On March 14th, the CDOIQ LATAM Symposium held its 1st edition in Brazil, with renowned market leaders and CDOs, and this was a question answered by Marcel Ghiraldini, Founder and CGO of MATH TECH , together with André Villamar, Head of Data & Analytics at Dexo and Jun Okara, Head of Analytics.
Knowing the need to be based on data, improve this culture in an organization and focus on business awareness, professionals were able to address the difference in the use of this data, in addition to conceptualizing Data Consumer and contributing to the delivery of value to a business.
Furthermore, the topic of fad culture regarding new technologies was also addressed. How about a brief brainstorming session on the topics?
The difference in data usage in corporations
To discover the difference in the use of data in corporations to generate real value, the first step is to be able to conceptualize a little about what a Data Consumer is and what a Data Driven operation is.
For Jun, most corporations have too much data, regardless of their strategy. So how can they generate value with so much information? Would it be through executive dashboards or through productized means?
In fact, for the head of analysis, the importance lies in looking at specific variables, rather than focusing on fifteen different models, with different graphs. Ultimately, this is not being Data-Driven, nor does the use of a 'lake' make you an expert in this field.
This standardization, according to Jun, is synonymous with being a Data Consumer or Data User. In other words, someone who only uses data. Data Driven is really about taking advantage of that small amount of information, knowing your main KPIs and being “driven” by the data.
“According to Google research, 80% of the data that a company needs , it actually has. But what questions are people asking the data?” That’s what André asks.
After all, being Data Driven is not a technical approach, it says much more about the cultural aspect and the golden triad: people, processes and technology.
Therefore, being Data Driven is nothing more than a strategy that guides decisions based on data analysis and interpretation, which drives decision-making and efficiency. In practice, it means using data in a coherent and minimalist way.
While Data Consumer means, according to professionals, a person who only consumes data. But, in addition, from a technical point of view, it consists of a management tool that naturally collaborates in the storage, organization and enrichment of information for data reading.
Technology beyond the culture of fad in companies
When we talk about cultural approach, there is one element that cannot be excluded: fashion.
Data Driven, for example, has been mentioned many times, as well as Data Mesh. However, the topic of the moment is Chat GPT.
For Marcel, Founder of MATH, organizations must separate what is actually being done and what is characterized as a fad, that is, what is being done only with the intention of following what is in fashion, such as the GPT chat.
Even because, everyone wants to be Data Driven, but where exactly do you want to get to?
Thinking about the industry, there is a bit of both fronts, but the main motivator is to see vietnam phone number example data as a strategic asset. This, according to André, says much more about the strategic movements from the point of view of communication and the value of that data and its importance in each of the departments.
For the professional, this tends to work in the best way. For example, applying AI does not work to solve any problem, and parallel articulations can contribute to the way the fad is seen. “How do I deliver value to the business? By making the data seen strategically,” added André.
For Jun, the trend is to want a Data Scientist for everything you need. The right thing to do is to think about value for the company beyond that, evaluating the trajectory of data evolution, which usually starts with descriptive analysis and can follow predictive and descriptive analysis.
But, are there any research and studies to understand these moments?
Therefore, the reflection is that data orientation justifies a decision that has already been made or to be considered for a future process.
Data culture in organizations
And speaking of professional Data Scientists, have you noticed how competitive data professionals are in the company?
Assembling teams focused on the sector is always a challenge when it comes to data culture. This is because, according to Marcel, the professional's perspective is approached through technology.
In other words, the value of the category in question for the market is not always known.
Companies are not mature in their understanding of data and data analysis, since the generation of transformational information is what changes and expands the position of these specialists.
With this in mind, the data layer is the systemic custody and, in any case, it is necessary for the application to be evaluated in all scenarios, but mainly, to act as teachers, training and generation of belonging.
For André, this position goes far beyond the technical aspect and the fact of being able to generate belonging to the team and present the value of what they have for the company from the point of view of strategic data management, regardless of the layer, is how this correlates to business processes.
First, we talk about people and engagement, exposing vulnerability;
Secondly, work on belonging in order to establish trust;
Finally, create an incredible environment, having a high-performance team, with leadership working in an extended way to generate business.
The ROI process
Through a data-driven team, and with the principles of the triad mentioned above, training bases end up being for the market.
However, without them, it is impossible to achieve an ideal team for this process. Through this, Marcel challenges and questions that the sales organization also needs to be part of this line.
Therefore, thinking about ROI – Return on Investment – it is not self-contained. So, what is the process like today when thinking about the chain under market evaluation or is it still thought about where this return is?
To this end, Jun reflects on the role he plays. “As a general data area, ROI is important, and in the financial sector it is what matters most. I am not saying that all of them have the maturity to keep up, and this is a big discussion when it comes to performance and product using data, what Data-Driven really is.
But the most important thing of all is: what ROI does business analytics bring to the product team and what ROI does the Data Scientist bring to the corporation in another way? It depends a lot on how the structure was formed, but we can use examples through A/B testing.”
After all, there is no point in just talking about information and implementing it for 100% of the base and having an increase in relation to the normal curve. Still thinking that the market can improve at that moment, “unintentionally”.
Therefore, it is necessary to have alignment with the business team and to have compatibility to carry out A/B tests that result in a demonstration of value to sell beyond your own result.
According to Marcel, it is not possible to carry out operations without separating alpha growth from beta. In other words, when carrying out the test, the separation of what the market did on its own from what your company did is not carried out, to the point of confusing your product with that of the competitor who made a “mistake”.
It is therefore understandable that the finance team is aligned with data, having ROI per year and sufficient demands to request more resources and justify investment.
Data Governance vs. People
Finally, in the digital context, supporting a data strategy goes beyond the ROI vision, with engineering and data layers facing a fundamental evolution process for a strategy.
With this in mind, the biggest challenges go beyond training, people and data culture motivated professionals.
This means that governance and people are the biggest challenges for CDOs and professionals in the field. One example brought up by Marcel is the use of GPT Chat, which fails due to the lack of governance of internet data, and thus uses false data, with poor quality and returns with “crazy” answers.
Therefore, governance, which is already important but neglected, becomes even more relevant. However, the main challenge today continues to be data governance and the need for attention to this centralization of information.