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IMPACT REPORT

SOLVING CLEANUP & INTEGRATION CHALLENGES FOR DATA DRIV EN SUCCESS

In an emerging digital economy, data is celebrated as the foundation of success. The transfer of knowledge from experienced individuals to algorithms has made data models more reliable. Experts are now contemplating how data cleanup, standardization, and integration efforts can enhance data-driven decision-making and actionable insights for business leaders.

“Knowledge that was once held by individuals with decades of experience has been transferred to algorithms in the form of data inputs, making business models more reliable”

Salvador Hernández CDO | Frisa Forjados

A Denodo study, as previously reported by MBn, uncovered a striking reality: only 54% of Mexican companies effectively harness their data resources, emphasizing the urgent need for streamlined data solutions. Surprisingly, while 90% of companies are in the midst of their data-driven transformation, just half are actively using data for informed decisionmaking, and 12% have not yet embarked on this journey. The study also reveals diverse objectives among surveyed organizations, including improving operational efficiency, gaining a competitive edge, reducing errors, and minimizing operational costs.

However, before companies can hope to realize these objectives, companies need to understand that the quality of data is an important qualifier. Whether for advanced analytics or predictive analytics, data quality is a fundamental component to an effective data strategy. According to Luis Pintado, CT o , Interprotección, “the insurance industry is undergoing a significant shift from relying on instinct to engaging directly with consumers through digital channels. This transition has prompted a need for more data-driven strategies.”

Despite ambitions to become more datadriven, use cases often remain isolated, according to Ana Coronel, Data Science Vice President, BanBaj ío. To expand them, it is essential to not only focus on data quality but also consider when and how data will be used to avoid wasting time cleaning data. She also considered comprehensive data governance, from inception to exploitation, to be vital to strategy formation. Without procuring these efforts from the beginning of strategy formation, companies’ risk undermining their ability to monetize their data.

“Data quality is paramount to building any data model, be it for advanced analytics or predictive insights. We all have a role in the lifecycle of data, and even a

minor error, like an incorrectly written tax identification number (r FC), can have farreaching consequences,” sai d Coronel.

Experts also shared how hyper-segmentation of data is vital for enhanced personalization, a capacity that has applications across industry segments. However, disorganized and poor data integration could lead to skewed investments in digital advertising, ineffective conversion rates, and challenges in targeting the desired audience. Herein lies productive capacity offered by disruptive technologies like machine learning, which could potentially expedite data clean-up and standardization, says

Pintado. n evertheless, no process should be left to automated models in its entirety. Human supervision will continue to be an integral part of data analysis, emphasized Hernández.

The application of emerging technologies in data analysis becomes relevant before a global shortage of qualified talent to clean, analyze and oversee data strategies. Moreover, continuous personnel turn-over can undermine the continuity and the viability of data initiatives. These issues pose a significant challenge to the medium and long-term data maturity of companies.

WELCOME TO THE ERA OF ASSISTANCE

The exponential growth of data has significantly broadened the scope of AI applications, culminating in the development of Large Language Models capable of adaptive and realistic user interactions. However, leveraging AIpowered technologies requires a responsible

approach that locates the security of the user and the organization at the center of its objectives, as argued by Aaron Castellanos, Solutions Architect, rackspace Technology. Failing to do so can lead organizations to incoherent decisions and biased analysis driven by AI models, potentially affecting the lives of individuals and the continuity of businesses.

“The immense amount of data available can significantly enhance the results, predictions, and insights provided by artificial intelligence, which can lead to better decision-making within organizations,” explained Castellanos at MACDS 2023. However, the quality of AI-driven analysis hinges on the accuracy and structure of the data it processes, as organizations leveraging unstructured data for AI-driven analysis may face flawed or incomplete insights.

To leverage vast amounts of data with AI-powered tools, Castellanos advises companies to “ensure good data governance, security, and quality before venturing to adopt this technology.” In fact, he explains that organizations looking to invest in AI “must have a very robust database before the integration of AI,” he added.

