OUR SERVICE

Your data science projects are in good hands with us.

Intro­duc­tion

Do you need help in deciding, planning, embed­ding or applying your algorithms?

No problem! Take advan­tage of our services that are perfectly tailored to you! We support you with our exper­tise where you need it to be successful.

Strategy consul­ting

The goal: What are the greatest benefits of using data science in your organiza­tion? And how do you get there?

“Data science, artifi­cial intel­li­gence or machine learning: sounds nice, but what’s the point? Where do we need to start and why didn’t our pilot project work?”

If you are asking yourself these or similar questions, you are not alone! Many of our custo­mers are at this point. This is exactly where our strategy consul­ting comes in: together with our clients, we analyze their current business goals, their organiza­tion and their (IT) processes and data. We then develop a data science strategy based on this analysis. This gives our custo­mers clarity on where they should use data science, what it can do for them and how they should proceed in order to actually make the project a success. And the great thing is: the whole thing doesn’t take as long as it may sound now. We develop most strate­gies in just a few weeks.

Speaking in detail…

It all starts with the objec­tive – in which area data science and automa­tion will bring the greatest benefit, taking into account the company’s strategic goals.
What weak points and pain points are there for the business, what opera­tional trade-offs need to be considered? We work this out together with our custo­mers, who contri­bute with their exper­tise from their own business. At this point, it is not yet a question of identi­fying the solutions, but rather the issues and priori­ties.

To ensure that the right solution is developed and used, we find it important to involve manage­ment, specia­list depart­ments and future users right from the start. We want to deliver a minimal, produc­tively usable solution as soon as possible, which will be itera­tively developed and adapted to changing requi­re­ments. The opera­tional condi­tions must be taken into account and risks must be calcu­lated, for example the accuracy that must be achieved before a model can be used in produc­tion. Depen­ding on requi­re­ments and data-driven maturity, a recom­men­da­tion system where the decision lies with a human, an AI that makes decis­ions and learns from them in real time, or a hybrid solution may be suitable.

Available data and the ability to generate or procure new data is an important feasi­bi­lity criterion and another corner­stone for use case selec­tion. But the use case discus­sion can also identify the need for digitiza­tion and inspire the integra­tion of additional data sources. The secure and sensible recor­ding of data enables automa­tion and ensures audit compli­ance. Being data-driven also means being able to under­stand the infor­ma­tion on which decis­ions were made.

The overall solution can consist of several compon­ents that operate the process together and can be brought into produc­tion step by step if neces­sary.
A modular struc­ture makes it possible to start with simple algorithms and a few influen­cing factors in order to replace them over time with more complex models and a higher degree of automa­tion.
Starting with the simplest possible algorithms has several advan­tages: low effort and suscep­ti­bi­lity to errors, inter­pr­e­ta­bi­lity and the function as a baseline for compa­rison when trying out more complex algorithms.

Project manage­ment

Promo­ting inter­di­sci­pli­nary colla­bo­ra­tion while keeping an eye on the market, the relevant stake­hol­ders and the objec­tives? That works!

Nowadays, achie­ving goals works best in inter­di­sci­pli­nary teams. In projects, the various disci­plines work together for a limited time on a limited goal. Successful project manage­ment is neces­sary to ensure that this can be done effec­tively and effici­ently and that external impulses, such as market changes, can be absorbed at the same time.

In our projects, we often find that (pure) data science projects or projects with a high propor­tion of data science “tick” differ­ently. This is mainly due to the fact that the disci­pline of data science cannot be precisely estimated and planned at all times due to its creative research work. However, it is precisely this creative research work that makes the diffe­rence and should there­fore not be restricted at the wrong time.

There should also be basic data science skills and a good under­stan­ding of the work of data scien­tists in project manage­ment.

Our project managers fulfill this requi­re­ment: they generally have a basic mathe­ma­tical educa­tion and additional training in project manage­ment. In their day-to-day work, they perform a variety of tasks such as planning sprints or projects, speci­fying requi­re­ments, leading team meetings or trans­la­ting between mathe­ma­tics and domain languages.

  • Classic vs. agile
  • The debate about tradi­tional and agile project approa­ches is already outdated. In the vast majority of situa­tions, we recom­mend an agile or at least hybrid approach. However, the miscon­cep­tion that an agile approach is synony­mous with a lack of planning persists. Our project managers have already worked on classic and agile projects and can combine the best of both approa­ches thanks to this experi­ence.

