Analitico Empowers Businesses with Data-Driven Excellence as a Leading Analytics Company.
Situation: The client has 4 main lines of business: Individual loans, Business loans, Construction loans, and Miscellaneous loans. The information for each business line is fragmented into silos, with independent systems and legacy technologies that hinder decision- making.
Solution: Through the use of next-gen tech, a data consumption strategy was implemented using a Data Lakehouse built from three main layers (Gold, Silver, Bronze), which centralize business information and enable data consumption for business users.
Situation: The client operates within the Fintech niche and aimed to gain visibility into their active client portfolio, default risk, and improve the loan acceptance rate. All decisions were made based on market standards, resulting in a high debt percentage.
Solution: Through the use of next-gen tech, a comprehensive analytical platform was implemented from the Data Lake layer to the consumption of strategic indicators in a DWH. This enabled the business to have visibility into its active portfolio and customer acquisition strategy.
Situation: Digital transformation initiatives arise from business needs and translate into technical requirements for the IT team. Currently, they have a portfolio of technologies that require constant maintenance and development to meet business objectives.
Solution: KodeFree is a trusted provider to maintain, grow, and develop these platforms on a daily basis. Through various profiles that help create an interdisciplinary team.
Situation: The bank is undergoing a process of enterprise digital transformation. To achieve this, they have defined data as a central business asset, establishing an architecture alongside data visualization and governance.
Solution: Establishing a comprehensive governance model that operates consistently for both segments of the bank. Attaining visibility of all data within existing systems.
Situation: The client aims to have a 360° view of their customers throughout the entire organization. To achieve this, they need to integrate and standardize all information silos into a single centralized point that the business can use for decision-making. The data is dirty, incomplete, duplicated, and lacks quality standards.
Solution: Using data quality tools, a single customer model was built based on information from their loyalty program.
Situation: The client has over a thousand sales representatives nationwide, but lacks visibility into how their sales are distributed in terms of the most relevant KPIs for the company, including: Number of prescriptions issued by physicians, geographic location of physicians, Prescription redemption, positioning vs. competitors, etc...
Solution: One of the initial projects that utilized cloud-native technologies for the creation of a Data Warehouse (DWH) and connection to external data. This involved a data cleansing process and the creation of dashboards specifically designed for tablets, allowing sales representatives to view KPI information.
Situation: There was no tool available to conduct analytics on internal processes for launching a new product. When the launch of a product is delayed due to any of the areas involved in the process, it can result in a significant loss of sales opportunities.
Solution: Leveraging data management technologies (Informatica) and reporting tools like (Qlik), we were able to develop the integration of processes from all involved areas and measure KPIs.
Situation: The company lacks established processes and relies heavily on Excel, especially for data management processes. They discovered they have databases with dirty and incomplete information, leading to inefficient decision-making. The most critical business domain is materials management, as there is no inventory control or association of costs and prices. This caused delays in product shipments and poor P&L control.
Solution: Development of data quality processes and support for material creation, updates, and deletions to establish greater structure and self-manageable business rules within the technology (GCP). This created synergy between business and systems areas.
Situation: The corporate environment is burdened by numerous legacy technologies, hindering business objectives due to high development costs, inefficient operation, and a lack of technological expertise.
Solution: Implementing a data governance framework by developing policies, tasks, and roles at both operational and strategic levels to map internal information knowledge, data cataloging, and lineage. This ensures that governance enables the identification of the best solution strategy for achieving business objectives and optimizing costs for projects, utilizing technology like ERWIN.