AI tools have the capacity to streamline organizational tasks, allowing IT specialists

to focus on more complex challenges essential for the operation of a company, according to Castellanos. They have also proven effective in driving innovation, optimizing resources, and enhancing customer experience across all industry sectors. In fact, “financial entities are currently leveraging generative AI to positively transform the industry, offering extreme personalization based on AI-driven insights into individual financial behaviors,” explained Castellanos.

However, artificially intelligent technology intersects with regulatory, ethical, and cybersecurity issues that companies must consider before integrating it into their operations, warned Castellanos. In fact, to ensure AI-empowered customer experience optimization, r ackspace and Microsoft designed an innovative practice focused on accelerating the responsible and sustainable adoption of generative AI solutions.

“The FAI r approach involves establishing robust data governance, incubating a minimum viable product aligned with organizational objectives, and industrializing generative AI within a defined life cycle,” explained Castellanos. This is meant to ensure a symbiotic, cyberresilient, and sustainable approach to AIpowe red tools.

As Microsoft and r ackspace contend, ensuring the responsible use of AI implies putting people and users at the center of their operations. “At the end of the day, AI can not only be exploited for technological purposes, but also for social solutions and services,” stated Castellanos. The FAI r framework exemplifies AI’s potential as a catalyst for social betterment, emphasizing the critical importance of deploying AI with ethical and responsible considerations at the forefront.

FUELING AI INSIGHTS IN MEXICO: DATA LAKES AND CLOUD WAREHOUSES

In response to the ever-growing volume of data, enterprises are proactively pursuing strategies for the efficient management and analysis of data to optimize operational efficiency and facilitate decision-making. Two compelling solutions on the horizon are Data Lakes and Cloud Warehouses, aided by the power of AI, which provide organizations with the capacity to securely store, govern, and analyze

extensive datasets. n evertheless, Mexican enterprises face the intricate challenge of integrating these systems into existing infrastructures in an efficient way that enhances their processes and, therefore, the ir value.

To fully capitalize on their datadriven transformations, companies are increasingly relying on emerging

technologies like data lakes and data warehouses (64%), predictive analytics (63%), and machine learning (51%), according to a Denodo survey. Currently, organizations are applying a combination of both to leverage the strengths of each, ensuring a secure, end-to-end system for storage, processing, and faster time to data-driven insights, according to Microsoft.

While data lakes and data warehouses are similar in that they both store and process data, they both have distinct characteristics. A data lake is versatile and flexible, capturing relational and nonrelational data from diverse sources like business applications, mobile apps, IoT devices, and social media, without the need to structure the data beforehand. This adaptability suits industries like telecommunication, healthcare, and manufacturing, where data formats are diverse and constantly evolving, according to Microsoft.

Meanwhile, data warehouses only operate in a relational way. Unlike data lakes, data warehouses store treated and transformed data with a defined purpose, often used to source analytic or operational reporting.

In this case, data warehouses can benefit industries in need of historical analysis and predictive analytics, such as finance, retail, and the automotive industry.

“A data lake serves as a comprehensive repository intended for later analysis and needs the expertise of a dedicated team to process and extract value for the organization. In contrast, data warehouses house data that is pre-structured and processed, readily available for immediate utilization,” says Diego Sánchez, Business Director, Mabe.

If a company decides to opt for a data warehouse, it will be able to undertake smaller, more agile projects with quicker execution. o n the other hand, data lakes are better suited for the execution of larger, more robust projects that promise greater value to the company, albeit with a slower pace, h e added.

The implementation of an AI solution needs a well-defined strategy to enhance data processing, particularly during the model’s training phase. As such, it is of paramount importance for the specialized team to engage in meticulous data curation to ensure that the model effectively aligns with the business’s strategic objectives, said Itzul Giron, Founder and CEo, Knowsy AI. This is meant to safeguard against the generation of irrelevant data, ensuring the model’s utility in the context of informed decisio n-making.

