Its 1st edition in the country arrived to make the performance of CDO leaders and executives even stronger, and included an extensive program with several renowned professionals in the market.
Among the speakers, we had representatives from brands such as Nestlé, Hospital Israelista Albert Einstein, Ambev, PagBank, AWS, Dexco and Banco Next, both partners of MATH TECH, which was also present both as a sponsor and as a participant in the event, composing the panel on Business Impacts: Data Driven vs. Data Consumer.
According to Nilson, co-host of CDOIQ, “the main objective of the symposium was to create, cultivate and strengthen relationships between CDOs”, which was very present throughout the day of exchanges.
Additionally, some of the topics covered specifically dealt with data science, value creation, and cultural promotion. Check out a brief summary of some of the contributions of these organizations.
Summary
More than data, people
Promoting Culture and Generating Value with Data
Data Governance: The Critical Path to Data Mesh and Data-Driven Culture
The Importance of Data Management for Analytical Competition
More than data, people
With the participation of professional Rodrigo Oliveira, Digital & Business Innovation Lead at Nestlé, the approach moved from data to people.
The professional addressed examples through cases on how to continue the data-driven journey, with people at the center focusing on consumers, employees and customers.
1. People are more digital than ever before
And it’s not just about shopping, but about the way of living.
In view of this, Nestlé focuses every decision
In the consumer;
On the client;
In the collaborator.
And he emphasizes that more than data, we need to think about people. To this end, he created an internal team called DATA LAB, which currently fills more than 60 internal and 20 external positions, which assesses the company's data in order to evolve the foundation into an online data ecosystem, using Azure as a data lake and treasure data as a data center.
2. Practical examples of how to know your consumer
In view of this, one of its other main cases brought a situation that increased the need to create personas, thinking not only about the individual, but about the mass, contemplating the following points:
Acquisition: with more consumers
Enrichment: with more attributes
Engagement and conversion: with more sales
All of this is based on the principle of knowing your audience, keeping in mind that it is not possible to access data from all the records already obtained, so enrichment is important.
After approaching their audience and recognizing them for a specific campaign, the results came to :
+ 11 thousand new registrations;
+40k savings;
14.3k salvage;
30% CTOR;
7.4% conversion rate (the forecast was to reach 5%).
3. Focus on boosting customer sales
For this case, the topic of recommendation was the initial and final agenda of this case, thinking about and using brands like Netflix and Amazon as the main source of inspiration.
Nestlé today, based on this, uses :
Recurrence, offering the customer what they are already used to buying, to gain reliability;
Association, which is based on the purchase of other users to propose a new product, as well as Amazon when it uses options such as “other users also liked...”, to indicate new sales opportunities.
Thinking about the marketplace, a good recommendation is a sign that the customer is happy to sell more, while the sales team is able to do a great job to achieve this result.
Promoting Culture and Generating Value with Data
And speaking of culture and people, generating value with data cannot be left out. All this because, if there is no good culture that involves people understanding Data & Analytics, it is impossible for a company to become data-driven.
This panel featured Andrea Suman, CDO of Hospital Israelista Albert Einsten, Diógenes Justo, Head of Data at Zé Delivery, representing Ambev, and Juliana Custódio, General Data Manager at PagBank, who were moderated by Janete Ribeiro, Data Specialist at AWS.
Knowing that a data-oriented culture is in high demand, those involved responded to why an organization has difficulty becoming data-oriented.
For Juliana, there are three big things that need to be equalized in order to give value to a company. Let's go to them :
“How can you have a clear strategy that combines tools, roles and responsibilities to avoid waste?
“How do we actually link data to levers that we have there for the company, thinking about finding something for the operational journey and cost efficiency, for those who work with data to use these levers in addition to technical data ? ”;
“Finally, how do we work on culture and literacy? Because how are we going to make this work without creating an awareness of the direction and role to make this happen?”
“Culture is a key point to make this happen”, according to the PagBank professional.
At other points, doubts became pertinent regarding the challenges faced by a company in extracting data and the way to influence the organization of teams to structure areas.
