The data analytics and artificial intelligence (AI) market has seen significant changes in the past few years. Companies like Alteryx and Tableau have undergone mergers and acquisitions, while the rise of open-source language has impacted traditional analytics technologies such as SAS. Start-ups in the analytics field have faced financial challenges and, in some cases, failed to establish sustainable business models. Additionally, the rapid adoption of generative AI has intensified competition, leading to unprecedented uncertainty in the world of data analytics.
In such an evolving landscape, choosing the right analytics partners is crucial. To ensure longevity and adaptability, partnerships should be built with companies that have proven track records, scalable cost structures, growth plans, and robust support systems.
When selecting data and AI technology, it’s critical to look for comprehensive end-to-end solutions. A partner should offer a wide range of tools, from data preparation and extract-transform-load (ETL) operations, to autoML forecasting and feature engineering. Other key features include generative AI fine-tuning, model development, workload orchestration, data visualization and multi-language analytics capabilities. When these tools are offered by the same technology partner, they integrate seamlessly for an efficient and intuitive workflow.
Strong software partners also offer all the resources in a workflow that empowers both data specialists and non-data specialists. Repositories of no-code and low-code tools allow non-data team members to execute minor but important tasks, freeing up the data team for more complex projects requiring comprehensive data science insights.
In addition to offering a robust technology suite, a good data and AI partner should demonstrate business stability. They should have proven results and a track record of resilience. A data vendor needs to be dependable as disruptions or miscommunications caused by unstable partners can negatively impact both short and long-term success.
Beyond providing technological solutions, partners should also have deep domain knowledge and deliver exceptional customer service. Real partners stand by your side to navigate challenges rather than merely offering vendor services. Also, given market uncertainties, it’s important to prioritize partners with consumer-friendly business models and licensing systems that offer increasing value as their tools and services are utilized more.
Understanding how to navigate uncertainties in the current data and AI market is essential. Through discussions and conferences, like Altair’s free Future.Industry 2024 virtual event, organizations can learn about the future of frictionless data and AI. Christian Buckner, SVP of data analytics at Altair, asserts that elevating the role of data in decision-making and automation helps innovative organizations build a better future. The post Navigating Today’s Data and AI Market Uncertainty appeared first on AI Quantum Intelligence.
