Data and Artificial Intelligence Main Digitalization in 2023
Nearly 40% of companies will increase their investment in digitization in 2023, according to data from MCPRO. Sectors such as energy, health, industry, retail, insurance or fintech will bet on advanced Big Data and Artificial Intelligence solutions to take a new step towards maximum customization of their products and services.
Advance in the security and privacy of their data and increase the speed of transactional operations on a large scale, fraud detection, the automation of its processes or predictive maintenance and the ubiquitous chatbots, as highlighted by the medium specialized in digital transformation.
An assessment that coincides with the vision of Keepler Data Tech, which has spent several years highlighting the relevance of 3 key drivers of the digitization of organizations; process automation, time-to-market reduction and product differentiation.
The Data Product approach helps companies to focus on this type of solution, highly oriented to solving a specific business challenge based on their data. This causes a strong impact on the business, quickly, demonstrably and reliably, for example, in areas such as improving the customer experience, employee productivity, creating new products and enhancing competitiveness.
To continue advancing in this line, companies must face five short-term challenges:
Greater data-centric perspective in Artificial Intelligence projects, where the priority is not to accumulate data but to work on improving its quality and consistency. Correct data labelling, data-augmentation strategies, versioning or feature stores speed up this process.
AI projects must take privacy and security aspects into account from their definition. In addition to securing the information, it is also necessary to determine more robust and reliable models, and apply techniques, such as adversarial training, to prevent responses to possible corrupted data or infrequent scenarios.
Automation of cognitive processes, incorporating services available in different Cloud platforms (voice, image, text or decision) or making use of pre-trained “multimodal” models in state-of-the-art (Dall-E or CLIP as examples) or textual (GPT 4) to solve different types of tasks of a creative nature, such as performing semantic synthesis, creating new textual and visual content, or answering questions interactively.
Increase Big Data capabilities of processes, which are increasingly demanding. In this sense, quantum computing gains relevance and can solve complex problems and carry out large-scale simulations or challenges in optimization processes, among others.
Implement good practices in the form of regulation and commitment so that the technology is applied in the most transparent, ethical and fair way possible, translating into the most representative datasets possible, checking biases, performing sensitivity analysis or prioritizing interpretability through models. Simpler goal oriented.
To face technological challenges, alliances with partners or specialized specialist collaborators, who have specialized knowledge and experience, is an increasingly widespread practice.
But, in addition, for this development to be sustainable in organizations, an investment in training is required by companies and a personal effort by individuals, which facilitates the acquisition of new skills, as well as an open attitude to the development and continuous training.