“Data curation, the process of refining data to ensure its accuracy and usability, forms the bedrock of data science. It serves as the essential precursor to extracting precise insights and building accurate models, thus empowering datadriven decision-making,” said r oberto Juarez, Sr. Systems Engineer, n utanix. To implement these solutions accurately, experts recommend hiring an in-house data scientist who truly understands the business model and the leader’s vision to inform data governance and streamline the integration of the chosen AI model.

Guidance holds paramount significance given the propensity of data stored in cloud warehouses and data lakes to descend into chaos and lose utility without a clear strategy. At present, most companies are grappling with gaps in expertise needed to realize their data-driven ambitions. Without them, companies lack the capabilities to build data architectures capable of generating the desired insights needed to optimize business decisions. These qualifiers are meant to precede the implementation of AI tools for data processing.

“It is crucial to assess whether AI is truly necessary, pinpoint the right moment for integration, and select the most appropriate AI category among the four types: generic AI, AI machine learning, AI deep learning, or GenAI. The significance of determining the type of AI used, lies in the particular emphasis of its pre-trained nature. While many companies currently prefer machine learning or deep learning, fewer have truly explored GenAI,” said José Carlos Huescas, WW HPC and AI Product Manage r, Lenovo.

DATA CENTER RESILIENCE, SAFEGUARDING BUSINESS CONTINUITY

Continuity of data center operations is key for companies that depend on these facilities, vital to the operation of digitally driven activities. However, companies have encountered significant challenges toward successful operations, including budget limitations, geographical constraints, and a shortage of tech expertise. To address these issues, industry experts suggest distributing resources across multiple cloud platforms is a key strategy for cost management and having a contingency plan to mitigate potential risks stemming from potential data center outages.

“Ensuring the seamless operations of a data center is of paramount significance. The utilization of additional power sources, connectivity, and cutting-edge technologies, coupled with the strategic placement of data centers across diverse geographical locations, is absolutely crucial. This comprehensive approach serves as a critical safeguard against substantial economic losses in times of crisis,” says rogelio Garcia, Chief Data Center officer, Grupo Salinas.

Preserving data security and ensuring uninterrupted operations are of utmost importance, which can be achieved by embracing a multifaceted strategy, says Darío García, Chief Technology o fficer LATAM, ManpowerGroup LATAM. This approach should encompass the integration of backup power supplies, fail-safe connectivity options, and cuttingedge technologies, he contends. To further enhance resilience, companies are also broadening their data center footprint across various geographic areas in the event of unforeseen disruptions like power failure, human error, and even climate disasters. This geographical expansion acts as a crucial safeguard, bolstering resilience against financial setbacks in times of crises.

“Strategically distributing services across multiple cloud platforms is a vital component of business resilience. Cloud architects and trained professionals are at the forefront of this strategic imperative,” says Óscar García, CIo, SmartPlus.

The strategic allocation of assets across different cloud service providers is meant to achieve equilibrium between economizing expenses and guaranteeing constant accessibility of data assets stored in data centers. Consequently, cloud architects must meticulously evaluate the unique requirements of the company and assign resources correspondingly. This way, costeffectiveness and uninterrupted business operations can be achieved simultaneously.

However, site selection process is far from straightforward; it demands a comprehensive approach to accommodate the necessary IT infrastructure. This includes critical considerations such as power supply and communication capabilities, which are integral to establishing a professional and resilient data center. The importance of site selection in the world of data center

management cannot be understated, as it lays the foundation for robust and reliable digital operations in an increasingly interconnected and data-driven age. “Choosing the right location and constructing efficient data centers is pivotal,” emphasized Benjamin Aguillón, Board Consultant, MEXDC.

The primary guideline is clear: the optimal geographical location for a data center should always be in close proximity to the user base and tailored to the specific needs of the business. This principle now drives a new wave of data center establishment, ensuring that these essential nerve centers of information are strategically positioned to address the dynamic demands of both users and corporate operations. By being situated near users, data centers not only bolster the speed and efficiency of data transmission but also significantly enhance the overall user experience. Simultaneously, aligning the location of data centers with the ever-evolving needs of the business ensures that companies can seamlessly adapt to shifting operational landscapes, thereby increasing operational resilience in the face of technological change.

