How to fall in love again with Scrum-based analytics proyects
When we start data projects, we always face different situations that challenge us to be better and to evolve.
One of the most common challenges in technology projects is resistance to the Scrum framework, another great challenge are the issues specific to each client — which can be as diverse as colors — and finally there are management challenges.
Let’s see what each of them consists of and how we can face them.
Resistance to Scrum
Starting a project with a new client is always like a lottery. You never know if they love or hate Scrum, you have no idea if they had any unpleasant experience with projects of the same kind and you don’t know what their work environment is like, how open or conservative they are.
When we talk about conservative clients, we refer to those organizations that are cautious when it comes to managing, planning or starting new things. They prefer to go for the known, for the path that has worked for them before trying one that is totally innovative.
The characteristic of most data analysis projects is that they are innovative, that is, they invite organizations to leave their comfort zones from many areas.
There is a common characteristic to all these projects and that is that they always bring to light technical or data problems that were hidden for a long time and/or that no one really wanted to see.
In addition to this, they make organizations question their way of managing projects. In the case of analytics, we commonly use Scrum, so it is common for us to face situations in which organizations do not know this framework, do not know how to use it, or simply do not like it 🤷♀️. In this way, a great challenge is posed for the organization, which, in addition to having to deal with everything that is brought to light from the technical side, must face a new way of working, of understanding needs and communicating.
It is a great internal challenge for the project and for the Scrum Master to make the framework be adopted in the best possible way in order to achieve good results. Love or hate for Scrum can be due to many factors, sometimes it is because they simply see it as a trend and as something that really does not add value, it can also happen that they consider it something belonging to the new generations and that before everything worked better.
In organizations that know and adopt Scrum, everything flows more easily. However, in those that do not, we must first observe and analyze very well where the resistance is, that is, we must understand if the rejection is really due to the framework or if it corresponds to a previous failed data project that generated distrust.
- Bad previous experiences: When there is a failed history, we have an important path to travel because we must rebuild trust, we must learn to communicate and earn the credibility of that client by showing results. At this point, Scrum can leverage us, since if you have a solid team that understands the iterative work of this methodology and the principles of agilism, in each Sprint we can show incremental progress and have productive results at the end of the Releases to gain the credibility and trust required.
- Rejection of agilism: When the distrust is directly with the framework, the best strategy is to be patient and, little by little, teach the pillars, the principles, the events and that everything flows as it should. At this point I refer to that we should not give in to managing projects as it was done before because in data projects we need the proximity and evidence of the impediments that Scrum gives us. However, we should not be aggressive with the framework either.
Scrum is one of those things that you learn by doing, that you can hardly learn without practicing.
From my experience, it has been very satisfying to see how clients who were totally opponents of Scrum, at the end of the project praise their results and are even very interested in learning it in depth and applying it to other projects.
From human psychology, the unknown always generates fear, and what I have seen is that sometimes the suspicion towards Scrum is nothing more than ignorance.
How to shield the data project from external chaos?
This is an interesting challenge because each organization is a world. It is a system that interrelates, that has conflicts, good things, not so good things. So, when we enter as an external team to support the development of a project we are part of that system, but at the same time we must be solid enough not to permeate what happens outside.
This is important, because the project as such is a system that has a beginning, an end, a purpose, internal and external relationships. A project is an entity that is expected to produce a result — preferably satisfactory — and as an independent system, which is part of other multiple systems, it must be shielded and self-sustaining in order to give the results that are expected of it.
There are challenges that take us out of our comfort zone such as the relationship with the internal IT areas of each client. This is a relationship that must be strengthened and carried out in the best possible way, but, as I mentioned before, generally analytics projects bring to light errors that have a direct relationship with the technology area, so communication and negotiation skills are so relevant.
The most common thing is that the technical debt that comes to light has negative effects on the relationship with the internal areas. It is necessary to be very careful with the way in which the findings are presented, it is important to show them in a constructive way, without pointing fingers or blaming anyone. It is important to show that we are all working towards the same goal and that the findings are opportunities for improvement.
Another challenge is the high level of uncertainty that exists in data projects, since it is a field that is constantly changing and evolving. It is necessary to be prepared to face this uncertainty, to adapt to changes and to be able to move forward without losing focus on the main objective.
The same thing happens with the organization in general, not only with the technology area but with all the areas with which we must relate and from which we need some kind of input. It may happen that we have clients who work on immediacy and day-to-day, and others who are more structured and with planning. In these cases, the most appropriate thing is to observe first, analyze and execute later. It is key to adapt to each type of client, regardless of the functioning of the organization with which we have to interact our system and internal gear must function perfectly.
Management challenges: How to make the client fall in love with analytics again 💔
In the long run there is a comeback as they say around here, and analytics projects are no exception. As in love, sometimes we have disappointments with some projects, or we think it will be much simpler than it really is. We can also have a disappointment and no longer want to trust again.
But just as in life, we can not deny the opportunity to find love only for fear of suffering again, so we must try again and with more desire. In the case of projects try again with more motivation and conviction that we are going to achieve it.
The most important thing of all is to be impeccable with management and be 100% oriented to achievements, show results that evidence all the work done so that, in this way, people can have a new experience with data, erasing previous negative moments.
Negative memories weigh on the unconscious, but sensations, feelings and new positive memories help mitigate the bad.
The importance of management lies mainly in achieving that the impediments are observed, faced and solved. That is why leadership plays a key role in the path of (re) falling in love with analytics projects, where thanks to that leadership, management and planning can be improved and learn from the stumbles of the way.
Sometimes it is also about learning to value each step, each result no matter how small it may seem, recognizing how we have improved and everything we have achieved.
Life teaches us all the time that looking for perfection can take us away from valuing what is good that has been achieved.
When the client comes from a failed project the first thing is not to let ourselves be impregnated with negative energy or heavy comments that may exist in the environment. Mainly do not take it personally, let the comment arrive but do not affect us because we are there, precisely, to show that things can always improve.
When the client has a resistance to the work framework, the main thing as facilitators is to show him the advantages of its use and enter the heart of the culture of the organization. In addition to that, we must be very clear about what we are going to do, how we are going to do it, with whom and why.
It is also important to create a climate of trust and transparency, where the client feels involved and part of the project, where he can see the progress, where he can see that what is being done is for the common good and for the success of the project.
In conclusion, management challenges in data projects are diverse and require a lot of strategy, leadership and planning to achieve them. But, as in love, with effort, dedication and perseverance everything is possible and, therefore, it is possible to make the client fall in love with analytics again.
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Written by Heidy Villa, Scrum Master @ Datalytics and translated by ChatGPT.
