Digging Deeper with Technology-Driven Data Mining

As digital infrastructure continues to expand, organizations find themselves increasingly saturated with extensive data resources that are not being harnessed efficiently. Despite these challenges, “the exploratory nature of data mining allows IT specialists to unveil intricate patterns and trends within huge datasets,” explains Dante Tellez, Director Data, Kubo Financiero. Nevertheless, data scientists must ensure the quality and reliability of the data being analyzed, as inconsistent information can lead to flawed conclusions and, consequently, poor business decisions.

Data mining has become an essential component for successful analytic initiatives within organizations, enhancing decision-making by providing executives with valuable insights. In fact, 69% of organizations are looking to leverage data to enhance their operational efficiency, according to a Denodo study. However, “without quality data, deriving valuable insights for capitalization becomes exceedingly challenging for businesses,” adds Rodrigo Murillo, CDO, Círculo de Crédito. 

“Conducting continuous assessments is fundamental in the realm of data management, as failing to understand the present condition of your operational systems can inevitably lead data projects to failure,” said Tellez. Additionally, applying data mining strategies without previous planning can also affect the quality of insights, as data strategies not adequately aligned with business objectives often fail to yield the expected results. 

"The vast computing capabilities offered by cloud platforms often leads to the temptation of employing brute force methods upon the gathered data, processing information without discernment. Initiating an investment process without meticulous planning can prove detrimental to business objectives,” stated Murillo. To avoid such issues, Juan Manuel Andrade, Chief Data, Analytics and Transformation Officer, Banco Azteca, recommends organizations to start by “distinguish[ing] between operational and analytical loads to avoid complexities associated with data handling."

Beyond the known challenges associated with data quality and strategy, data mining initiatives are also contingent on their effective communication and integration into the decision-making process. Téllez affirms that “an effective data strategy becomes significantly simpler when it is aligned with the company's overall business strategy.” The capacity to explain the results of these complex algorithms to stakeholders is paramount, albeit a significant challenge. Nevertheless, companies must make an effort to "cultivate effective dialogue among cross-functional teams” to bolster the efficacy of data mining projects, explains Juan Carlos Balboa, Technological Transformation Director, Vector Casa de Bolsa.

By extension, companies should be careful to avoid conflicts within their IT team, which normally arises from deviating interpretations and or understandings of their organizations' objectives, says Francisco Viana, CDO and Director of Data Powerhouse, Danone. To address this collaborative challenge, Andrade explains that “IT translators serve as an effective solution to ensure seamless communication between developers and business executives.”

Data has become a valuable business asset, according to experts, enhancing business decision-making and propelling operational efficiency forward. However, planning a unified business objective between IT specialists and executives is a significant challenge. Nevertheless, Murillo explains that “top executives are not concerned about data’s perfection; rather, they prioritize its quality, ensuring it generates a return on investment or a tangible economic benefit for the company.”