“Cutting-edge technology, latency, energy, and sustainability are paramount factors that demand your full attention when envisioning your data center,” said Josue ramirez, regional Director LATAM, IDCA.

nevertheless, it is imperative for businesses to establish a well thought-out disaster recovery plan. Whether it is in response to a natural disaster, a cybersecurity breach, or any other unforeseen event, having a robust strategy to ensure the continuity of essential services is key to a company’s resilience and long-term success.

Industry leaders agree that finding skilled professionals has become an arduous task, with 53% of data center operators currently grappling with this daunting challenge. In their collective insight, they emphasize that addressing this shortage is key to sustaining and propelling the data center industr y forward.

ADVANCING IOT ANALYTICS THROUGH EDGE-CLOUD SYNERGY

The growing volume of data produced by IoT devices requires applications capable of monitoring and analyzing data flows, closer to the source to curb transmission latency. The integration of edge and cloud technologies creates a powerful infrastructure, capable of bridging the gap between the explosive growth of IoT data and the need for real-time, data-driven insights, according to Juan Carlos Montero, Partner, Head of Digital Technology Mexico, n TT Data.

“ r eal-time data processing at the edge guarantees immediate responses, while cloud-based analytics provide predictive insights, enabling companies to not just meet but surpass client expectations,” says Óscar Parra, Managing Director, Genesys.

n etwork congestion caused by high traffic or network overload can significantly increase data transmission latency between edge devices and cloud servers. This issue is a critical factor in various applications, especially in real-time systems such as online gaming and financial trading platforms, where even small delays can significantly impact user experience or financial transactions. In light of this, choosing servers that are geographically closer to the source can serve as a viable

solution to reduce data latency, which in turn can enhance real-time, datadriven decisions within organizations as discussed with industry leaders at Mexico AI, Cloud and Dat a Summit.

Edge computing represents an infrastructure paradigm that empowers devices to perform extensive data processing at the network’s periphery. This approach offers the dual advantages of conserving network resources, thereby reducing data traffic, latency, and associated costs. Conversely, cloud computing provides organizations with the capacity to amass extensive data repositories and computational capa bilities.

The fusion of edge and cloud computing assumes a pivotal role in amplifying the efficacy of real-time data processing for IoT analytics. This synergy minimizes the necessity for data transmission to the cloud, affording a framework that fosters customer-centric s trategies.

“Edge computing empowers data analytics by moving processing closer to the data source. It is not just about data; it is about real-time insights, facilitating smarter decisions,” says Sebastián Carmona,

Global Vice President r&D Transformation, Honeywell Technology Solutio ns Mexico.

Integrating IoT data into the cloud-edge computing framework poses several significant challenges. Data integrity, a linchpin in this endeavor, revolves around three critical facets: devices, platforms, and communication channels. In the context of IoT, the primary challenge emerges in the assurance of data quality for processing, particularly within the context of hybrid wireless networks.

Ensuring that each of these pre-processed data streams can seamlessly harmonize with a central algorithm constitutes a formidable challenge. The amalgamation of these elements is essential for achieving an integrated and comprehensive outcome, but

it presents a complex and rapidly evolving task. The initial creation of a foundational algorithm and its subsequent adaptation require substantial effort and expertise.

As intelligence within IoT systems continues to advance, the effective management of cloudbased communication becomes paramount. The evolution of artificial intelligence (AI), exemplified by the emergence of technologies such as chatbots, signifies a profound shift toward collaborative and creative roles, demanding a heightened degree of adaptability. Ignacio Madrid, Head of Data Management, Citibanamex, underscores the necessity of a holistic perspective on the IoT ecosystem, recognizing adaptability as a key imperative in a landscape where AI and automation are redefining traditional job functions.