This, of course, is not a problem for just one company. On the contrary, both panelists vietnam phone number example presented challenges in forming a team focused on Data & Analytics.
However, the use of Decentralization, due to the type of data complexity, cited by Andrea Suman, is noteworthy. “If I decentralize at construction times, we have more difficulties.”
Decentralization is nothing more than stopping concentrating all information in a single place.
“What we have seen in decentralization is how to better orchestrate value generation. When decentralizing, the challenge is to look at portfolios in a more orchestrated way,” added Andrea.
Therefore, for those who are starting out, the professionals' tip is
Find the business problem and seek a solution strategically;
Start small, with one or two cases, and find the sponsors who are engaged, not the detractors, so that they can reach the end of the journey;
Have more constant deliveries, understand the process well, what you are going to transform and have the same people during this development.
Diógenes also adds that one should focus on what will contribute most in the search for generating values.
Data Governance: The Critical Path to Data Mesh and Data-Driven Culture
With the participation of Rafael Kataoka, Chief Data Officer & DPO at Banco ABC Brasil, the topic reached data governance .
According to him, when thinking about data within a financial institution's decisions, data governance is important to avoid negative impacts, especially when we talk about LGPD (General Data Protection Law).
To do this, we go directly into Data Mesh and data-driven culture. This means that, for a Data Mesh platform, first of all, it is necessary to understand the centralization of governance, but that the data is per business domain.
In other words, how does a company consume data? For Rafael, it is necessary to have a Data Marketplace, so that the information is centralized. And to find the information, we use Data Products.
Have a good Data Mesh. That is, a culture of data as a tool for the marketplace to evolve the platform for use;
There is no point in having a good Data Lake with the best possible technology if there is no good Marketplace to manage its use;
A data catalog is necessary to understand what certain information means, like a business dictionary of data. This is an enhancement to the knowledge culture, mentioned in the previous topics, for example;
Information with reliable data for automatic decisions. But how can you be sure of this reliability? Rafael even supports the use of operating systems to monitor performance, billing and feeding;
Data security as a legal reason, such as the LGPD;
Be addressable. That is, having a Data Lake to address usage, since the Data Driven culture will need to make you use the information for a specific area and leverage the business, such as revenue generation, for example;
Data by Design as interoperable. Using decentralization and dividing components into different 'squads' to be able to access data in a governed way, using more agility.
Read more about the topic in “ Data governance framework: what it is and how to choose yours ”
The Importance of Data Management for Analytical Competition
Finally, we joined André Vil l amar, Head of Data Analytics & DPO at Dexco, to discuss in more depth the importance of data management for the analytical competition.
The pace of growth is increasing, both from the point of view of the business model, whether digital or not, the volume of users is growing, as well as the volume of data and the use of Analytics. This is part of the development of product improvements, according to André.
Data is a strategic asset, providing a competitive edge in different growth areas, both in terms of efficiency and the consumer journey. Following this line, there is plenty of room for growth.
And for Dexco, an inspirational model to help it evolve was:
Delta Plus – definition of quality and integration, since DELTA stands for D (Data); E (Enterprise); L (Leadership); T (Targets); A (Analysts). The last two letters can also stand for Technology and Analytics Techniques, consecutively.
And as a methodological process, they also opted for a path of proof, which meets 5 stages.
Analytical deficiencies
An organization has some data and some interest in analytical intelligence on the part of management.
Localized analysis
Functional management develops favorable conditions for analysis and executive interest through basic analytical application.
Analytical Aspirants
Executives commit to analytics by aligning blueprints and setting a timeline for developing broad analytics capabilities.
Analytical companies
Enterprise-wide analytical capabilities. Senior executives consider analytical capabilities a corporate priority.
Analytical competitors
The organization continually reaps the benefits of analytical competence developed across the enterprise and focuses on continuous analytical renewal.
And so, he concludes by considering that the human and organizational aspects of the analytical competition are the real differentiating elements, and not the technology. Which is basically what some of the other panels have addressed: people.
In short, this can be designed to train those who already exist in companies and adopt Data in their daily lives, as learning and growth.
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