7Puentes brinda asesoramiento a start ups en Tucumán
En la Provincia de Tucumán se está gestando algo más que interesante. AETTI (la Asociación de Empresas Tucumanas de Tecnología de la Información) creó una incubadora que ofrece acompañamiento a los emprendedores que quieran obtener financiamiento para hacer crecer sus negocios: AETTI Hub. Y hasta allá fuimos, a aportarles nuevas ideas, propuestas de valor para sus empresas y para motivarlos a que tracen puentes entre los distintos sectores.
Ernesto Mislej, Director de 7Puentes, brindó en la capital tucumana semanas atrás una charla a las empresas que integran AETTI Hub. Puso foco en la importancia de aprovechar los datos para obtener mayor rentabilidad en las organizaciones. “Estas empresas, más allá de tener modelos de negocios particulares y propios, generan datos, lo que las transforma en compañías de información. La clave está en investigar qué otros valores pueden extraer de esos datos para monetizarse y desarrollar otras líneas de negocio que las vuelvan aún más rentables”, explicó Mislej luego de su exposición, realizada en el marco de Camino Emprendedor, evento organizado por la incubadora.
Desde la perspectiva de la ciencia de datos, estos últimos hoy se presentan de manera compleja y, a la vez, tienen la potencialidad de utilizarse dentro de un modelo de negocio que permita ganar dinero, predecir eventos y modelar el comportamiento de los consumidores.
Entonces, esta complejidad es un desafío porque las empresas, como las start ups en este caso, deberán ser lo suficientemente inteligentes para trabajar con un solo aspecto de esa complejidad, para seleccionar los parámetros que permitan simplificarla. “Ante ese sinfín de datos hay que frenar y preguntarse si hace falta ese nivel de rigurosidad, si se debe monitorear una variable y no todas”, recomendó Mislej. En ese contexto es que 7Puentes propone a los emprendedores pensar en términos de MVM: Minimum Valuable Model.
“MVM parte del principio presupuesto de que las starts ups basadas en datos necesitan contar con modelos de data science accesibles para su realidad financiera pero que, de todos modos, sean aceptables en términos de precisión. Esta metodología de trabajo, basada en modelos mínimos y efectivos, reduce los riesgos a la inversión”, explicó Mislej a las empresas de AETTI Hub.
Con este y otros conceptos, 7Puentes capacitó y seguirá asesorando a estas start ups para despertar motivaciones, hacer que puedan evaluar emprendimientos de distintas partes del mundo y para que aprendan a ver cómo generar un nuevo valor a partir del uso de los datos que generan ellas.
Practical data science: Building Minimum Viable Models
When we talk about innovative services or products, many startups follow a smoother model of development. This allows them to minimize the risk to be able to have improvements when collecting capital to finance themselves. Once they found the market fit, the issue will be about the growth, to achieve a balance point.
For those startups based on data (nowadays, most of them consider their data as a strategic active for the decision making), to find a model that interprets them is a difficult task. Extract/collect data, measure, model and making decisions is a common road for any startup that aims to a dynamic, changing, fluid sector of the market.
It is the data scientist or the data science team’s task to find that/ those model/s, but finding it/them (determine the modelling technique, setting parameters and adjustment) may be a very long, and sometimes, non-aligned task with the business times. For example: it does not make sense a model to “predict the results of a football match” that finds the results after the match was played. So, how startups can minimize this risk when launching a new app? Do they need so much deployment to enter the market, do they have the necessary resources? In 7Puentes we understand they do not and that is why we coined a new concept: MVM (Minimum Valuable Model).
MVM is based on the principle that data-based startups need to have affordable data science models for their financial reality but also, these models have to be acceptable in terms of accuracy. This working methodology based on minimum and effective models, minimizes the risks in the event the product does not succeed in the market and, therefore, is an obstacle less in regards to the launching.
“A data science model with 75% of accuracy, which is acceptable to guarantee the well-functioning of the app, takes 25% of the time. To escalate to a 100%, i.e., to a perfect model, exponentially increases the time used and the required investment,” explains Carlos Lizarralde, 7Puentes’ CEO.
If we think a model of recommendation for an app like “Tinder”, a MVM does not need the 10 offers to be ideal, but, to have out of 10 offers an average of good offers and, maybe, a low percentage of very bad offers. It is not necessary to develop a prediction algorithm 100% effective and it is not feasible in financial terms. Every sector/project has its “good-enough”: sometimes the priority is a quick response but in other cases the covering is the focus.
To find a MVM, it is necessary a constant dialogue between the areas that define the business goals and the data scientist. It is no use the specialist working only two months with the data, since finding the MVM requires to pay attention to what data provide. Many times, the business areas require a very precise model with training data that is not enough, they are noisy or they do not adjust to the thought model. Maybe it is better to reduce the scope of the model to the portion of the data where it better works and, in the future, expand the coverage of the model when the startup has better financial resources.
More than 70% of data science project’s efforts consist on data-junk: collection and cleaning of data. And the time for modeling, experimenting and communicating results is too short. So that MVM model comes to accelerate the knowledge extraction process from a “lean” perspective.