“In the era of AI, embracing new technologies and adapting to evolving roles is essential. AI has taken over tasks we could not have imagined before, allowing us to shift our focus toward collaborative and creative endeavors. What was once inconceivable, like AI-generated text, is now a reality. It is crucial to adopt an attitude of adaptability to thrive,” says Madrid.

The standardization of global operations through the deployment of edge computing brings about heightened efficiency in data processing, particularly within the domain of private networks. The integration of AI, such as ChatGPT, underscores the critical importance of judicious utilization to optimize resource management and response times, especially when confronted with substantial data loads. Gabriel Fernández, Innovation and IoT Director, AT&T Mexico, aptly highlights the significance of Low Power Wide Area n etworks (LPWA n) in preserving battery life and enhancing resource optimization for IoT devices.

STRIVING TOWARDS MATURITY: MEXICO’S DIGITAL LANDSCAPE

Mexico finds itself at the crossroads of transformation, where the convergence of emerging technologies promises to reshape

the nation’s economic and technological trajectory. As Mexico strives toward digital maturity, it is essential to examine the trends

that are set to propel or challenge the country’s digital economy in the coming years.

“The digital landscape of Mexico is currently experiencing a significant transformation. As indicated by a recent survey conducted by MEXDEC, a noteworthy 96 million citizens have established internet connectivity. This vast online presence has unveiled a spectrum of diverse internet behaviors, spanning multiple generations,” says Itzul Girón, Founder and CEo, Knowsy.

According to the MEXDEC survey 42.7% of internet users in Mexico spend between seven and nine hours online, well above the global average of four hours. IThe survey shows a 45% increase in the development of financial and banking apps, allowing more unbanked people to access financial services.

In the e-commerce industry, 32% of Mexican users prefer shopping at physical stores, and 17.5% are not convinced by the offers presented online. This suggests that data is not yet being effectively utilized to personalize offerings, highlighting a lack of interoperability and data architecture, sugge sts Girón.

Interoperability is key in any data strategy within an organization so that every part of

the organization can gather, process, and use data to optimize operations. Because of this, the Mexican Internet Association and Knowsy propose the Identification Inter-Platform Standard (IDMX), an initiative designed to facilitate the accessibility of essential user data for corporations, enabling them to provide tailored products and services to their clients.

The next step in digital development in Mexico is the introduction of Mission Focused Cognitive Agents. These are AI-powered data processing models that are able to sift through data inputs to streamline decisionmaking in a specific area. Developers create a cognitive agent with the abilities of a highly specialized person and, with the input of cured data, they are able to suggest more efficient solutions. nonetheless, to operationalize this endeavor effectively, a framework must be established to define the model’s agency, its mission, dependencies, and regulatory oversight.

“The next step is multi-agent interoperable communication, where multiple cognitive agents are able to share information to further enhance the decision-making process for corporations,” says Girón. After that, in about five years, developers will be getting closer to General AI, which possesses the ability to understand, learn, and perform tasks in divers e domains.

If the integration of AI-enhanced tools like cognitive agents is successful, it could

supercharge Mexico’s GDP growth, potentially taking it from 2.4% to 6.4%.

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”

Dante Tellez Director Data | Kubo Financiero

“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.

n evertheless, 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 decisionmaking 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 r odrigo Murillo, CD o , Círculo de Crédito.

“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 o fficer, 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 crossfunctional 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, CD o 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. n evertheless, 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.”

INNOVATION AND TECHNOLOGY IN THE BUSINESS DNA

To maintain its market prominence amid the accelerated evolution of Mexico’s retail and e-commerce industries, Grupo Coppel is strategically incorporating emerging technologies at the core of its business DnA to continue driving innovation, explains ricardo Álvarez, n ational Manager, Grupo Coppel. This proactive approach has resulted in substantial investments in artificial intelligence and data analytics, with the purpose of gaining a deeper understanding of its vast customer base and informing business decisions.