    By the way, agile project manage­ment is not the same as Scrum. In addition to the well-known Scrum frame­work, there are various other process models that could be more suitable for your project.

  • Project manage­ment goals
  • The aim of project manage­ment is to organize and control the project and all those involved in terms of scope, budget and time. Depen­ding on whether the project is carried out in a classic, agile or hybrid way, dimen­sions are fixed or still variable. The advan­tage for you is obvious: you get a well-founded organiza­tion of your project and thus the best possible profes­sional result. You always have an overview of where the project currently stands and what challenges still need to be overcome.

The debate about tradi­tional and agile project approa­ches is already outdated. In the vast majority of situa­tions, we recom­mend an agile or at least hybrid approach. However, the miscon­cep­tion that an agile approach is synony­mous with a lack of planning persists. Our project managers have already worked on classic and agile projects and can combine the best of both approa­ches thanks to this experi­ence.

By the way, agile project manage­ment is not the same as Scrum. In addition to the well-known Scrum frame­work, there are various other process models that could be more suitable for your project.

The aim of project manage­ment is to organize and control the project and all those involved in terms of scope, budget and time. Depen­ding on whether the project is carried out in a classic, agile or hybrid way, dimen­sions are fixed or still variable. The advan­tage for you is obvious: you get a well-founded organiza­tion of your project and thus the best possible profes­sional result. You always have an overview of where the project currently stands and what challenges still need to be overcome.

Services in detail

Change manage­ment

Algorithm and organiza­tional develo­p­ment in perfect symbiosis

The full poten­tial of algorith­miza­tion can only be exploited if you continue to develop your organiza­tion at the same time. If algorithms are ineffec­tively embedded in the organiza­tion, this can even be counter­pro­duc­tive in the worst case.

Employees work past the algorithms, they look for loopholes to trick the algorithms and there is a lack of trust in the algorithms. The magic word in this case is change manage­ment: the neces­sary environ­ment for the newly developed algorithms must be created by consciously managing changes in the organiza­tion. The change can affect various areas of the organiza­tion: From the organiza­tional struc­ture and processes to compe­ten­cies and corpo­rate culture.

The chall­enge of digital trans­for­ma­tion consists of two aspects: On the one hand, to use the latest techno­lo­gies and, on the other hand, to develop the organiza­tion to use these new techno­lo­gies effici­ently. This requires a basic under­stan­ding of algorithms, how they work and their influences and effects, as well as knowledge of change manage­ment methods. A digital trans­for­ma­tion can only succeed and algorithms can only work successfully in the company if the organiza­tion can be designed sustain­ably.

Objec­tives of change manage­ment

  • Creating an environ­ment in which algorithms can work in the best possible way (fully automated, semi-automated or supportive).
  • Invol­vement and support of employees during the change process.
  • Shaping the corpo­rate culture and working together.
  • Estab­lish­ment of a learning organiza­tion.
  • Desig­ning the inter­ac­tion between people and techno­logy.

Services in detail

Appli­ca­tion opera­tion

The demands on opera­tions are huge these days: 24/7 in real time, without downtimes, bugs or other problems

Ideally, the clients don’t notice anything about the opera­tion and the users are satis­fied. In order to fulfill this requi­re­ment, a lot has to be done in appli­ca­tion opera­tion. Monito­ring and reactive inter­ven­tion are no longer enough these days. Instead, issues must be tackled proac­tively and improved on an ongoing basis.

Services in detail

Mainten­ance

The main task of opera­tions is mainten­ance, i.e. the neces­sary functional and technical adjus­t­ments to the IT system to ensure smooth opera­tion. We use various methods to design preven­tive mainten­ance and thus increase the satis­fac­tion of your users.

DevOps

Nowadays, appli­ca­tions are constantly being developed further. We use DevOps methods to consider further develo­p­ment and opera­tion together. This enables us to increase the quality and speed of your software develo­p­ment.

Struc­ture

After a project, the question of how to operate the new appli­ca­tion often arises. We will not leave you alone here and will support you in setting up the new struc­tures and handing over the project to the company.

Support

If you need support with appli­ca­tion opera­tion, we are also there for you in the long term. For example, in a support model in which you can draw on our exper­tise for diffi­cult or major issues.

Complete takeover

If you do not have your own company organiza­tion, we can organize the opera­tion for you.

Services

We use data and intel­li­gent algorithms to achieve your business goals!

Find out more about the services we can offer you to get the best out of your projects!

Project request

Thank you for your interest in m²hycon’s services. We look forward to hearing about your project and attach great importance to providing you with detailed advice.

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