EXPERT SPEAK
DATA ANALYTICS : TRENDS AND CHALLENGES FOR THE COMING MONTHS
Andrés Alexander , Regional Services and Cloud Cluster VP at BGH Tech Partner , explains how data analytics will impact the IT market and why companies should value the information they generate .
The process of examining data to obtain valuable information for decision-making has become a fundamental input for organizations . Therefore , it is advisable to closely follow the technologies and techniques that are coming to maximize their potential for the business .
The information that a company possesses about its customers , products or suppliers represents an asset without which it is not possible to carry out the main activities or make business decisions .
Following this reality , the data analytics market is growing at a compound annual rate of almost 30 %, according to data from the consulting firm Statista . With the increase in hybrid work and network dispersion , companies are requiring more easily accessible data to continue empowering their personnel , so that each collaborator can take advantage of the power of data to make better decisions . These processes will continue in 2023 , as they allow companies to :
• Better understand their customers
• Optimize the development of products and services
• Add efficiency in internal operations
• Add new sources of income
Andrés Alexander , VP of the Regional Cloud and Services Cluster at BGH Tech Partner
In the coming months , organizations will continue to move their analytics and data solutions to the cloud in multi-cloud and hybrid cloud contexts . In addition , companies will tend to incorporate systems that allow for real-time data recovery and processing to track their entire business , manage unexpected changes and address issues quickly .
In parallel , the use of Data-as-a-Service ( DaaS ) will gain popularity , which allows for data collection , management and storage by third parties through cloud services . This model will be in greater demand as it avoids companies having to build their own storage and collection systems while allowing them to work with data without needing to set up and maintain data science operations that are often costly and specialized .
On the other hand , there are already requirements for data structures with unique and consistent management frameworks that allow for analysis throughout the enterprise and data services that automate processes such as data collection , exploration , discovery , preparation and integration . This decentralized and self-service architecture helps teams use resources and tools on demand . This architecture design is based
Companies are requiring more easily accessible data to continue empowering their personnel .
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