Grupo Coppel maintains an extensive network of over 1,700 stores and more than 180 distribution centers, catering to approximately 27 million active customers across various segments, including commerce, retirement savings, and banking. In their pursuit of operational optimization, efficiency, and market exploration, the company has embarked on a data-driven journey, expanding beyond traditional transaction and portfolio data to unearth new opportunities. Their strategic initiatives include the implementation of machine learning models and chatbots at various customer touchpoints, aimed at enhancing customer interactions.

“From our fleet of delivery units to our distribution centers and strategic partnerships, we traverse a multifaceted landscape of operations, each of these transactions leaves behind a trail of data, which we are currently harnessing and exploring with the intricate web of our operations,” says Álvarez.

Grupo Coppel is committed to maintaining competitiveness in a rapidly changing market, where adapting to evolving customer demands and preferences is paramount. What has significantly contributed to the company’s success in its evolving approach, is its transition from a primarily applicationcentric model to one that prioritizes data science at the forefront, explained Álvarez. This strategic shift entails a dedicated focus on democratizing data tools and fostering a culture of creativity among its workforce. Data science teams have been established, featuring a range of expertise, from editorial to technically specialized roles.

“Coppel actively advises the construction of organizational support structures, including research centers and centers of excellence, to facilitate technological innovation,” says Álvarez. These initiatives enable the company to effectively

monitor retail trends and explore various technological domains. notably, their pursuits extend beyond data science as they venture into the realm of robotics. These initiatives enable the company to effectively monitor retail trends and explore various technological domains. n otably, their pursuits extend beyond data science as they venture into the realm of robotics.

Furthermore, the company maintains a dedicated research center, focused on exploring advanced research agendas, which often involve atypical questions, such as inventory balancing and dynamic pricing analysis. Coppel’s commitment extends to aligning data exploitation and automated

structures with a resolute focus on a customer-centric company strategy. However, interoperability issues, privacy concerns, high implementation costs, and compliance challenges continue to impede a seamless adoption of these technologies.

Despite these challenges, as Grupo Coppel envisions the future, it anticipates the implementation of automated inventory management and personalized product creation through customer terminals. Their ultimate objective is to systematically disseminate knowledge and cultivate creativity, firmly positioning data science and AI as long-term strategies, rather than fleeting trends.

CONNECTING MEXICO BUSINESS: 5G, CLOU D AND EDGE

As frictionless connectivity becomes a pivotal element for the operational efficiency of digital businesses, 5G integration arises to ensure expedited data transfer for realtime interactions between companies and consumers. However, embracing this cutting-edge technology is not without its challenges. Companies face the daunting task of ensuring compatibility with 5G, which demands substantial investments in scalable and flexible network infrastructure.

In Mexico, the anticipated impact of 5G technology on companies’ digital

transformations is poised to be profoundly transformative. It promises to usher in enhanced business efficiency through streamlined production and automation, as part of a growing portfolio of application use cases. This outlook was confirmed by Fabián Monge, Country Manager, Ericsson Mexico, who noted that “two-thirds of envisioned 5G use cases are still in the development phase, underscoring the immense potential at hand.”

While 5G networks are poised to spur innovation, curtail operational failures, and

elevate customer experiences, as explained by Monge, there are still numerous unexplored applications. o ne of the most exciting is in the predictive analysis of potential failure scenarios in organizations. Traditionally, before 5G, this was a timeconsuming process, taking an average of two to three weeks to identify. However, “the connectivity provided by 5G enables companies to leverage real-time data capturing algorithms, reducing prediction time from three weeks to 15 minutes,” says Monge. In short, the robust connectivity offered by 5G, provides companies with the necessary bandwidth to deploy machine learning algorithms needed to respond, adjust, and pivot their operations a s needed.

In light of this potential, Monge emphasizes that the integration of technologies like 5G and edge computing “must become a priority for companies carrying out complex operations that need to be efficiently virtualized.” These technologies play a pivotal role in streamlining cloud processes and enhancing predictive risk scenarios related to supply chain

operations, effectively improving timesensitive tasks and elevating the overall quality of products.

