Scientific research applied in business context

Your reality today?

Three ways we help you getting things done

Act as sounding board

We provide a second opinion on data science plans and roadmaps, but we also go all the way down to the nitty gritty of assessing the algorithm fit with the problem at hand and the quality of implementation.

Coach teams

Having the experience of industrialising many machine learning solutions, we know common pitfalls and problems. We help you avoid these mistakes and support you from conceptual thinking, to product industrialisation and implementation in the organisation.

Drive execution

If you are looking for results we can lead the development and implementation of our modules. This allows you to take informed (automated) decisions based on machine learning insights made understandable.

Overcome the challenges you face in your data science journey

VERY EARLY STAGE

“Help me avoid the risk of recruiting the wrong professionals”

Knowing the content at a very deep level is not always needed to lead and manage a team. However, when it comes to a highly specialized group of professionals in a knowledge based domain, one can not ignore the relationship between content expertise and selecting your professionals. For the past 10 years, recruitment activities traditionally have focused on searching matches between keywords in job descriptions and CVs, and so disregarding any other potentially important characteristics of job applicants. As a consequence, the poor results delivered keep nurturing the necessity of recruiting making this an apparently endless process at a high cost and with often low expectations of succeeding.

This problem is exceptionally remarkable in the field of Data Science, where most of the applicants’ CVs are constructed based on the same set of keywords driven by the hype, and they have equivalent backgrounds and studied similar disciplines. It is very tempting to believe that the same method to select traditional data analysts and even software engineers is the way to go. Regrettably the results of a recruitment process are visible years after, when the high expectations and broken promises turn out to frustrations and the burden of having spent a lot of time and money. Sometimes ignorance is a legitimate excuse for wrong decisions, and sometimes it isn't. Would you take that risk? Are you up for a chat and hear more about how to address this matter?

EARLY STAGE WITH SMALL INEXPERIENCED TEAMS AND/OR USING THIRD PARTIES AGENCIES

“Help me to reshape my team and 3rd party support to convert the myth of benefits of using ML into reality”

Why do 87% of data science projects never make it into production? This terrible question is the result of a decade worth of wasted efforts and money. Unfortunately the complexity of successfully implementing a useful ML solution makes it difficult to spot a single simple reason. Nonetheless, based on almost two decades of delivering value using advanced analytics techniques, recently called Data Science, we dare to say that the causes are: (1) team capabilities and (2) company organization, with the former being the main driver. Importantly to note is that these two factors interact with each other bidirectionally. On the one hand, the lack of results from the team makes the company very reluctant to reshape the organization, on the other hand, given a potentially proper organization, the lack of results make the company hesitant to believe the organization is set properly, and so this situation drives to further changes. 

Regardless of focusing on acquiring in-house capabilities or relying on the support from third parties, how do you ensure your organization has a capable set of professionals to face this complex matter? Specially when a large undistinguishable number of potential employees and third parties are claiming to be able to help you, often arguing the existence of magical results, in your working endeavours. Recently a provocative report showed that “Forty percent of ‘AI startups’ in Europe don't actually use AI”, and so highlighting the fact that numerous opportunists are eager to somehow take part of the problem rather than provide solutions, sometimes due to ignorance.

We are a group of specialists who co-create, build capability and empower your team. Not an army of consultants and developers learning on the job and finishing their mandate with a set of doubtful and unproven advice. We dare you to have a chat with us and convert the myth of benefits of using Machine Learning into a reality.

INTERMEDIARY STAGE WITH ALREADY A TEAM AND/OR USING THIRD PARTIES AGENCIES

“Help me to continuously improve my ML solutions as key driver of competitive advantage”

As it happens in the research field, achieving the best possible results is an ongoing process. Especially important in a field where every year new advancements make your solution obsolete or not good enough anymore. While one has to be aware of the pragmatism required in a corporate setup, it can be not ignored that a competitive advantage is very often a matter of small nuances and improvements, yielding very large benefits. 

A fast pace in finding solutions and improvements, and most importantly in implementing these solutions within the company, is one of the keys to a successful business. In the Data Science realm, a solution can be seen as one product more, and as such it has a life cycle that requires continuous monitoring, feedback adjustments, and at some point an end-of-life. Thus, it is of key relevance not only to find a solution but to continuously monitor its performance with metrics that represent the value to our business.

A competitive advantage is a question of benchmarking and maintenance, and it boils down to being able to answer the following questions: is our solution legitimately good? Can we improve it? Is the solution still adding value to the business as initially expected? Let us talk to you about how to address these questions. Dare to push the  bounds of your business.