable price increase. Further costs are related to the gaps existing in the available functionality. More likely Microsoft will attempt closing some of the gaps, however until then the customers are forced to opt for existing solutions or have the functionality built. Microsoft pretends that their cloud-based ERP solution provides lower ownership costs, however, looking at the prices, it’s questionable on whether D365 is affordable for small and average organizations. To put it bluntly – think how many socks (aka products) one needs to sell just to cover the implementation, the licensing and infrastructure costs!

One important decision taken by Microsoft was to not allow the direct access to the D365 production database, decision that limits an organization’s choices and flexibility in addressing reporting requirements. Of course, the existing BI infrastructure can still be leveraged with a few workarounds, though the flexibility is lost, while further challenges are involved.

The Ugly: ERP implementations based on D365 make no exceptions from the general trend – given their complexity they are predisposed to fail achieving the set objectives, and this despite Microsoft’s attempts of creating methodologies, tools and strong communities to support the service providers and customers in such projects. The reasons for failure reside with the customers and service providers altogether, the chains of implications forming a complex network of causalities with multiple levels of reinforcement. When the negative reinforcements break the balance, it can result a tipping point where the things start to go wrong – escalations, finger-pointing, teams’ restructuring, litigations, etc. In extremis, even if the project reaches the finish, the costs can easily reach an overrun of 50-150% from the initial estimation, and that’s a lot to bear.

Project Management: Methodologies (The Good, the Bad and the Ugly)

The Good: Nowadays there're several Project Management (PM) methodologies to choose from to address a project’s specifics and, when adapted and applied accordingly, a methodology can enable projects to be run and brought under control.

The Bad: Even if the theoretical basis of PM methodologies has been proved and perfected over the years, projects continue to fail at a disturbing rate. Of course, the reasons behind their failure are multiple, though often the failure reasons are rooted in how PM methodologies are taught, understood and implemented.

Same as a theoretical course in cooking won’t make one a good cook, a theoretical course in PM won’t make one a good Project Manager or knowledgeable team member in applying the learned methodology. Surprisingly, the expectation is exactly that – the team member got a training and is good to go. Moreover, people believe that managing a software project is like coordinating the building of a small treehouse. To some degree there are many similarities though the challenges typically lie in details, and these details often escape a standard course.

To bridge the gap between theory and practice is needed time for the learner to grow in the role, to learn the does and don’ts, and, most important, to learn how to use the tools at hand efficiently. The methodology is itself a tool making use of further tools in its processes – project plans, work breakdown structures, checklists, charters, reports, records, etc. These can be learned only through practice, hopefully with some help (aka mentoring) from an experienced person in the respective methodology, either the Project Manager itself, a trainer or other team member. Same as one can’t be thrown into the water and expected to traverse the Channel Tunnel, you can’t do that with a newbie.

There’s a natural fallacy to think that we've understood more than we have. We can observe our understanding's limits when we are confronted with the complexities involved in handing PM activities. A second fallacy is not believing other people’s warnings against using a tool or performing an activity in a certain way. A newbie’s mind has sometimes the predisposition of a child to try touching a hot stove even if warned against it. It’s part of the learning process, though some persist in such behavior without learning much. What’s even more dangerous is a newbie pretending to be an expert and this almost always ends badly.

The Ugly appears when the bad is brought to extreme, when methodologies are misused for the wrong purposes to the degree that they destroy anything in their way. Of course, a pool can be dug by using a spoon but does it make sense to do that? Just because a tool can be used for something it doesn’t mean it should be used for it as long there are better tools for the same. It seems a pretty logical thing though the contrary happens more often than we’d like. It starts with the preconception that one should use the tool one knows best, ignoring in the process the fit for purpose condition. What’s even more deplorable is breaking down a project to fit a methodology while ignoring the technical and logistical aspects.

Any tool can lead to damages when used excessively, in wrong places, at the wrong point in time or by the wrong person. Like the instruments in an orchestra, when an instrument plays the wrong note, it dissonates from the rest. When more instruments play wrongly, then the piece is unrecognizable. It’s the role of the bandmaster to make the players touch the right notes at the right time.

Data Management: Data Literacy as Second Language

At the Gartner Data & Analytics Summit that took place in 2018 in Grapevine, Texas, it was reiterated the importance of data literacy for taking advantage of the emergence of data analytics, artificial intelligence (AI) and machine learning (ML) technologies. Gartner expected then that by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy [1] – or how they put it – learning to ‘speak data’ as a ‘second language’.

Data literacy is typically defined as the ability to read, work with, analyze, and argue with data. Sure, these form the blocks of data literacy, though what I’m missing from this definition is the ability to understand the data, even if understanding should be the outcome of reading, and the ability to put data into the context of business problems, even if the analyzes of data could involve this later aspect too.

Understanding has several aspects: understanding the data structures available within an organization, understanding the problems with data (including quality, governance, privacy and security), respectively understanding how the data are linked to the business processes. These aspects go beyond the simple ability included in the above definition, which from my perspective doesn’t include the particularities of an organization (data structure, data quality and processes) – the business component. This is reflected in one of the problems often met in the BI/data analytics industry – the solutions developed by the various service providers don’t reflect organizations’ needs, one of the causes being the inability to understand the business on segments or holistically.  

Putting data into context means being able to use the respective data in answering stringent business problems. A business problem needs to be first correctly defined and this requires a deep understanding of the business. Then one needs to identify the data that could help finding the answers to the problem, respectively of building one or more models that would allow elaborating further theories and performing further simulations. This is an ongoing process in which the models built are further enhanced, when possible, or replaced by better ones.

Probably the comparison with a second language is only partially true. One can learn a second language and argue in the respective language, though it doesn’t mean that the argumentations will be correct or constructive as long the person can’t do the same in the native language. Moreover, one can have such abilities in the native or a secondary language, but not be able do the same in what concerns the data, as different skillsets are involved. This aspect can make quite a difference in a business scenario. One must be able also to philosophize, think critically, as well to understand the forms of communication and their rules in respect to data.

To philosophize means being able to understand the causality and further relations existing within the business and think critically about them. Being able to communicate means more than being able to argue – it means being able to use effectively the communication tools – communication channels, as well the methods of representing data, information and knowledge. In extremis one might even go beyond the basic statistical tools, stepping thus in what statistical literacy is about. In fact, the difference between the two types of literacy became thinner, the difference residing in the accent put on their specific aspects.

These are the areas which probably many professionals lack. Data literacy should be the aim, however this takes time and is a continuous iterative process that can take years to reach maturity. It’s important for organizations to start addressing these aspects, progress in small increments and learn from the experience accumulated.

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References:
[1] Gartner (2018) How data and analytics leaders learn to master information as a second language, by Christy Pettey (link)