Looking ahead, as the adoption of IoT devices continues to surge, the indispensability of 5G technology becomes increasingly evident. At pace, Ericsson’s research predicts that over 24 billion interconnected devices will be operational by 2050. To ensure the sustained interoperability, frictionless connectivity will be paramount, directly affecting operational efficiency and business continuity. However, the adoption of this technology requires financial and infrastructure assessments to guarantee its effective integration, war ned Monge.

Altogether, there is no doubt that 5G technology is expected to transform the way in which we experience the world around us. In fact, just as “the internet democratized knowledge and information, extended reality powered by 5G will likely democratize experiences by introducing new alternative digital dimensions,” predic ts Monge.

The rapid proliferation and integration of emerging technologies has given rise to complex digital infrastructures where compatibility gaps impede the efficient flow of data. As businesses expand and diversify, the need for seamless integration between IT tools becomes of critical importance, as a fragmented IT system

can significantly hamper an organization’s overall agility, efficiency, and ada ptability.

o pen source offers a range of compelling advantages, but among them three chiefly stand out: standardization, flexibility and expansion, explained Facundo Chambó, CTo, Homie. o pen source fosters industry

EMBRACING OPEN SOURCE FOR SEAMLESS INTEGRATION

compatibility through the promotion of open source standards. n onetheless, open source is flexible enough to be adapted to the unique business and operational needs if needed. Moreover, since open source is continuously maintained and improved by entire communities of developers, digital expansion is facilitated. This advantage is particularly relevant in the event of shared-problems like software and security issues, which will be addressed and corrected by an entire community of stakeholders instead of intern al teams.

“The inherent value of open-source technology is reflected in its remarkable adaptability. When utilized by businesses, it becomes a versatile toolkit for crafting customized solutions”

Facundo Chambóz CTO | Homie

direction. Collaborating within the open source community brings forth a diverse array of expert perspectives, enabling startups to more effectively identify opportunities and challenges, thereby charting a course for success in a fiercely competitive market. This broader perspective empowers startups to better comprehend issues and develop more effective solutions. This includes addressing challenges like personalization, the insights from which can assist in adapting solutions more adeptly to customer needs and desires.

For startups, open source technology emerges as a pivotal asset. It offers burgeoning enterprises the means to enter the tech arena without bearing the substantial financial burdens often associated with proprietary solutions, as noted by rafael Verduzco, CTo , Zendala. Through the utilization of open access to source code and the wealth of available resources, startups can craft top-tier products and services while circumventing onerous software licensing expenses.

In addition to the financial and time-saving advantages, open source technology significantly enhances the decision-making process for organizations. By granting access to comprehensive data and indepth analysis, companies can make more informed choices regarding their product development, strategic planning, and overall

While open source technology offers remarkable flexibility and customization, organizations must exercise caution to avoid the pitfall of excessive customization. overcustomization can lead to compatibility and maintenance issues as open source projects evolve and receive updates. It is crucial to strike a balance between tailoring the software to specific needs and ensuring the ability to benefit from community-driven updates. This equilibrium is key to fully leveraging the advantages of open source while safeguarding its long-term functionality. o n the flipped side, there is the concern of dependency on the development community. In the event that a community becomes inactive or an open source project is abandoned, organizations relying on that technology may find themselves in a precarious position, highlighting the importance of careful consideration and contingency planning in open source adoption.

Moreover, the accessibility of open source’s source code exposes it to potential security risks, as malicious actors can identify and exploit vulnerabilities, says Juan Arturo Dueñas, CIo, GM Mexico. To mitigate these risks, it is imperative for companies and developers to follow security best practices, which include prompt implementation of updates and security patches.

