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Juan R. González: "We carry out 'Big Data' projects with tangible results in weeks"

Companies obtain massive amounts of data from their customers through multiple channels. Therefore, good management of this information can help to weather uncertain times such as those that are approaching. Gathering data on customer behaviour, their habits, ways of buying, etc., in order to analyse them, can be a key roadmap to optimise the operation of any business. We interviewed Juan Ramón González, Chief Operating Officer of Mática Partners.

Diario Abierto- Susana de Pablos

November 20th, 2022

Companies obtain data from their customers through multiple channels: web, social networks, email, etc. Good management of this information can help to weather uncertain times such as those that are approaching. Collecting data on customer behavior, habits, shopping habits, etc., in order to analyze them, can be a key roadmap to optimize the operation of any business.

The main employers' associations are warning of the need for SMEs to understand the importance of increasing the productivity of their resources. The challenge is to turn the crisis around, based on a greater knowledge of the company itself, which can lead to the optimization of its costs and an improvement in its response capacity. Good use of data enables the right decisions to be made in any situation.

This is what Juan Ramón González, co-founder of Mática Partners, a company created in 2018 that closed last year with a turnover of 2.5 million euros, a figure that, according to him, will reach 3.5 million in 2023. For González, organizations need to know how to make good use of the data they collect to offer better services, to be better. In his more than 15 years of experience, he has been successfully dedicated to helping large companies make good decisions, shedding light on the data they handle and interpret on a daily basis, thus obtaining the most useful information to optimize their business and innovate. For all these reasons, he is considered one of the leading professionals in Spain. We interviewed him.

♦ Mática Partners promotes itself as a company that develops purpose-oriented solutions, business analytics solutions, big data and data governance. How does it stand out from its competitors? Juan Ramón González: When we created Mática Partners, our objective was to offer high quality services, focusing on excellence in our work, a high level of specialization and a focus on high productivity. This allows us to maintain a work model with our clients of short, high-intensity projects, where tangible results are provided in a few weeks.

♦ Is it difficult to convince companies to get started in the world of data management? J.R.G.: There are no longer strong barriers to entry. The different sectors, to a greater or lesser extent, see it as a fundamental competitive advantage. When a client is reluctant to embark on a data initiative, it is usually due to two factors: the investment required and uncertainty about the benefits it will bring. We help them in this aspect, prioritizing and breaking down those data needs into projects that can be addressed in two months on average, and with cloud technologies the initial investment is minimized.

♦ What advice would you give to companies so that they can collect really useful, quality data? J.R.G.: Before you start collecting data, you always have to be clear about the "what for", that is, be clear about the use cases and understand the type of data that can help you develop those cases. In the past there have been many initiatives that only collected information, without a clear business objective, which did not give very good results. In this data collection, it is very important to be able to understand the quality of the information obtained. There are solutions such as Aqtiva, a product developed by Grupo Mática to help detect quality problems, a fundamental pillar when using that information to improve the service.

♦ What would you recommend to avoid biases in the data, for example, gender or age biases? J.R.G.: Whenever I talk about biases, for example in an analytical model, it is important to differentiate the bias in the data and in the models. It may be that the historical data being used is biased, because the historical process is already biased. In that case, it is necessary to work on the data sample to "balance" it, i.e., to achieve an equitable data volume based on parameters such as age or gender. Subsequently, when creating an analytical model (machine learning, Artificial Intelligence) there are techniques that allow you to analyze how each variable impacts the model. It takes time, but it is vital to analyze how the model behaves in the face of variables that may cause social bias, and if found, it is necessary to retrain and improve the model to avoid this type of situation.

♦ You have been helping large companies to make good decisions based on data for almost 15 years. Could you give an example? J.R.G.: With many of our clients we work, for example, with sales forecasting models. Based on the client's historical information, and by gathering information from external sources - meteorology, advertising impact, etc. - we are able to forecast the evolution of sales by product, division or area and analyze how variations in external factors will affect future sales. This is then taken to the point of making a complete P&L forecasting model for a customer.

♦ How can Big Data shed light on uncertainty? J.R.G.: In periods of high uncertainty like the current one, data brings certainty. Big Data can help in two major areas:

  • Prediction. For example, you can forecast expected cost increases based on different scenarios, in order to optimize a company's pricing policy, or forecast expected sales volume. Optimization takes on critical relevance in periods leading to economic downturn or recession where competition is much fiercer.

  • Optimization. Big Data and business analytics techniques help to optimize all areas of an organization. For example, they help to reveal which are the most profitable product lines, to analyze new market niches, to improve operations or to analyze the best commercial or marketing actions based on their return.

♦ How long would it take an SME to recover the money invested in data management technologies? J.R.G.: We always advise starting in the data world with problems that can be addressed in a period of two to three months, and where the return on investment can be seen in a similar period of time. There are cases where the business impact is immediate. For example, a customer churn prediction model, a product purchase recommendation model, or a model for the optimization and reduction of logistics costs, will provide a return from the first moment it is implemented.

♦ Data centers are silos where data is stored for analysis that consume large amounts of electricity. Does data management have a negative environmental impact and be a cause of climate change? J.R.G.: It is important to differentiate between the large volumes of information stored, for example, by large technology companies such as Google, which stores all its users' data, or Meta, which stores the data shared by users on its social networks, with the aim of improving its advertising campaign platforms, and the volume of information needed by the organizations we work with. We always recommend our clients not to store data for the sake of storing it, but to focus their efforts only on data that will be of real and functional use to their business. This type of information has a minimal impact compared to the volume of data mentioned above.

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