THE AI HORIZON: MAPPING YOUR INTEGRATION STRATEGY

The strategic adoption of AI tools has emerged as a crucial driver of competitiveness and efficiency, facilitating the digital transformation of enterprises.

n evertheless, the hasty integration of AI technology without the prior establishment of well-defined business objectives and a comprehensive assessment of

compatibility, can pose significant risks to operational integrity and render substantial investments ineffective. A case in point is the potential incompatibility of inadequate IT infrastructure with AI tools, resulting in formidable challenges and elevated costs needed to correct the integration of these technologies into systems.

“Mexico ranks fifth among the 12 countries that make up the Latin American Artificial Intelligence Index, with a score of 48.55 against the regional average of 42.61. The country shows a good performance in research, although the need to strengthen its infrastructure is evident,” says Carlos Marcel, Managing Director, Kyndryl Mexico. “Even though [Mexico’s] ranking is high up in the region, there are still many opportunities to grow and to strengthen the technological ecosystem to allow AI to really make a substantial difference in its competitiveness,” he explaine d to MB n

Businesses looking to leverage AIpowered tools are constantly looking to enhance their operational efficiency. By harnessing this technology, organizations can effectively streamline internal processes, automate repetitive tasks, and optimize resource utilization. Furthermore, by customizing interactions, anticipating customer needs, and enhancing service quality through AI-driven insights, businesses can significantly improve customer’s experience and gain an advantage over co mpetitors.

“To ensure the successful integration of AI, companies must know with absolute certainty the state in which the company’s digital architecture is in before even beginning to plan to integrate enhancing tools,” says Diego Halffter, Chief Data and Analytics o fficer, Justo. A company cannot start evaluating the integration of AI-powered tools and models if it does not have clear business objectives. “The key to a successful AI solution resides in its ability to address well-defined needs. Artificial intelligence by itself is destined for failure,” he added. To achieve this, he recommends breaking down each step of the integration phase as much as possible so that AI implementation and its scale is as seamless and organic as possible.

“ n evertheless, conducting a comprehensive cost-benefit analysis and measuring the return on investment is indispensable to ensure that AI implementation aligns with an organization’s financial objectives and&or limitations,” says Marco Antonio Hernández, CD o, P ro SA.

“The comprehensive documentation of the organization’s requirements, the reason for implementation—such as cost reduction and time efficiency—and a welldefined timeline for expected outcomes is imperative,” said Ignacio Madrid, Head of Data Management, Citibanamex. This approach ensures alignment with user requirements and expectations. The process, he elaborated, should be

underpinned by robust data governance frameworks, wherein the Chief Data officer ensures data quality and curation, while the Chief Analytics o fficer identifies use cases that deliver tangible value through data-driven insights. This will ensure that investments are inextricably linked to the specific use cases, a strategy that promises a more accurate allocation of resources and a more optimal path to ensuring roI.

“Maintaining a simple, yet clear approach to the case of use and business objectives for AI-powered tools is key for the most effective implementation of this technology,” said Pablo Guzzi, Chief Data and Analytics officer, Ualá. Simplifying complex data into clear and unambiguous metrics allows for development of lucid and straightforward solutions that are valuable for the company. Furthermore, adopting a forward-thinking perspective, with an eye on large-scale deployment, prioritizes scalability from the inception of their AI journey. This approach, he suggested, not only fosters innovation but also paves the way for sustained competitiveness.

Another key factor to ensure successful AI implementation is the interconnectedness of the different areas within an organization. Alfredo Pequeño, Director of Payment Methods and Data, Banregio, underscored the essential nature of collaboration and cohesion when integrating AI solutions.“It is useless if only the CDo or CAo are aware of what the analytic model is doing, because data and decision-making touch every single branch of an organization. n o man is an island, so every person involved must have a clear idea of what the goal and expected resu lts are.”

In the future, when companies have successfully integrated these solutions into their processes, there will be a redefinition of organizational structures, according to Justo’s CD o . As businesses evolve with these types of technology, those who have the skills to carry this out will be the leaders. “Today’s CD o s will be the CE o s of the future, as today’s skills will be the skills needed for tomorrow’s companies,” he concluded.

Mexico AI, Cloud & Data Summit 2023 - Impact Report